Sample records for machine scheduling problems

  1. An algorithm for a single machine scheduling problem with sequence dependent setup times and scheduling windows

    NASA Technical Reports Server (NTRS)

    Moore, J. E.

    1975-01-01

    An enumeration algorithm is presented for solving a scheduling problem similar to the single machine job shop problem with sequence dependent setup times. The scheduling problem differs from the job shop problem in two ways. First, its objective is to select an optimum subset of the available tasks to be performed during a fixed period of time. Secondly, each task scheduled is constrained to occur within its particular scheduling window. The algorithm is currently being used to develop typical observational timelines for a telescope that will be operated in earth orbit. Computational times associated with timeline development are presented.

  2. A parallel-machine scheduling problem with two competing agents

    NASA Astrophysics Data System (ADS)

    Lee, Wen-Chiung; Chung, Yu-Hsiang; Wang, Jen-Ya

    2017-06-01

    Scheduling with two competing agents has become popular in recent years. Most of the research has focused on single-machine problems. This article considers a parallel-machine problem, the objective of which is to minimize the total completion time of jobs from the first agent given that the maximum tardiness of jobs from the second agent cannot exceed an upper bound. The NP-hardness of this problem is also examined. A genetic algorithm equipped with local search is proposed to search for the near-optimal solution. Computational experiments are conducted to evaluate the proposed genetic algorithm.

  3. Manipulating Tabu List to Handle Machine Breakdowns in Job Shop Scheduling Problems

    NASA Astrophysics Data System (ADS)

    Nababan, Erna Budhiarti; SalimSitompul, Opim

    2011-06-01

    Machine breakdowns in a production schedule may occur on a random basis that make the well-known hard combinatorial problem of Job Shop Scheduling Problems (JSSP) becomes more complex. One of popular techniques used to solve the combinatorial problems is Tabu Search. In this technique, moves that will be not allowed to be revisited are retained in a tabu list in order to avoid in gaining solutions that have been obtained previously. In this paper, we propose an algorithm to employ a second tabu list to keep broken machines, in addition to the tabu list that keeps the moves. The period of how long the broken machines will be kept on the list is categorized using fuzzy membership function. Our technique are tested to the benchmark data of JSSP available on the OR library. From the experiment, we found that our algorithm is promising to help a decision maker to face the event of machine breakdowns.

  4. Multi-objective problem of the modified distributed parallel machine and assembly scheduling problem (MDPMASP) with eligibility constraints

    NASA Astrophysics Data System (ADS)

    Amallynda, I.; Santosa, B.

    2017-11-01

    This paper proposes a new generalization of the distributed parallel machine and assembly scheduling problem (DPMASP) with eligibility constraints referred to as the modified distributed parallel machine and assembly scheduling problem (MDPMASP) with eligibility constraints. Within this generalization, we assume that there are a set non-identical factories or production lines, each one with a set unrelated parallel machine with different speeds in processing them disposed to a single assembly machine in series. A set of different products that are manufactured through an assembly program of a set of components (jobs) according to the requested demand. Each product requires several kinds of jobs with different sizes. Beside that we also consider to the multi-objective problem (MOP) of minimizing mean flow time and the number of tardy products simultaneously. This is known to be NP-Hard problem, is important to practice, as the former criterions to reflect the customer's demand and manufacturer's perspective. This is a realistic and complex problem with wide range of possible solutions, we propose four simple heuristics and two metaheuristics to solve it. Various parameters of the proposed metaheuristic algorithms are discussed and calibrated by means of Taguchi technique. All proposed algorithms are tested by Matlab software. Our computational experiments indicate that the proposed problem and fourth proposed algorithms are able to be implemented and can be used to solve moderately-sized instances, and giving efficient solutions, which are close to optimum in most cases.

  5. Some single-machine scheduling problems with learning effects and two competing agents.

    PubMed

    Li, Hongjie; Li, Zeyuan; Yin, Yunqiang

    2014-01-01

    This study considers a scheduling environment in which there are two agents and a set of jobs, each of which belongs to one of the two agents and its actual processing time is defined as a decreasing linear function of its starting time. Each of the two agents competes to process its respective jobs on a single machine and has its own scheduling objective to optimize. The objective is to assign the jobs so that the resulting schedule performs well with respect to the objectives of both agents. The objective functions addressed in this study include the maximum cost, the total weighted completion time, and the discounted total weighted completion time. We investigate three problems arising from different combinations of the objectives of the two agents. The computational complexity of the problems is discussed and solution algorithms where possible are presented.

  6. Two-machine flow shop scheduling integrated with preventive maintenance planning

    NASA Astrophysics Data System (ADS)

    Wang, Shijin; Liu, Ming

    2016-02-01

    This paper investigates an integrated optimisation problem of production scheduling and preventive maintenance (PM) in a two-machine flow shop with time to failure of each machine subject to a Weibull probability distribution. The objective is to find the optimal job sequence and the optimal PM decisions before each job such that the expected makespan is minimised. To investigate the value of integrated scheduling solution, computational experiments on small-scale problems with different configurations are conducted with total enumeration method, and the results are compared with those of scheduling without maintenance but with machine degradation, and individual job scheduling combined with independent PM planning. Then, for large-scale problems, four genetic algorithm (GA) based heuristics are proposed. The numerical results with several large problem sizes and different configurations indicate the potential benefits of integrated scheduling solution and the results also show that proposed GA-based heuristics are efficient for the integrated problem.

  7. Scheduling of hybrid types of machines with two-machine flowshop as the first type and a single machine as the second type

    NASA Astrophysics Data System (ADS)

    Hsiao, Ming-Chih; Su, Ling-Huey

    2018-02-01

    This research addresses the problem of scheduling hybrid machine types, in which one type is a two-machine flowshop and another type is a single machine. A job is either processed on the two-machine flowshop or on the single machine. The objective is to determine a production schedule for all jobs so as to minimize the makespan. The problem is NP-hard since the two parallel machines problem was proved to be NP-hard. Simulated annealing algorithms are developed to solve the problem optimally. A mixed integer programming (MIP) is developed and used to evaluate the performance for two SAs. Computational experiments demonstrate the efficiency of the simulated annealing algorithms, the quality of the simulated annealing algorithms will also be reported.

  8. Hybrid Metaheuristics for Solving a Fuzzy Single Batch-Processing Machine Scheduling Problem

    PubMed Central

    Molla-Alizadeh-Zavardehi, S.; Tavakkoli-Moghaddam, R.; Lotfi, F. Hosseinzadeh

    2014-01-01

    This paper deals with a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM) scheduling in the presence of fuzzy due date. In this paper, first a fuzzy mixed integer linear programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design two hybrid metaheuristics called GA-VNS and VNS-SA applying the advantages of genetic algorithm (GA), variable neighborhood search (VNS), and simulated annealing (SA) frameworks. Besides, we propose three fuzzy earliest due date heuristics to solve the given problem. Through computational experiments with several random test problems, a robust calibration is applied on the parameters. Finally, computational results on different-scale test problems are presented to compare the proposed algorithms. PMID:24883359

  9. Job shop scheduling problem with late work criterion

    NASA Astrophysics Data System (ADS)

    Piroozfard, Hamed; Wong, Kuan Yew

    2015-05-01

    Scheduling is considered as a key task in many industries, such as project based scheduling, crew scheduling, flight scheduling, machine scheduling, etc. In the machine scheduling area, the job shop scheduling problems are considered to be important and highly complex, in which they are characterized as NP-hard. The job shop scheduling problems with late work criterion and non-preemptive jobs are addressed in this paper. Late work criterion is a fairly new objective function. It is a qualitative measure and concerns with late parts of the jobs, unlike classical objective functions that are quantitative measures. In this work, simulated annealing was presented to solve the scheduling problem. In addition, operation based representation was used to encode the solution, and a neighbourhood search structure was employed to search for the new solutions. The case studies are Lawrence instances that were taken from the Operations Research Library. Computational results of this probabilistic meta-heuristic algorithm were compared with a conventional genetic algorithm, and a conclusion was made based on the algorithm and problem.

  10. Scheduling of flow shop problems on 3 machines in fuzzy environment with double transport facility

    NASA Astrophysics Data System (ADS)

    Sathish, Shakeela; Ganesan, K.

    2016-06-01

    Flow shop scheduling is a decision making problem in production and manufacturing field which has a significant impact on the performance of an organization. When the machines on which jobs are to be processed are placed at different places, the transportation time plays a significant role in production. Further two different transport agents where 1st takes the job from 1st machine to 2nd machine and then returns back to the first machine and the 2nd takes the job from 2nd machine to 3rd machine and then returns back to the 2nd machine are also considered. We propose a method to minimize the total make span; without converting the fuzzy processing time to classical numbers by using a new type of fuzzy arithmetic and a fuzzy ranking method. A numerical example is provided to explain the proposed method.

  11. Scheduling Jobs with Variable Job Processing Times on Unrelated Parallel Machines

    PubMed Central

    Zhang, Guang-Qian; Wang, Jian-Jun; Liu, Ya-Jing

    2014-01-01

    m unrelated parallel machines scheduling problems with variable job processing times are considered, where the processing time of a job is a function of its position in a sequence, its starting time, and its resource allocation. The objective is to determine the optimal resource allocation and the optimal schedule to minimize a total cost function that dependents on the total completion (waiting) time, the total machine load, the total absolute differences in completion (waiting) times on all machines, and total resource cost. If the number of machines is a given constant number, we propose a polynomial time algorithm to solve the problem. PMID:24982933

  12. Heuristic methods for the single machine scheduling problem with different ready times and a common due date

    NASA Astrophysics Data System (ADS)

    Birgin, Ernesto G.; Ronconi, Débora P.

    2012-10-01

    The single machine scheduling problem with a common due date and non-identical ready times for the jobs is examined in this work. Performance is measured by the minimization of the weighted sum of earliness and tardiness penalties of the jobs. Since this problem is NP-hard, the application of constructive heuristics that exploit specific characteristics of the problem to improve their performance is investigated. The proposed approaches are examined through a computational comparative study on a set of 280 benchmark test problems with up to 1000 jobs.

  13. Permutation flow-shop scheduling problem to optimize a quadratic objective function

    NASA Astrophysics Data System (ADS)

    Ren, Tao; Zhao, Peng; Zhang, Da; Liu, Bingqian; Yuan, Huawei; Bai, Danyu

    2017-09-01

    A flow-shop scheduling model enables appropriate sequencing for each job and for processing on a set of machines in compliance with identical processing orders. The objective is to achieve a feasible schedule for optimizing a given criterion. Permutation is a special setting of the model in which the processing order of the jobs on the machines is identical for each subsequent step of processing. This article addresses the permutation flow-shop scheduling problem to minimize the criterion of total weighted quadratic completion time. With a probability hypothesis, the asymptotic optimality of the weighted shortest processing time schedule under a consistency condition (WSPT-CC) is proven for sufficiently large-scale problems. However, the worst case performance ratio of the WSPT-CC schedule is the square of the number of machines in certain situations. A discrete differential evolution algorithm, where a new crossover method with multiple-point insertion is used to improve the final outcome, is presented to obtain high-quality solutions for moderate-scale problems. A sequence-independent lower bound is designed for pruning in a branch-and-bound algorithm for small-scale problems. A set of random experiments demonstrates the performance of the lower bound and the effectiveness of the proposed algorithms.

  14. Scheduling algorithm for flow shop with two batch-processing machines and arbitrary job sizes

    NASA Astrophysics Data System (ADS)

    Cheng, Bayi; Yang, Shanlin; Hu, Xiaoxuan; Li, Kai

    2014-03-01

    This article considers the problem of scheduling two batch-processing machines in flow shop where the jobs have arbitrary sizes and the machines have limited capacity. The jobs are processed in batches and the total size of jobs in each batch cannot exceed the machine capacity. Once a batch is being processed, no interruption is allowed until all the jobs in it are completed. The problem of minimising makespan is NP-hard in the strong sense. First, we present a mathematical model of the problem using integer programme. We show the scale of feasible solutions of the problem and provide optimality properties. Then, we propose a polynomial time algorithm with running time in O(nlogn). The jobs are first assigned in feasible batches and then scheduled on machines. For the general case, we prove that the proposed algorithm has a performance guarantee of 4. For the special case where the processing times of each job on the two machines satisfy p 1 j = ap 2 j , the performance guarantee is ? for a > 0.

  15. A hybrid flowshop scheduling model considering dedicated machines and lot-splitting for the solar cell industry

    NASA Astrophysics Data System (ADS)

    Wang, Li-Chih; Chen, Yin-Yann; Chen, Tzu-Li; Cheng, Chen-Yang; Chang, Chin-Wei

    2014-10-01

    This paper studies a solar cell industry scheduling problem, which is similar to traditional hybrid flowshop scheduling (HFS). In a typical HFS problem, the allocation of machine resources for each order should be scheduled in advance. However, the challenge in solar cell manufacturing is the number of machines that can be adjusted dynamically to complete the job. An optimal production scheduling model is developed to explore these issues, considering the practical characteristics, such as hybrid flowshop, parallel machine system, dedicated machines, sequence independent job setup times and sequence dependent job setup times. The objective of this model is to minimise the makespan and to decide the processing sequence of the orders/lots in each stage, lot-splitting decisions for the orders and the number of machines used to satisfy the demands in each stage. From the experimental results, lot-splitting has significant effect on shortening the makespan, and the improvement effect is influenced by the processing time and the setup time of orders. Therefore, the threshold point to improve the makespan can be identified. In addition, the model also indicates that more lot-splitting approaches, that is, the flexibility of allocating orders/lots to machines is larger, will result in a better scheduling performance.

  16. Single machine scheduling with slack due dates assignment

    NASA Astrophysics Data System (ADS)

    Liu, Weiguo; Hu, Xiangpei; Wang, Xuyin

    2017-04-01

    This paper considers a single machine scheduling problem in which each job is assigned an individual due date based on a common flow allowance (i.e. all jobs have slack due date). The goal is to find a sequence for jobs, together with a due date assignment, that minimizes a non-regular criterion comprising the total weighted absolute lateness value and common flow allowance cost, where the weight is a position-dependent weight. In order to solve this problem, an ? time algorithm is proposed. Some extensions of the problem are also shown.

  17. Sensibility study in a flexible job shop scheduling problem

    NASA Astrophysics Data System (ADS)

    Curralo, Ana; Pereira, Ana I.; Barbosa, José; Leitão, Paulo

    2013-10-01

    This paper proposes the impact assessment of the jobs order in the optimal time of operations in a Flexible Job Shop Scheduling Problem. In this work a real assembly cell was studied: the AIP-PRIMECA cell at the Université de Valenciennes et du Hainaut-Cambrésis, in France, which is considered as a Flexible Job Shop problem. The problem consists in finding the machines operations schedule, taking into account the precedence constraints. The main objective is to minimize the batch makespan, i.e. the finish time of the last operation completed in the schedule. Shortly, the present study consists in evaluating if the jobs order affects the optimal time of the operations schedule. The genetic algorithm was used to solve the optimization problem. As a conclusion, it's assessed that the jobs order influence the optimal time.

  18. Meta-RaPS Algorithm for the Aerial Refueling Scheduling Problem

    NASA Technical Reports Server (NTRS)

    Kaplan, Sezgin; Arin, Arif; Rabadi, Ghaith

    2011-01-01

    The Aerial Refueling Scheduling Problem (ARSP) can be defined as determining the refueling completion times for each fighter aircraft (job) on multiple tankers (machines). ARSP assumes that jobs have different release times and due dates, The total weighted tardiness is used to evaluate schedule's quality. Therefore, ARSP can be modeled as a parallel machine scheduling with release limes and due dates to minimize the total weighted tardiness. Since ARSP is NP-hard, it will be more appropriate to develop a pproimate or heuristic algorithm to obtain solutions in reasonable computation limes. In this paper, Meta-Raps-ATC algorithm is implemented to create high quality solutions. Meta-RaPS (Meta-heuristic for Randomized Priority Search) is a recent and promising meta heuristic that is applied by introducing randomness to a construction heuristic. The Apparent Tardiness Rule (ATC), which is a good rule for scheduling problems with tardiness objective, is used to construct initial solutions which are improved by an exchanging operation. Results are presented for generated instances.

  19. A Solution Method of Scheduling Problem with Worker Allocation by a Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Osawa, Akira; Ida, Kenichi

    In a scheduling problem with worker allocation (SPWA) proposed by Iima et al, the worker's skill level to each machine is all the same. However, each worker has a different skill level for each machine in the real world. For that reason, we propose a new model of SPWA in which a worker has the different skill level to each machine. To solve the problem, we propose a new GA for SPWA consisting of the following new three procedures, shortening of idle time, modifying infeasible solution to feasible solution, and a new selection method for GA. The effectiveness of the proposed algorithm is clarified by numerical experiments using benchmark problems for job-shop scheduling.

  20. Single-machine group scheduling problems with deteriorating and learning effect

    NASA Astrophysics Data System (ADS)

    Xingong, Zhang; Yong, Wang; Shikun, Bai

    2016-07-01

    The concepts of deteriorating jobs and learning effects have been individually studied in many scheduling problems. However, most studies considering the deteriorating and learning effects ignore the fact that production efficiency can be increased by grouping various parts and products with similar designs and/or production processes. This phenomenon is known as 'group technology' in the literature. In this paper, a new group scheduling model with deteriorating and learning effects is proposed, where learning effect depends not only on job position, but also on the position of the corresponding job group; deteriorating effect depends on its starting time of the job. This paper shows that the makespan and the total completion time problems remain polynomial optimal solvable under the proposed model. In addition, a polynomial optimal solution is also presented to minimise the maximum lateness problem under certain agreeable restriction.

  1. A meta-heuristic method for solving scheduling problem: crow search algorithm

    NASA Astrophysics Data System (ADS)

    Adhi, Antono; Santosa, Budi; Siswanto, Nurhadi

    2018-04-01

    Scheduling is one of the most important processes in an industry both in manufacturingand services. The scheduling process is the process of selecting resources to perform an operation on tasks. Resources can be machines, peoples, tasks, jobs or operations.. The selection of optimum sequence of jobs from a permutation is an essential issue in every research in scheduling problem. Optimum sequence becomes optimum solution to resolve scheduling problem. Scheduling problem becomes NP-hard problem since the number of job in the sequence is more than normal number can be processed by exact algorithm. In order to obtain optimum results, it needs a method with capability to solve complex scheduling problems in an acceptable time. Meta-heuristic is a method usually used to solve scheduling problem. The recently published method called Crow Search Algorithm (CSA) is adopted in this research to solve scheduling problem. CSA is an evolutionary meta-heuristic method which is based on the behavior in flocks of crow. The calculation result of CSA for solving scheduling problem is compared with other algorithms. From the comparison, it is found that CSA has better performance in term of optimum solution and time calculation than other algorithms.

  2. Simultaneous Scheduling of Jobs, AGVs and Tools Considering Tool Transfer Times in Multi Machine FMS By SOS Algorithm

    NASA Astrophysics Data System (ADS)

    Sivarami Reddy, N.; Ramamurthy, D. V., Dr.; Prahlada Rao, K., Dr.

    2017-08-01

    This article addresses simultaneous scheduling of machines, AGVs and tools where machines are allowed to share the tools considering transfer times of jobs and tools between machines, to generate best optimal sequences that minimize makespan in a multi-machine Flexible Manufacturing System (FMS). Performance of FMS is expected to improve by effective utilization of its resources, by proper integration and synchronization of their scheduling. Symbiotic Organisms Search (SOS) algorithm is a potent tool which is a better alternative for solving optimization problems like scheduling and proven itself. The proposed SOS algorithm is tested on 22 job sets with makespan as objective for scheduling of machines and tools where machines are allowed to share tools without considering transfer times of jobs and tools and the results are compared with the results of existing methods. The results show that the SOS has outperformed. The same SOS algorithm is used for simultaneous scheduling of machines, AGVs and tools where machines are allowed to share tools considering transfer times of jobs and tools to determine the best optimal sequences that minimize makespan.

  3. Hybrid Pareto artificial bee colony algorithm for multi-objective single machine group scheduling problem with sequence-dependent setup times and learning effects.

    PubMed

    Yue, Lei; Guan, Zailin; Saif, Ullah; Zhang, Fei; Wang, Hao

    2016-01-01

    Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to "learning effect". The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.

  4. Run-time scheduling and execution of loops on message passing machines

    NASA Technical Reports Server (NTRS)

    Crowley, Kay; Saltz, Joel; Mirchandaney, Ravi; Berryman, Harry

    1989-01-01

    Sparse system solvers and general purpose codes for solving partial differential equations are examples of the many types of problems whose irregularity can result in poor performance on distributed memory machines. Often, the data structures used in these problems are very flexible. Crucial details concerning loop dependences are encoded in these structures rather than being explicitly represented in the program. Good methods for parallelizing and partitioning these types of problems require assignment of computations in rather arbitrary ways. Naive implementations of programs on distributed memory machines requiring general loop partitions can be extremely inefficient. Instead, the scheduling mechanism needs to capture the data reference patterns of the loops in order to partition the problem. First, the indices assigned to each processor must be locally numbered. Next, it is necessary to precompute what information is needed by each processor at various points in the computation. The precomputed information is then used to generate an execution template designed to carry out the computation, communication, and partitioning of data, in an optimized manner. The design is presented for a general preprocessor and schedule executer, the structures of which do not vary, even though the details of the computation and of the type of information are problem dependent.

  5. Run-time scheduling and execution of loops on message passing machines

    NASA Technical Reports Server (NTRS)

    Saltz, Joel; Crowley, Kathleen; Mirchandaney, Ravi; Berryman, Harry

    1990-01-01

    Sparse system solvers and general purpose codes for solving partial differential equations are examples of the many types of problems whose irregularity can result in poor performance on distributed memory machines. Often, the data structures used in these problems are very flexible. Crucial details concerning loop dependences are encoded in these structures rather than being explicitly represented in the program. Good methods for parallelizing and partitioning these types of problems require assignment of computations in rather arbitrary ways. Naive implementations of programs on distributed memory machines requiring general loop partitions can be extremely inefficient. Instead, the scheduling mechanism needs to capture the data reference patterns of the loops in order to partition the problem. First, the indices assigned to each processor must be locally numbered. Next, it is necessary to precompute what information is needed by each processor at various points in the computation. The precomputed information is then used to generate an execution template designed to carry out the computation, communication, and partitioning of data, in an optimized manner. The design is presented for a general preprocessor and schedule executer, the structures of which do not vary, even though the details of the computation and of the type of information are problem dependent.

  6. On-Line Scheduling of Parallel Machines

    DTIC Science & Technology

    1990-11-01

    machine without losing any work; this is referred to as the preemptive model. In contrast to the nonpreemptive model which we have considered in this paper...that there exists no schedule of length d. The 2-relaxed decision procedure is as follows. Put each job into the queue of the slowest machine Mk such...in their queues . If a machine’s queue is empty it takes jobs to process from the queue of the first machine that is slower than it and that has a

  7. Open shop scheduling problem to minimize total weighted completion time

    NASA Astrophysics Data System (ADS)

    Bai, Danyu; Zhang, Zhihai; Zhang, Qiang; Tang, Mengqian

    2017-01-01

    A given number of jobs in an open shop scheduling environment must each be processed for given amounts of time on each of a given set of machines in an arbitrary sequence. This study aims to achieve a schedule that minimizes total weighted completion time. Owing to the strong NP-hardness of the problem, the weighted shortest processing time block (WSPTB) heuristic is presented to obtain approximate solutions for large-scale problems. Performance analysis proves the asymptotic optimality of the WSPTB heuristic in the sense of probability limits. The largest weight block rule is provided to seek optimal schedules in polynomial time for a special case. A hybrid discrete differential evolution algorithm is designed to obtain high-quality solutions for moderate-scale problems. Simulation experiments demonstrate the effectiveness of the proposed algorithms.

  8. Discrete particle swarm optimization to solve multi-objective limited-wait hybrid flow shop scheduling problem

    NASA Astrophysics Data System (ADS)

    Santosa, B.; Siswanto, N.; Fiqihesa

    2018-04-01

    This paper proposes a discrete Particle Swam Optimization (PSO) to solve limited-wait hybrid flowshop scheduing problem with multi objectives. Flow shop schedulimg represents the condition when several machines are arranged in series and each job must be processed at each machine with same sequence. The objective functions are minimizing completion time (makespan), total tardiness time, and total machine idle time. Flow shop scheduling model always grows to cope with the real production system accurately. Since flow shop scheduling is a NP-Hard problem then the most suitable method to solve is metaheuristics. One of metaheuristics algorithm is Particle Swarm Optimization (PSO), an algorithm which is based on the behavior of a swarm. Originally, PSO was intended to solve continuous optimization problems. Since flow shop scheduling is a discrete optimization problem, then, we need to modify PSO to fit the problem. The modification is done by using probability transition matrix mechanism. While to handle multi objectives problem, we use Pareto Optimal (MPSO). The results of MPSO is better than the PSO because the MPSO solution set produced higher probability to find the optimal solution. Besides the MPSO solution set is closer to the optimal solution

  9. Efficient bounding schemes for the two-center hybrid flow shop scheduling problem with removal times.

    PubMed

    Hidri, Lotfi; Gharbi, Anis; Louly, Mohamed Aly

    2014-01-01

    We focus on the two-center hybrid flow shop scheduling problem with identical parallel machines and removal times. The job removal time is the required duration to remove it from a machine after its processing. The objective is to minimize the maximum completion time (makespan). A heuristic and a lower bound are proposed for this NP-Hard problem. These procedures are based on the optimal solution of the parallel machine scheduling problem with release dates and delivery times. The heuristic is composed of two phases. The first one is a constructive phase in which an initial feasible solution is provided, while the second phase is an improvement one. Intensive computational experiments have been conducted to confirm the good performance of the proposed procedures.

  10. Efficient Bounding Schemes for the Two-Center Hybrid Flow Shop Scheduling Problem with Removal Times

    PubMed Central

    2014-01-01

    We focus on the two-center hybrid flow shop scheduling problem with identical parallel machines and removal times. The job removal time is the required duration to remove it from a machine after its processing. The objective is to minimize the maximum completion time (makespan). A heuristic and a lower bound are proposed for this NP-Hard problem. These procedures are based on the optimal solution of the parallel machine scheduling problem with release dates and delivery times. The heuristic is composed of two phases. The first one is a constructive phase in which an initial feasible solution is provided, while the second phase is an improvement one. Intensive computational experiments have been conducted to confirm the good performance of the proposed procedures. PMID:25610911

  11. Competitive two-agent scheduling problems to minimize the weighted combination of makespans in a two-machine open shop

    NASA Astrophysics Data System (ADS)

    Jiang, Fuhong; Zhang, Xingong; Bai, Danyu; Wu, Chin-Chia

    2018-04-01

    In this article, a competitive two-agent scheduling problem in a two-machine open shop is studied. The objective is to minimize the weighted sum of the makespans of two competitive agents. A complexity proof is presented for minimizing the weighted combination of the makespan of each agent if the weight α belonging to agent B is arbitrary. Furthermore, two pseudo-polynomial-time algorithms using the largest alternate processing time (LAPT) rule are presented. Finally, two approximation algorithms are presented if the weight is equal to one. Additionally, another approximation algorithm is presented if the weight is larger than one.

  12. Comparison of multiobjective evolutionary algorithms for operations scheduling under machine availability constraints.

    PubMed

    Frutos, M; Méndez, M; Tohmé, F; Broz, D

    2013-01-01

    Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs) for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier.

  13. Comparison of Multiobjective Evolutionary Algorithms for Operations Scheduling under Machine Availability Constraints

    PubMed Central

    Frutos, M.; Méndez, M.; Tohmé, F.; Broz, D.

    2013-01-01

    Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs) for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier. PMID:24489502

  14. Performance of Extended Local Clustering Organization (LCO) for Large Scale Job-Shop Scheduling Problem (JSP)

    NASA Astrophysics Data System (ADS)

    Konno, Yohko; Suzuki, Keiji

    This paper describes an approach to development of a solution algorithm of a general-purpose for large scale problems using “Local Clustering Organization (LCO)” as a new solution for Job-shop scheduling problem (JSP). Using a performance effective large scale scheduling in the study of usual LCO, a solving JSP keep stability induced better solution is examined. In this study for an improvement of a performance of a solution for JSP, processes to a optimization by LCO is examined, and a scheduling solution-structure is extended to a new solution-structure based on machine-division. A solving method introduced into effective local clustering for the solution-structure is proposed as an extended LCO. An extended LCO has an algorithm which improves scheduling evaluation efficiently by clustering of parallel search which extends over plural machines. A result verified by an application of extended LCO on various scale of problems proved to conduce to minimizing make-span and improving on the stable performance.

  15. Prediction based proactive thermal virtual machine scheduling in green clouds.

    PubMed

    Kinger, Supriya; Kumar, Rajesh; Sharma, Anju

    2014-01-01

    Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated.

  16. Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds

    PubMed Central

    Kinger, Supriya; Kumar, Rajesh; Sharma, Anju

    2014-01-01

    Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated. PMID:24737962

  17. A note on resource allocation scheduling with group technology and learning effects on a single machine

    NASA Astrophysics Data System (ADS)

    Lu, Yuan-Yuan; Wang, Ji-Bo; Ji, Ping; He, Hongyu

    2017-09-01

    In this article, single-machine group scheduling with learning effects and convex resource allocation is studied. The goal is to find the optimal job schedule, the optimal group schedule, and resource allocations of jobs and groups. For the problem of minimizing the makespan subject to limited resource availability, it is proved that the problem can be solved in polynomial time under the condition that the setup times of groups are independent. For the general setup times of groups, a heuristic algorithm and a branch-and-bound algorithm are proposed, respectively. Computational experiments show that the performance of the heuristic algorithm is fairly accurate in obtaining near-optimal solutions.

  18. Scheduling Jobs and a Variable Maintenance on a Single Machine with Common Due-Date Assignment

    PubMed Central

    Wan, Long

    2014-01-01

    We investigate a common due-date assignment scheduling problem with a variable maintenance on a single machine. The goal is to minimize the total earliness, tardiness, and due-date cost. We derive some properties on an optimal solution for our problem. For a special case with identical jobs we propose an optimal polynomial time algorithm followed by a numerical example. PMID:25147861

  19. A hybrid dynamic harmony search algorithm for identical parallel machines scheduling

    NASA Astrophysics Data System (ADS)

    Chen, Jing; Pan, Quan-Ke; Wang, Ling; Li, Jun-Qing

    2012-02-01

    In this article, a dynamic harmony search (DHS) algorithm is proposed for the identical parallel machines scheduling problem with the objective to minimize makespan. First, an encoding scheme based on a list scheduling rule is developed to convert the continuous harmony vectors to discrete job assignments. Second, the whole harmony memory (HM) is divided into multiple small-sized sub-HMs, and each sub-HM performs evolution independently and exchanges information with others periodically by using a regrouping schedule. Third, a novel improvisation process is applied to generate a new harmony by making use of the information of harmony vectors in each sub-HM. Moreover, a local search strategy is presented and incorporated into the DHS algorithm to find promising solutions. Simulation results show that the hybrid DHS (DHS_LS) is very competitive in comparison to its competitors in terms of mean performance and average computational time.

  20. Job shop scheduling model for non-identic machine with fixed delivery time to minimize tardiness

    NASA Astrophysics Data System (ADS)

    Kusuma, K. K.; Maruf, A.

    2016-02-01

    Scheduling non-identic machines problem with low utilization characteristic and fixed delivery time are frequent in manufacture industry. This paper propose a mathematical model to minimize total tardiness for non-identic machines in job shop environment. This model will be categorized as an integer linier programming model and using branch and bound algorithm as the solver method. We will use fixed delivery time as main constraint and different processing time to process a job. The result of this proposed model shows that the utilization of production machines can be increase with minimal tardiness using fixed delivery time as constraint.

  1. Single machine total completion time minimization scheduling with a time-dependent learning effect and deteriorating jobs

    NASA Astrophysics Data System (ADS)

    Wang, Ji-Bo; Wang, Ming-Zheng; Ji, Ping

    2012-05-01

    In this article, we consider a single machine scheduling problem with a time-dependent learning effect and deteriorating jobs. By the effects of time-dependent learning and deterioration, we mean that the job processing time is defined by a function of its starting time and total normal processing time of jobs in front of it in the sequence. The objective is to determine an optimal schedule so as to minimize the total completion time. This problem remains open for the case of -1 < a < 0, where a denotes the learning index; we show that an optimal schedule of the problem is V-shaped with respect to job normal processing times. Three heuristic algorithms utilising the V-shaped property are proposed, and computational experiments show that the last heuristic algorithm performs effectively and efficiently in obtaining near-optimal solutions.

  2. A novel hybrid genetic algorithm to solve the make-to-order sequence-dependent flow-shop scheduling problem

    NASA Astrophysics Data System (ADS)

    Mirabi, Mohammad; Fatemi Ghomi, S. M. T.; Jolai, F.

    2014-04-01

    Flow-shop scheduling problem (FSP) deals with the scheduling of a set of n jobs that visit a set of m machines in the same order. As the FSP is NP-hard, there is no efficient algorithm to reach the optimal solution of the problem. To minimize the holding, delay and setup costs of large permutation flow-shop scheduling problems with sequence-dependent setup times on each machine, this paper develops a novel hybrid genetic algorithm (HGA) with three genetic operators. Proposed HGA applies a modified approach to generate a pool of initial solutions, and also uses an improved heuristic called the iterated swap procedure to improve the initial solutions. We consider the make-to-order production approach that some sequences between jobs are assumed as tabu based on maximum allowable setup cost. In addition, the results are compared to some recently developed heuristics and computational experimental results show that the proposed HGA performs very competitively with respect to accuracy and efficiency of solution.

  3. Outsourcing and scheduling for a two-machine flow shop with release times

    NASA Astrophysics Data System (ADS)

    Ahmadizar, Fardin; Amiri, Zeinab

    2018-03-01

    This article addresses a two-machine flow shop scheduling problem where jobs are released intermittently and outsourcing is allowed. The first operations of outsourced jobs are processed by the first subcontractor, they are transported in batches to the second subcontractor for processing their second operations, and finally they are transported back to the manufacturer. The objective is to select a subset of jobs to be outsourced, to schedule both the in-house and the outsourced jobs, and to determine a transportation plan for the outsourced jobs so as to minimize the sum of the makespan and the outsourcing and transportation costs. Two mathematical models of the problem and several necessary optimality conditions are presented. A solution approach is then proposed by incorporating the dominance properties with an ant colony algorithm. Finally, computational experiments are conducted to evaluate the performance of the models and solution approach.

  4. Improved teaching-learning-based and JAYA optimization algorithms for solving flexible flow shop scheduling problems

    NASA Astrophysics Data System (ADS)

    Buddala, Raviteja; Mahapatra, Siba Sankar

    2017-11-01

    Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having `g' operations is performed on `g' operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NP-hard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain near-optimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching-learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP.

  5. MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

    PubMed

    Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

    2014-01-01

    We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

  6. MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines

    PubMed Central

    Ozmutlu, H. Cenk

    2014-01-01

    We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

  7. Approximation algorithms for scheduling unrelated parallel machines with release dates

    NASA Astrophysics Data System (ADS)

    Avdeenko, T. V.; Mesentsev, Y. A.; Estraykh, I. V.

    2017-01-01

    In this paper we propose approaches to optimal scheduling of unrelated parallel machines with release dates. One approach is based on the scheme of dynamic programming modified with adaptive narrowing of search domain ensuring its computational effectiveness. We discussed complexity of the exact schedules synthesis and compared it with approximate, close to optimal, solutions. Also we explain how the algorithm works for the example of two unrelated parallel machines and five jobs with release dates. Performance results that show the efficiency of the proposed approach have been given.

  8. 41 CFR 101-26.509-2 - Requisitioning tabulating machine cards not available from Federal Supply Schedule contracts.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... machine cards not available from Federal Supply Schedule contracts. 101-26.509-2 Section 101-26.509-2... Programs § 101-26.509-2 Requisitioning tabulating machine cards not available from Federal Supply Schedule contracts. (a) Requisitions for tabulating machine cards covered by Federal Supply Schedule contracts which...

  9. Application of a hybrid generation/utility assessment heuristic to a class of scheduling problems

    NASA Technical Reports Server (NTRS)

    Heyward, Ann O.

    1989-01-01

    A two-stage heuristic solution approach for a class of multiobjective, n-job, 1-machine scheduling problems is described. Minimization of job-to-job interference for n jobs is sought. The first stage generates alternative schedule sequences by interchanging pairs of schedule elements. The set of alternative sequences can represent nodes of a decision tree; each node is reached via decision to interchange job elements. The second stage selects the parent node for the next generation of alternative sequences through automated paired comparison of objective performance for all current nodes. An application of the heuristic approach to communications satellite systems planning is presented.

  10. Estimates of the absolute error and a scheme for an approximate solution to scheduling problems

    NASA Astrophysics Data System (ADS)

    Lazarev, A. A.

    2009-02-01

    An approach is proposed for estimating absolute errors and finding approximate solutions to classical NP-hard scheduling problems of minimizing the maximum lateness for one or many machines and makespan is minimized. The concept of a metric (distance) between instances of the problem is introduced. The idea behind the approach is, given the problem instance, to construct another instance for which an optimal or approximate solution can be found at the minimum distance from the initial instance in the metric introduced. Instead of solving the original problem (instance), a set of approximating polynomially/pseudopolynomially solvable problems (instances) are considered, an instance at the minimum distance from the given one is chosen, and the resulting schedule is then applied to the original instance.

  11. Scheduling with non-decreasing deterioration jobs and variable maintenance activities on a single machine

    NASA Astrophysics Data System (ADS)

    Zhang, Xingong; Yin, Yunqiang; Wu, Chin-Chia

    2017-01-01

    There is a situation found in many manufacturing systems, such as steel rolling mills, fire fighting or single-server cycle-queues, where a job that is processed later consumes more time than that same job when processed earlier. The research finds that machine maintenance can improve the worsening of processing conditions. After maintenance activity, the machine will be restored. The maintenance duration is a positive and non-decreasing differentiable convex function of the total processing times of the jobs between maintenance activities. Motivated by this observation, the makespan and the total completion time minimization problems in the scheduling of jobs with non-decreasing rates of job processing time on a single machine are considered in this article. It is shown that both the makespan and the total completion time minimization problems are NP-hard in the strong sense when the number of maintenance activities is arbitrary, while the makespan minimization problem is NP-hard in the ordinary sense when the number of maintenance activities is fixed. If the deterioration rates of the jobs are identical and the maintenance duration is a linear function of the total processing times of the jobs between maintenance activities, then this article shows that the group balance principle is satisfied for the makespan minimization problem. Furthermore, two polynomial-time algorithms are presented for solving the makespan problem and the total completion time problem under identical deterioration rates, respectively.

  12. Human-Machine Collaborative Optimization via Apprenticeship Scheduling

    DTIC Science & Technology

    2016-09-09

    prenticeship Scheduling (COVAS), which performs ma- chine learning using human expert demonstration, in conjunction with optimization, to automatically and ef...ficiently produce optimal solutions to challenging real- world scheduling problems. COVAS first learns a policy from human scheduling demonstration via...apprentice- ship learning , then uses this initial solution to provide a tight bound on the value of the optimal solution, thereby substantially

  13. An ant colony optimization heuristic for an integrated production and distribution scheduling problem

    NASA Astrophysics Data System (ADS)

    Chang, Yung-Chia; Li, Vincent C.; Chiang, Chia-Ju

    2014-04-01

    Make-to-order or direct-order business models that require close interaction between production and distribution activities have been adopted by many enterprises in order to be competitive in demanding markets. This article considers an integrated production and distribution scheduling problem in which jobs are first processed by one of the unrelated parallel machines and then distributed to corresponding customers by capacitated vehicles without intermediate inventory. The objective is to find a joint production and distribution schedule so that the weighted sum of total weighted job delivery time and the total distribution cost is minimized. This article presents a mathematical model for describing the problem and designs an algorithm using ant colony optimization. Computational experiments illustrate that the algorithm developed is capable of generating near-optimal solutions. The computational results also demonstrate the value of integrating production and distribution in the model for the studied problem.

  14. AI techniques for a space application scheduling problem

    NASA Technical Reports Server (NTRS)

    Thalman, N.; Sparn, T.; Jaffres, L.; Gablehouse, D.; Judd, D.; Russell, C.

    1991-01-01

    Scheduling is a very complex optimization problem which can be categorized as an NP-complete problem. NP-complete problems are quite diverse, as are the algorithms used in searching for an optimal solution. In most cases, the best solutions that can be derived for these combinatorial explosive problems are near-optimal solutions. Due to the complexity of the scheduling problem, artificial intelligence (AI) can aid in solving these types of problems. Some of the factors are examined which make space application scheduling problems difficult and presents a fairly new AI-based technique called tabu search as applied to a real scheduling application. the specific problem is concerned with scheduling application. The specific problem is concerned with scheduling solar and stellar observations for the SOLar-STellar Irradiance Comparison Experiment (SOLSTICE) instrument in a constrained environment which produces minimum impact on the other instruments and maximizes target observation times. The SOLSTICE instrument will gly on-board the Upper Atmosphere Research Satellite (UARS) in 1991, and a similar instrument will fly on the earth observing system (Eos).

  15. A Solution Method of Job-shop Scheduling Problems by the Idle Time Shortening Type Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Ida, Kenichi; Osawa, Akira

    In this paper, we propose a new idle time shortening method for Job-shop scheduling problems (JSPs). We insert its method into a genetic algorithm (GA). The purpose of JSP is to find a schedule with the minimum makespan. We suppose that it is effective to reduce idle time of a machine in order to improve the makespan. The left shift is a famous algorithm in existing algorithms for shortening idle time. The left shift can not arrange the work to idle time. For that reason, some idle times are not shortened by the left shift. We propose two kinds of algorithms which shorten such idle time. Next, we combine these algorithms and the reversal of a schedule. We apply GA with its algorithm to benchmark problems and we show its effectiveness.

  16. Machine Learning Based Online Performance Prediction for Runtime Parallelization and Task Scheduling

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Li, J; Ma, X; Singh, K

    2008-10-09

    With the emerging many-core paradigm, parallel programming must extend beyond its traditional realm of scientific applications. Converting existing sequential applications as well as developing next-generation software requires assistance from hardware, compilers and runtime systems to exploit parallelism transparently within applications. These systems must decompose applications into tasks that can be executed in parallel and then schedule those tasks to minimize load imbalance. However, many systems lack a priori knowledge about the execution time of all tasks to perform effective load balancing with low scheduling overhead. In this paper, we approach this fundamental problem using machine learning techniques first to generatemore » performance models for all tasks and then applying those models to perform automatic performance prediction across program executions. We also extend an existing scheduling algorithm to use generated task cost estimates for online task partitioning and scheduling. We implement the above techniques in the pR framework, which transparently parallelizes scripts in the popular R language, and evaluate their performance and overhead with both a real-world application and a large number of synthetic representative test scripts. Our experimental results show that our proposed approach significantly improves task partitioning and scheduling, with maximum improvements of 21.8%, 40.3% and 22.1% and average improvements of 15.9%, 16.9% and 4.2% for LMM (a real R application) and synthetic test cases with independent and dependent tasks, respectively.« less

  17. Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

    NASA Astrophysics Data System (ADS)

    Ausaf, Muhammad Farhan; Gao, Liang; Li, Xinyu

    2015-12-01

    For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

  18. Skipping Strategy (SS) for Initial Population of Job-Shop Scheduling Problem

    NASA Astrophysics Data System (ADS)

    Abdolrazzagh-Nezhad, M.; Nababan, E. B.; Sarim, H. M.

    2018-03-01

    Initial population in job-shop scheduling problem (JSSP) is an essential step to obtain near optimal solution. Techniques used to solve JSSP are computationally demanding. Skipping strategy (SS) is employed to acquire initial population after sequence of job on machine and sequence of operations (expressed in Plates-jobs and mPlates-jobs) are determined. The proposed technique is applied to benchmark datasets and the results are compared to that of other initialization techniques. It is shown that the initial population obtained from the SS approach could generate optimal solution.

  19. Multiple-variable neighbourhood search for the single-machine total weighted tardiness problem

    NASA Astrophysics Data System (ADS)

    Chung, Tsui-Ping; Fu, Qunjie; Liao, Ching-Jong; Liu, Yi-Ting

    2017-07-01

    The single-machine total weighted tardiness (SMTWT) problem is a typical discrete combinatorial optimization problem in the scheduling literature. This problem has been proved to be NP hard and thus provides a challenging area for metaheuristics, especially the variable neighbourhood search algorithm. In this article, a multiple variable neighbourhood search (m-VNS) algorithm with multiple neighbourhood structures is proposed to solve the problem. Special mechanisms named matching and strengthening operations are employed in the algorithm, which has an auto-revising local search procedure to explore the solution space beyond local optimality. Two aspects, searching direction and searching depth, are considered, and neighbourhood structures are systematically exchanged. Experimental results show that the proposed m-VNS algorithm outperforms all the compared algorithms in solving the SMTWT problem.

  20. Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem

    NASA Astrophysics Data System (ADS)

    Tang, Dunbing; Dai, Min

    2015-09-01

    The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production planning and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed smalland large-size instances, respectively. The experimental results show that the approach can save 5%-10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%-4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.

  1. The compound Atwood machine problem

    NASA Astrophysics Data System (ADS)

    Lopes Coelho, R.

    2017-05-01

    The present paper accounts for progress in physics teaching in the sense that a problem, which has been closed to students for being too difficult, is gained for the high school curriculum. This problem is the compound Atwood machine with three bodies. Its introduction into high school classes is based on a recent study on the weighing of an Atwood machine.

  2. The comparison of predictive scheduling algorithms for different sizes of job shop scheduling problems

    NASA Astrophysics Data System (ADS)

    Paprocka, I.; Kempa, W. M.; Grabowik, C.; Kalinowski, K.; Krenczyk, D.

    2016-08-01

    In the paper a survey of predictive and reactive scheduling methods is done in order to evaluate how the ability of prediction of reliability characteristics influences over robustness criteria. The most important reliability characteristics are: Mean Time to Failure, Mean Time of Repair. Survey analysis is done for a job shop scheduling problem. The paper answers the question: what method generates robust schedules in the case of a bottleneck failure occurrence before, at the beginning of planned maintenance actions or after planned maintenance actions? Efficiency of predictive schedules is evaluated using criteria: makespan, total tardiness, flow time, idle time. Efficiency of reactive schedules is evaluated using: solution robustness criterion and quality robustness criterion. This paper is the continuation of the research conducted in the paper [1], where the survey of predictive and reactive scheduling methods is done only for small size scheduling problems.

  3. Learning dominance relations in combinatorial search problems

    NASA Technical Reports Server (NTRS)

    Yu, Chee-Fen; Wah, Benjamin W.

    1988-01-01

    Dominance relations commonly are used to prune unnecessary nodes in search graphs, but they are problem-dependent and cannot be derived by a general procedure. The authors identify machine learning of dominance relations and the applicable learning mechanisms. A study of learning dominance relations using learning by experimentation is described. This system has been able to learn dominance relations for the 0/1-knapsack problem, an inventory problem, the reliability-by-replication problem, the two-machine flow shop problem, a number of single-machine scheduling problems, and a two-machine scheduling problem. It is considered that the same methodology can be extended to learn dominance relations in general.

  4. Machine Maintenance Scheduling with Reliability Engineering Method and Maintenance Value Stream Mapping

    NASA Astrophysics Data System (ADS)

    Sembiring, N.; Nasution, A. H.

    2018-02-01

    Corrective maintenance i.e replacing or repairing the machine component after machine break down always done in a manufacturing company. It causes the production process must be stopped. Production time will decrease due to the maintenance team must replace or repair the damage machine component. This paper proposes a preventive maintenance’s schedule for a critical component of a critical machine of an crude palm oil and kernel company due to increase maintenance efficiency. The Reliability Engineering & Maintenance Value Stream Mapping is used as a method and a tool to analize the reliability of the component and reduce the wastage in any process by segregating value added and non value added activities.

  5. Optimal recombination in genetic algorithms for flowshop scheduling problems

    NASA Astrophysics Data System (ADS)

    Kovalenko, Julia

    2016-10-01

    The optimal recombination problem consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. We prove NP-hardness of the optimal recombination for various variants of the flowshop scheduling problem with makespan criterion and criterion of maximum lateness. An algorithm for solving the optimal recombination problem for permutation flowshop problems is built, using enumeration of prefect matchings in a special bipartite graph. The algorithm is adopted for the classical flowshop scheduling problem and for the no-wait flowshop problem. It is shown that the optimal recombination problem for the permutation flowshop scheduling problem is solvable in polynomial time for almost all pairs of parent solutions as the number of jobs tends to infinity.

  6. Assessment of New Load Schedules for the Machine Calibration of a Force Balance

    NASA Technical Reports Server (NTRS)

    Ulbrich, N.; Gisler, R.; Kew, R.

    2015-01-01

    New load schedules for the machine calibration of a six-component force balance are currently being developed and evaluated at the NASA Ames Balance Calibration Laboratory. One of the proposed load schedules is discussed in the paper. It has a total of 2082 points that are distributed across 16 load series. Several criteria were applied to define the load schedule. It was decided, for example, to specify the calibration load set in force balance format as this approach greatly simplifies the definition of the lower and upper bounds of the load schedule. In addition, all loads are assumed to be applied in a calibration machine by using the one-factor-at-a-time approach. At first, all single-component loads are applied in six load series. Then, three two-component load series are applied. They consist of the load pairs (N1, N2), (S1, S2), and (RM, AF). Afterwards, four three-component load series are applied. They consist of the combinations (N1, N2, AF), (S1, S2, AF), (N1, N2, RM), and (S1, S2, RM). In the next step, one four-component load series is applied. It is the load combination (N1, N2, S1, S2). Finally, two five-component load series are applied. They are the load combination (N1, N2, S1, S2, AF) and (N1, N2, S1, S2, RM). The maximum difference between loads of two subsequent data points of the load schedule is limited to 33 % of capacity. This constraint helps avoid unwanted load "jumps" in the load schedule that can have a negative impact on the performance of a calibration machine. Only loadings of the single- and two-component load series are loaded to 100 % of capacity. This approach was selected because it keeps the total number of calibration points to a reasonable limit while still allowing for the application of some of the more complex load combinations. Data from two of NASA's force balances is used to illustrate important characteristics of the proposed 2082-point calibration load schedule.

  7. Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization.

    PubMed

    Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

    2016-01-01

    Taking resource allocation into account, flexible job shop problem (FJSP) is a class of complex scheduling problem in manufacturing system. In order to utilize the machine resources rationally, multi-objective particle swarm optimization (MOPSO) integrating with variable neighborhood search is introduced to address FJSP efficiently. Firstly, the assignment rules (AL) and dispatching rules (DR) are provided to initialize the population. And then special discrete operators are designed to produce new individuals and earliest completion machine (ECM) is adopted in the disturbance operator to escape the optima. Secondly, personal-best archives (cognitive memories) and global-best archive (social memory), which are updated by the predefined non-dominated archive update strategy, are simultaneously designed to preserve non-dominated individuals and select personal-best positions and the global-best position. Finally, three neighborhoods are provided to search the neighborhoods of global-best archive for enhancing local search ability. The proposed algorithm is evaluated by using Kacem instances and Brdata instances, and a comparison with other approaches shows the effectiveness of the proposed algorithm for FJSP.

  8. Optimized Hypervisor Scheduler for Parallel Discrete Event Simulations on Virtual Machine Platforms

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Yoginath, Srikanth B; Perumalla, Kalyan S

    2013-01-01

    With the advent of virtual machine (VM)-based platforms for parallel computing, it is now possible to execute parallel discrete event simulations (PDES) over multiple virtual machines, in contrast to executing in native mode directly over hardware as is traditionally done over the past decades. While mature VM-based parallel systems now offer new, compelling benefits such as serviceability, dynamic reconfigurability and overall cost effectiveness, the runtime performance of parallel applications can be significantly affected. In particular, most VM-based platforms are optimized for general workloads, but PDES execution exhibits unique dynamics significantly different from other workloads. Here we first present results frommore » experiments that highlight the gross deterioration of the runtime performance of VM-based PDES simulations when executed using traditional VM schedulers, quantitatively showing the bad scaling properties of the scheduler as the number of VMs is increased. The mismatch is fundamental in nature in the sense that any fairness-based VM scheduler implementation would exhibit this mismatch with PDES runs. We also present a new scheduler optimized specifically for PDES applications, and describe its design and implementation. Experimental results obtained from running PDES benchmarks (PHOLD and vehicular traffic simulations) over VMs show over an order of magnitude improvement in the run time of the PDES-optimized scheduler relative to the regular VM scheduler, with over 20 reduction in run time of simulations using up to 64 VMs. The observations and results are timely in the context of emerging systems such as cloud platforms and VM-based high performance computing installations, highlighting to the community the need for PDES-specific support, and the feasibility of significantly reducing the runtime overhead for scalable PDES on VM platforms.« less

  9. Deconstructing Nowicki and Smutnickis i-TSAB tabu search algorithm for the job-shop scheduling problem.

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Whitley, L. Darrell; Watson, Jean-Paul; Howe, Adele E.

    Over the last decade and a half, tabu search algorithms for machine scheduling have gained a near-mythical reputation by consistently equaling or establishing state-of-the-art performance levels on a range of academic and real-world problems. Yet, despite these successes, remarkably little research has been devoted to developing an understanding of why tabu search is so effective on this problem class. In this paper, we report results that provide significant progress in this direction. We consider Nowicki and Smutnicki's i-TSAB tabu search algorithm, which represents the current state-of-the-art for the makespan-minimization form of the classical jobshop scheduling problem. Via a series ofmore » controlled experiments, we identify those components of i-TSAB that enable it to achieve state-of-the-art performance levels. In doing so, we expose a number of misconceptions regarding the behavior and/or benefits of tabu search and other local search metaheuristics for the job-shop problem. Our results also serve to focus future research, by identifying those specific directions that are most likely to yield further improvements in performance.« less

  10. Decomposition of timed automata for solving scheduling problems

    NASA Astrophysics Data System (ADS)

    Nishi, Tatsushi; Wakatake, Masato

    2014-03-01

    A decomposition algorithm for scheduling problems based on timed automata (TA) model is proposed. The problem is represented as an optimal state transition problem for TA. The model comprises of the parallel composition of submodels such as jobs and resources. The procedure of the proposed methodology can be divided into two steps. The first step is to decompose the TA model into several submodels by using decomposable condition. The second step is to combine individual solution of subproblems for the decomposed submodels by the penalty function method. A feasible solution for the entire model is derived through the iterated computation of solving the subproblem for each submodel. The proposed methodology is applied to solve flowshop and jobshop scheduling problems. Computational experiments demonstrate the effectiveness of the proposed algorithm compared with a conventional TA scheduling algorithm without decomposition.

  11. Balancing antagonistic time and resource utilization constraints in over-subscribed scheduling problems

    NASA Technical Reports Server (NTRS)

    Smith, Stephen F.; Pathak, Dhiraj K.

    1991-01-01

    In this paper, we report work aimed at applying concepts of constraint-based problem structuring and multi-perspective scheduling to an over-subscribed scheduling problem. Previous research has demonstrated the utility of these concepts as a means for effectively balancing conflicting objectives in constraint-relaxable scheduling problems, and our goal here is to provide evidence of their similar potential in the context of HST observation scheduling. To this end, we define and experimentally assess the performance of two time-bounded heuristic scheduling strategies in balancing the tradeoff between resource setup time minimization and satisfaction of absolute time constraints. The first strategy considered is motivated by dispatch-based manufacturing scheduling research, and employs a problem decomposition that concentrates local search on minimizing resource idle time due to setup activities. The second is motivated by research in opportunistic scheduling and advocates a problem decomposition that focuses attention on the goal activities that have the tightest temporal constraints. Analysis of experimental results gives evidence of differential superiority on the part of each strategy in different problem solving circumstances. A composite strategy based on recognition of characteristics of the current problem solving state is then defined and tested to illustrate the potential benefits of constraint-based problem structuring and multi-perspective scheduling in over-subscribe scheduling problems.

  12. A bicriteria heuristic for an elective surgery scheduling problem.

    PubMed

    Marques, Inês; Captivo, M Eugénia; Vaz Pato, Margarida

    2015-09-01

    Resource rationalization and reduction of waiting lists for surgery are two main guidelines for hospital units outlined in the Portuguese National Health Plan. This work is dedicated to an elective surgery scheduling problem arising in a Lisbon public hospital. In order to increase the surgical suite's efficiency and to reduce the waiting lists for surgery, two objectives are considered: maximize surgical suite occupation and maximize the number of surgeries scheduled. This elective surgery scheduling problem consists of assigning an intervention date, an operating room and a starting time for elective surgeries selected from the hospital waiting list. Accordingly, a bicriteria surgery scheduling problem arising in the hospital under study is presented. To search for efficient solutions of the bicriteria optimization problem, the minimization of a weighted Chebyshev distance to a reference point is used. A constructive and improvement heuristic procedure specially designed to address the objectives of the problem is developed and results of computational experiments obtained with empirical data from the hospital are presented. This study shows that by using the bicriteria approach presented here it is possible to build surgical plans with very good performance levels. This method can be used within an interactive approach with the decision maker. It can also be easily adapted to other hospitals with similar scheduling conditions.

  13. Research on Production Scheduling System with Bottleneck Based on Multi-agent

    NASA Astrophysics Data System (ADS)

    Zhenqiang, Bao; Weiye, Wang; Peng, Wang; Pan, Quanke

    Aimed at the imbalance problem of resource capacity in Production Scheduling System, this paper uses Production Scheduling System based on multi-agent which has been constructed, and combines the dynamic and autonomous of Agent; the bottleneck problem in the scheduling is solved dynamically. Firstly, this paper uses Bottleneck Resource Agent to find out the bottleneck resource in the production line, analyses the inherent mechanism of bottleneck, and describes the production scheduling process based on bottleneck resource. Bottleneck Decomposition Agent harmonizes the relationship of job's arrival time and transfer time in Bottleneck Resource Agent and Non-Bottleneck Resource Agents, therefore, the dynamic scheduling problem is simplified as the single machine scheduling of each resource which takes part in the scheduling. Finally, the dynamic real-time scheduling problem is effectively solved in Production Scheduling System.

  14. Single Machine Scheduling and Due Date Assignment with Past-Sequence-Dependent Setup Time and Position-Dependent Processing Time

    PubMed Central

    Zhao, Chuan-Li; Hsu, Hua-Feng

    2014-01-01

    This paper considers single machine scheduling and due date assignment with setup time. The setup time is proportional to the length of the already processed jobs; that is, the setup time is past-sequence-dependent (p-s-d). It is assumed that a job's processing time depends on its position in a sequence. The objective functions include total earliness, the weighted number of tardy jobs, and the cost of due date assignment. We analyze these problems with two different due date assignment methods. We first consider the model with job-dependent position effects. For each case, by converting the problem to a series of assignment problems, we proved that the problems can be solved in O(n 4) time. For the model with job-independent position effects, we proved that the problems can be solved in O(n 3) time by providing a dynamic programming algorithm. PMID:25258727

  15. Single machine scheduling and due date assignment with past-sequence-dependent setup time and position-dependent processing time.

    PubMed

    Zhao, Chuan-Li; Hsu, Chou-Jung; Hsu, Hua-Feng

    2014-01-01

    This paper considers single machine scheduling and due date assignment with setup time. The setup time is proportional to the length of the already processed jobs; that is, the setup time is past-sequence-dependent (p-s-d). It is assumed that a job's processing time depends on its position in a sequence. The objective functions include total earliness, the weighted number of tardy jobs, and the cost of due date assignment. We analyze these problems with two different due date assignment methods. We first consider the model with job-dependent position effects. For each case, by converting the problem to a series of assignment problems, we proved that the problems can be solved in O(n(4)) time. For the model with job-independent position effects, we proved that the problems can be solved in O(n(3)) time by providing a dynamic programming algorithm.

  16. Performance comparison of some evolutionary algorithms on job shop scheduling problems

    NASA Astrophysics Data System (ADS)

    Mishra, S. K.; Rao, C. S. P.

    2016-09-01

    Job Shop Scheduling as a state space search problem belonging to NP-hard category due to its complexity and combinational explosion of states. Several naturally inspire evolutionary methods have been developed to solve Job Shop Scheduling Problems. In this paper the evolutionary methods namely Particles Swarm Optimization, Artificial Intelligence, Invasive Weed Optimization, Bacterial Foraging Optimization, Music Based Harmony Search Algorithms are applied and find tuned to model and solve Job Shop Scheduling Problems. To compare about 250 Bench Mark instances have been used to evaluate the performance of these algorithms. The capabilities of each these algorithms in solving Job Shop Scheduling Problems are outlined.

  17. Genetic algorithm parameters tuning for resource-constrained project scheduling problem

    NASA Astrophysics Data System (ADS)

    Tian, Xingke; Yuan, Shengrui

    2018-04-01

    Project Scheduling Problem (RCPSP) is a kind of important scheduling problem. To achieve a certain optimal goal such as the shortest duration, the smallest cost, the resource balance and so on, it is required to arrange the start and finish of all tasks under the condition of satisfying project timing constraints and resource constraints. In theory, the problem belongs to the NP-hard problem, and the model is abundant. Many combinatorial optimization problems are special cases of RCPSP, such as job shop scheduling, flow shop scheduling and so on. At present, the genetic algorithm (GA) has been used to deal with the classical RCPSP problem and achieved remarkable results. Vast scholars have also studied the improved genetic algorithm for the RCPSP problem, which makes it to solve the RCPSP problem more efficiently and accurately. However, for the selection of the main parameters of the genetic algorithm, there is no parameter optimization in these studies. Generally, we used the empirical method, but it cannot ensure to meet the optimal parameters. In this paper, the problem was carried out, which is the blind selection of parameters in the process of solving the RCPSP problem. We made sampling analysis, the establishment of proxy model and ultimately solved the optimal parameters.

  18. Asymptotic analysis of online algorithms and improved scheme for the flow shop scheduling problem with release dates

    NASA Astrophysics Data System (ADS)

    Bai, Danyu

    2015-08-01

    This paper discusses the flow shop scheduling problem to minimise the total quadratic completion time (TQCT) with release dates in offline and online environments. For this NP-hard problem, the investigation is focused on the performance of two online algorithms based on the Shortest Processing Time among Available jobs rule. Theoretical results indicate the asymptotic optimality of the algorithms as the problem scale is sufficiently large. To further enhance the quality of the original solutions, the improvement scheme is provided for these algorithms. A new lower bound with performance guarantee is provided, and computational experiments show the effectiveness of these heuristics. Moreover, several results of the single-machine TQCT problem with release dates are also obtained for the deduction of the main theorem.

  19. Analysis of Feeder Bus Network Design and Scheduling Problems

    PubMed Central

    Almasi, Mohammad Hadi; Karim, Mohamed Rehan

    2014-01-01

    A growing concern for public transit is its inability to shift passenger's mode from private to public transport. In order to overcome this problem, a more developed feeder bus network and matched schedules will play important roles. The present paper aims to review some of the studies performed on Feeder Bus Network Design and Scheduling Problem (FNDSP) based on three distinctive parts of the FNDSP setup, namely, problem description, problem characteristics, and solution approaches. The problems consist of different subproblems including data preparation, feeder bus network design, route generation, and feeder bus scheduling. Subsequently, descriptive analysis and classification of previous works are presented to highlight the main characteristics and solution methods. Finally, some of the issues and trends for future research are identified. This paper is targeted at dealing with the FNDSP to exhibit strategic and tactical goals and also contributes to the unification of the field which might be a useful complement to the few existing reviews. PMID:24526890

  20. Periodic Heterogeneous Vehicle Routing Problem With Driver Scheduling

    NASA Astrophysics Data System (ADS)

    Mardiana Panggabean, Ellis; Mawengkang, Herman; Azis, Zainal; Filia Sari, Rina

    2018-01-01

    The paper develops a model for the optimal management of logistic delivery of a given commodity. The company has different type of vehicles with different capacity to deliver the commodity for customers. The problem is then called Periodic Heterogeneous Vehicle Routing Problem (PHVRP). The goal is to schedule the deliveries according to feasible combinations of delivery days and to determine the scheduling of fleet and driver and routing policies of the vehicles. The objective is to minimize the sum of the costs of all routes over the planning horizon. We propose a combined approach of heuristic algorithm and exact method to solve the problem.

  1. The application of artificial intelligence to astronomical scheduling problems

    NASA Technical Reports Server (NTRS)

    Johnston, Mark D.

    1992-01-01

    Efficient utilization of expensive space- and ground-based observatories is an important goal for the astronomical community; the cost of modern observing facilities is enormous, and the available observing time is much less than the demand from astronomers around the world. The complexity and variety of scheduling constraints and goals has led several groups to investigate how artificial intelligence (AI) techniques might help solve these kinds of problems. The earliest and most successful of these projects was started at Space Telescope Science Institute in 1987 and has led to the development of the Spike scheduling system to support the scheduling of Hubble Space Telescope (HST). The aim of Spike at STScI is to allocate observations to timescales of days to a week observing all scheduling constraints and maximizing preferences that help ensure that observations are made at optimal times. Spike has been in use operationally for HST since shortly after the observatory was launched in Apr. 1990. Although developed specifically for HST scheduling, Spike was carefully designed to provide a general framework for similar (activity-based) scheduling problems. In particular, the tasks to be scheduled are defined in the system in general terms, and no assumptions about the scheduling timescale are built in. The mechanisms for describing, combining, and propagating temporal and other constraints and preferences are quite general. The success of this approach has been demonstrated by the application of Spike to the scheduling of other satellite observatories: changes to the system are required only in the specific constraints that apply, and not in the framework itself. In particular, the Spike framework is sufficiently flexible to handle both long-term and short-term scheduling, on timescales of years down to minutes or less. This talk will discuss recent progress made in scheduling search techniques, the lessons learned from early HST operations, the application of Spike

  2. Flexible Job-Shop Scheduling with Dual-Resource Constraints to Minimize Tardiness Using Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Paksi, A. B. N.; Ma'ruf, A.

    2016-02-01

    In general, both machines and human resources are needed for processing a job on production floor. However, most classical scheduling problems have ignored the possible constraint caused by availability of workers and have considered only machines as a limited resource. In addition, along with production technology development, routing flexibility appears as a consequence of high product variety and medium demand for each product. Routing flexibility is caused by capability of machines that offers more than one machining process. This paper presents a method to address scheduling problem constrained by both machines and workers, considering routing flexibility. Scheduling in a Dual-Resource Constrained shop is categorized as NP-hard problem that needs long computational time. Meta-heuristic approach, based on Genetic Algorithm, is used due to its practical implementation in industry. Developed Genetic Algorithm uses indirect chromosome representative and procedure to transform chromosome into Gantt chart. Genetic operators, namely selection, elitism, crossover, and mutation are developed to search the best fitness value until steady state condition is achieved. A case study in a manufacturing SME is used to minimize tardiness as objective function. The algorithm has shown 25.6% reduction of tardiness, equal to 43.5 hours.

  3. An Optimization Model for Scheduling Problems with Two-Dimensional Spatial Resource Constraint

    NASA Technical Reports Server (NTRS)

    Garcia, Christopher; Rabadi, Ghaith

    2010-01-01

    Traditional scheduling problems involve determining temporal assignments for a set of jobs in order to optimize some objective. Some scheduling problems also require the use of limited resources, which adds another dimension of complexity. In this paper we introduce a spatial resource-constrained scheduling problem that can arise in assembly, warehousing, cross-docking, inventory management, and other areas of logistics and supply chain management. This scheduling problem involves a twodimensional rectangular area as a limited resource. Each job, in addition to having temporal requirements, has a width and a height and utilizes a certain amount of space inside the area. We propose an optimization model for scheduling the jobs while respecting all temporal and spatial constraints.

  4. A Genetic Algorithm for Flow Shop Scheduling with Assembly Operations to Minimize Makespan

    NASA Astrophysics Data System (ADS)

    Bhongade, A. S.; Khodke, P. M.

    2014-04-01

    Manufacturing systems, in which, several parts are processed through machining workstations and later assembled to form final products, is common. Though scheduling of such problems are solved using heuristics, available solution approaches can provide solution for only moderate sized problems due to large computation time required. In this work, scheduling approach is developed for such flow-shop manufacturing system having machining workstations followed by assembly workstations. The initial schedule is generated using Disjunctive method and genetic algorithm (GA) is applied further for generating schedule for large sized problems. GA is found to give near optimal solution based on the deviation of makespan from lower bound. The lower bound of makespan of such problem is estimated and percent deviation of makespan from lower bounds is used as a performance measure to evaluate the schedules. Computational experiments are conducted on problems developed using fractional factorial orthogonal array, varying the number of parts per product, number of products, and number of workstations (ranging upto 1,520 number of operations). A statistical analysis indicated the significance of all the three factors considered. It is concluded that GA method can obtain optimal makespan.

  5. Discrete Bat Algorithm for Optimal Problem of Permutation Flow Shop Scheduling

    PubMed Central

    Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

    2014-01-01

    A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem. PMID:25243220

  6. Discrete bat algorithm for optimal problem of permutation flow shop scheduling.

    PubMed

    Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

    2014-01-01

    A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem.

  7. Application of decentralized cooperative problem solving in dynamic flexible scheduling

    NASA Astrophysics Data System (ADS)

    Guan, Zai-Lin; Lei, Ming; Wu, Bo; Wu, Ya; Yang, Shuzi

    1995-08-01

    The object of this study is to discuss an intelligent solution to the problem of task-allocation in shop floor scheduling. For this purpose, the technique of distributed artificial intelligence (DAI) is applied. Intelligent agents (IAs) are used to realize decentralized cooperation, and negotiation is realized by using message passing based on the contract net model. Multiple agents, such as manager agents, workcell agents, and workstation agents, make game-like decisions based on multiple criteria evaluations. This procedure of decentralized cooperative problem solving makes local scheduling possible. And by integrating such multiple local schedules, dynamic flexible scheduling for the whole shop floor production can be realized.

  8. The Compound Atwood Machine Problem

    ERIC Educational Resources Information Center

    Coelho, R. Lopes

    2017-01-01

    The present paper accounts for progress in physics teaching in the sense that a problem, which has been closed to students for being too difficult, is gained for the high school curriculum. This problem is the compound Atwood machine with three bodies. Its introduction into high school classes is based on a recent study on the weighing of an…

  9. Multiagent scheduling method with earliness and tardiness objectives in flexible job shops.

    PubMed

    Wu, Zuobao; Weng, Michael X

    2005-04-01

    Flexible job-shop scheduling problems are an important extension of the classical job-shop scheduling problems and present additional complexity. Such problems are mainly due to the existence of a considerable amount of overlapping capacities with modern machines. Classical scheduling methods are generally incapable of addressing such capacity overlapping. We propose a multiagent scheduling method with job earliness and tardiness objectives in a flexible job-shop environment. The earliness and tardiness objectives are consistent with the just-in-time production philosophy which has attracted significant attention in both industry and academic community. A new job-routing and sequencing mechanism is proposed. In this mechanism, two kinds of jobs are defined to distinguish jobs with one operation left from jobs with more than one operation left. Different criteria are proposed to route these two kinds of jobs. Job sequencing enables to hold a job that may be completed too early. Two heuristic algorithms for job sequencing are developed to deal with these two kinds of jobs. The computational experiments show that the proposed multiagent scheduling method significantly outperforms the existing scheduling methods in the literature. In addition, the proposed method is quite fast. In fact, the simulation time to find a complete schedule with over 2000 jobs on ten machines is less than 1.5 min.

  10. Car painting process scheduling with harmony search algorithm

    NASA Astrophysics Data System (ADS)

    Syahputra, M. F.; Maiyasya, A.; Purnamawati, S.; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

    2018-02-01

    Automotive painting program in the process of painting the car body by using robot power, making efficiency in the production system. Production system will be more efficient if pay attention to scheduling of car order which will be done by considering painting body shape of car. Flow shop scheduling is a scheduling model in which the job-job to be processed entirely flows in the same product direction / path. Scheduling problems often arise if there are n jobs to be processed on the machine, which must be specified which must be done first and how to allocate jobs on the machine to obtain a scheduled production process. Harmony Search Algorithm is a metaheuristic optimization algorithm based on music. The algorithm is inspired by observations that lead to music in search of perfect harmony. This musical harmony is in line to find optimal in the optimization process. Based on the tests that have been done, obtained the optimal car sequence with minimum makespan value.

  11. Producing Satisfactory Solutions to Scheduling Problems: An Iterative Constraint Relaxation Approach

    NASA Technical Reports Server (NTRS)

    Chien, S.; Gratch, J.

    1994-01-01

    One drawback to using constraint-propagation in planning and scheduling systems is that when a problem has an unsatisfiable set of constraints such algorithms typically only show that no solution exists. While, technically correct, in practical situations, it is desirable in these cases to produce a satisficing solution that satisfies the most important constraints (typically defined in terms of maximizing a utility function). This paper describes an iterative constraint relaxation approach in which the scheduler uses heuristics to progressively relax problem constraints until the problem becomes satisfiable. We present empirical results of applying these techniques to the problem of scheduling spacecraft communications for JPL/NASA antenna resources.

  12. Solving a real-world problem using an evolving heuristically driven schedule builder.

    PubMed

    Hart, E; Ross, P; Nelson, J

    1998-01-01

    This work addresses the real-life scheduling problem of a Scottish company that must produce daily schedules for the catching and transportation of large numbers of live chickens. The problem is complex and highly constrained. We show that it can be successfully solved by division into two subproblems and solving each using a separate genetic algorithm (GA). We address the problem of whether this produces locally optimal solutions and how to overcome this. We extend the traditional approach of evolving a "permutation + schedule builder" by concentrating on evolving the schedule builder itself. This results in a unique schedule builder being built for each daily scheduling problem, each individually tailored to deal with the particular features of that problem. This results in a robust, fast, and flexible system that can cope with most of the circumstances imaginable at the factory. We also compare the performance of a GA approach to several other evolutionary methods and show that population-based methods are superior to both hill-climbing and simulated annealing in the quality of solutions produced. Population-based methods also have the distinct advantage of producing multiple, equally fit solutions, which is of particular importance when considering the practical aspects of the problem.

  13. An integer batch scheduling model considering learning, forgetting, and deterioration effects for a single machine to minimize total inventory holding cost

    NASA Astrophysics Data System (ADS)

    Yusriski, R.; Sukoyo; Samadhi, T. M. A. A.; Halim, A. H.

    2018-03-01

    This research deals with a single machine batch scheduling model considering the influenced of learning, forgetting, and machine deterioration effects. The objective of the model is to minimize total inventory holding cost, and the decision variables are the number of batches (N), batch sizes (Q[i], i = 1, 2, .., N) and the sequence of processing the resulting batches. The parts to be processed are received at the right time and the right quantities, and all completed parts must be delivered at a common due date. We propose a heuristic procedure based on the Lagrange method to solve the problem. The effectiveness of the procedure is evaluated by comparing the resulting solution to the optimal solution obtained from the enumeration procedure using the integer composition technique and shows that the average effectiveness is 94%.

  14. Optimal pre-scheduling of problem remappings

    NASA Technical Reports Server (NTRS)

    Nicol, David M.; Saltz, Joel H.

    1987-01-01

    A large class of scientific computational problems can be characterized as a sequence of steps where a significant amount of computation occurs each step, but the work performed at each step is not necessarily identical. Two good examples of this type of computation are: (1) regridding methods which change the problem discretization during the course of the computation, and (2) methods for solving sparse triangular systems of linear equations. Recent work has investigated a means of mapping such computations onto parallel processors; the method defines a family of static mappings with differing degrees of importance placed on the conflicting goals of good load balance and low communication/synchronization overhead. The performance tradeoffs are controllable by adjusting the parameters of the mapping method. To achieve good performance it may be necessary to dynamically change these parameters at run-time, but such changes can impose additional costs. If the computation's behavior can be determined prior to its execution, it can be possible to construct an optimal parameter schedule using a low-order-polynomial-time dynamic programming algorithm. Since the latter can be expensive, the performance is studied of the effect of a linear-time scheduling heuristic on one of the model problems, and it is shown to be effective and nearly optimal.

  15. Algorithms for Scheduling and Network Problems

    DTIC Science & Technology

    1991-09-01

    time. We already know, by Lemma 2.2.1, that WOPT = O(log( mpU )), so if we could solve this integer program optimally we would be done. However, the...Folydirat, 15:177-191, 1982. [6] I.S. Belov and Ya. N. Stolin. An algorithm in a single path operations scheduling problem. In Mathematical Economics and

  16. Spike: Artificial intelligence scheduling for Hubble space telescope

    NASA Technical Reports Server (NTRS)

    Johnston, Mark; Miller, Glenn; Sponsler, Jeff; Vick, Shon; Jackson, Robert

    1990-01-01

    Efficient utilization of spacecraft resources is essential, but the accompanying scheduling problems are often computationally intractable and are difficult to approximate because of the presence of numerous interacting constraints. Artificial intelligence techniques were applied to the scheduling of the NASA/ESA Hubble Space Telescope (HST). This presents a particularly challenging problem since a yearlong observing program can contain some tens of thousands of exposures which are subject to a large number of scientific, operational, spacecraft, and environmental constraints. New techniques were developed for machine reasoning about scheduling constraints and goals, especially in cases where uncertainty is an important scheduling consideration and where resolving conflicts among conflicting preferences is essential. These technique were utilized in a set of workstation based scheduling tools (Spike) for HST. Graphical displays of activities, constraints, and schedules are an important feature of the system. High level scheduling strategies using both rule based and neural network approaches were developed. While the specific constraints implemented are those most relevant to HST, the framework developed is far more general and could easily handle other kinds of scheduling problems. The concept and implementation of the Spike system are described along with some experiments in adapting Spike to other spacecraft scheduling domains.

  17. Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach

    NASA Technical Reports Server (NTRS)

    Globus, Al; Crawford, James; Lohn, Jason; Morris, Robert; Clancy, Daniel (Technical Monitor)

    2002-01-01

    We describe work in progress concerning multi-instrument, multi-satellite scheduling. Most, although not all, Earth observing instruments currently in orbit are unique. In the relatively near future, however, we expect to see fleets of Earth observing spacecraft, many carrying nearly identical instruments. This presents a substantially new scheduling challenge. Inspired by successful commercial applications of evolutionary algorithms in scheduling domains, this paper presents work in progress regarding the use of evolutionary algorithms to solve a set of Earth observing related model problems. Both the model problems and the software are described. Since the larger problems will require substantial computation and evolutionary algorithms are embarrassingly parallel, we discuss our parallelization techniques using dedicated and cycle-scavenged workstations.

  18. Genetic algorithm to solve the problems of lectures and practicums scheduling

    NASA Astrophysics Data System (ADS)

    Syahputra, M. F.; Apriani, R.; Sawaluddin; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

    2018-02-01

    Generally, the scheduling process is done manually. However, this method has a low accuracy level, along with possibilities that a scheduled process collides with another scheduled process. When doing theory class and practicum timetable scheduling process, there are numerous problems, such as lecturer teaching schedule collision, schedule collision with another schedule, practicum lesson schedules that collides with theory class, and the number of classrooms available. In this research, genetic algorithm is implemented to perform theory class and practicum timetable scheduling process. The algorithm will be used to process the data containing lists of lecturers, courses, and class rooms, obtained from information technology department at University of Sumatera Utara. The result of scheduling process using genetic algorithm is the most optimal timetable that conforms to available time slots, class rooms, courses, and lecturer schedules.

  19. Efficiently Scheduling Multi-core Guest Virtual Machines on Multi-core Hosts in Network Simulation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Yoginath, Srikanth B; Perumalla, Kalyan S

    2011-01-01

    Virtual machine (VM)-based simulation is a method used by network simulators to incorporate realistic application behaviors by executing actual VMs as high-fidelity surrogates for simulated end-hosts. A critical requirement in such a method is the simulation time-ordered scheduling and execution of the VMs. Prior approaches such as time dilation are less efficient due to the high degree of multiplexing possible when multiple multi-core VMs are simulated on multi-core host systems. We present a new simulation time-ordered scheduler to efficiently schedule multi-core VMs on multi-core real hosts, with a virtual clock realized on each virtual core. The distinguishing features of ourmore » approach are: (1) customizable granularity of the VM scheduling time unit on the simulation time axis, (2) ability to take arbitrary leaps in virtual time by VMs to maximize the utilization of host (real) cores when guest virtual cores idle, and (3) empirically determinable optimality in the tradeoff between total execution (real) time and time-ordering accuracy levels. Experiments show that it is possible to get nearly perfect time-ordered execution, with a slight cost in total run time, relative to optimized non-simulation VM schedulers. Interestingly, with our time-ordered scheduler, it is also possible to reduce the time-ordering error from over 50% of non-simulation scheduler to less than 1% realized by our scheduler, with almost the same run time efficiency as that of the highly efficient non-simulation VM schedulers.« less

  20. Automated Planning and Scheduling for Space Mission Operations

    NASA Technical Reports Server (NTRS)

    Chien, Steve; Jonsson, Ari; Knight, Russell

    2005-01-01

    Research Trends: a) Finite-capacity scheduling under more complex constraints and increased problem dimensionality (subcontracting, overtime, lot splitting, inventory, etc.) b) Integrated planning and scheduling. c) Mixed-initiative frameworks. d) Management of uncertainty (proactive and reactive). e) Autonomous agent architectures and distributed production management. e) Integration of machine learning capabilities. f) Wider scope of applications: 1) analysis of supplier/buyer protocols & tradeoffs; 2) integration of strategic & tactical decision-making; and 3) enterprise integration.

  1. Applications of dynamic scheduling technique to space related problems: Some case studies

    NASA Astrophysics Data System (ADS)

    Nakasuka, Shinichi; Ninomiya, Tetsujiro

    1994-10-01

    The paper discusses the applications of 'Dynamic Scheduling' technique, which has been invented for the scheduling of Flexible Manufacturing System, to two space related scheduling problems: operation scheduling of a future space transportation system, and resource allocation in a space system with limited resources such as space station or space shuttle.

  2. Optimization-based manufacturing scheduling with multiple resources and setup requirements

    NASA Astrophysics Data System (ADS)

    Chen, Dong; Luh, Peter B.; Thakur, Lakshman S.; Moreno, Jack, Jr.

    1998-10-01

    The increasing demand for on-time delivery and low price forces manufacturer to seek effective schedules to improve coordination of multiple resources and to reduce product internal costs associated with labor, setup and inventory. This study describes the design and implementation of a scheduling system for J. M. Product Inc. whose manufacturing is characterized by the need to simultaneously consider machines and operators while an operator may attend several operations at the same time, and the presence of machines requiring significant setup times. The scheduling problem with these characteristics are typical for many manufacturers, very difficult to be handled, and have not been adequately addressed in the literature. In this study, both machine and operators are modeled as resources with finite capacities to obtain efficient coordination between them, and an operator's time can be shared by several operations at the same time to make full use of the operator. Setups are explicitly modeled following our previous work, with additional penalties on excessive setups to reduce setup costs and avoid possible scraps. An integer formulation with a separable structure is developed to maximize on-time delivery of products, low inventory and small number of setups. Within the Lagrangian relaxation framework, the problem is decomposed into individual subproblems that are effectively solved by using dynamic programming with additional penalties embedded in state transitions. Heuristics is then developed to obtain a feasible schedule following on our previous work with new mechanism to satisfy operator capacity constraints. The method has been implemented using the object-oriented programming language C++ with a user-friendly interface, and numerical testing shows that the method generates high quality schedules in a timely fashion. Through simultaneous consideration of machines and operators, machines and operators are well coordinated to facilitate the smooth flow of

  3. Machine Learning and Inverse Problem in Geodynamics

    NASA Astrophysics Data System (ADS)

    Shahnas, M. H.; Yuen, D. A.; Pysklywec, R.

    2017-12-01

    During the past few decades numerical modeling and traditional HPC have been widely deployed in many diverse fields for problem solutions. However, in recent years the rapid emergence of machine learning (ML), a subfield of the artificial intelligence (AI), in many fields of sciences, engineering, and finance seems to mark a turning point in the replacement of traditional modeling procedures with artificial intelligence-based techniques. The study of the circulation in the interior of Earth relies on the study of high pressure mineral physics, geochemistry, and petrology where the number of the mantle parameters is large and the thermoelastic parameters are highly pressure- and temperature-dependent. More complexity arises from the fact that many of these parameters that are incorporated in the numerical models as input parameters are not yet well established. In such complex systems the application of machine learning algorithms can play a valuable role. Our focus in this study is the application of supervised machine learning (SML) algorithms in predicting mantle properties with the emphasis on SML techniques in solving the inverse problem. As a sample problem we focus on the spin transition in ferropericlase and perovskite that may cause slab and plume stagnation at mid-mantle depths. The degree of the stagnation depends on the degree of negative density anomaly at the spin transition zone. The training and testing samples for the machine learning models are produced by the numerical convection models with known magnitudes of density anomaly (as the class labels of the samples). The volume fractions of the stagnated slabs and plumes which can be considered as measures for the degree of stagnation are assigned as sample features. The machine learning models can determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at mid-mantle depths. Employing support vector machine (SVM) algorithms we show that SML techniques

  4. Discrete Optimization Model for Vehicle Routing Problem with Scheduling Side Cosntraints

    NASA Astrophysics Data System (ADS)

    Juliandri, Dedy; Mawengkang, Herman; Bu'ulolo, F.

    2018-01-01

    Vehicle Routing Problem (VRP) is an important element of many logistic systems which involve routing and scheduling of vehicles from a depot to a set of customers node. This is a hard combinatorial optimization problem with the objective to find an optimal set of routes used by a fleet of vehicles to serve the demands a set of customers It is required that these vehicles return to the depot after serving customers’ demand. The problem incorporates time windows, fleet and driver scheduling, pick-up and delivery in the planning horizon. The goal is to determine the scheduling of fleet and driver and routing policies of the vehicles. The objective is to minimize the overall costs of all routes over the planning horizon. We model the problem as a linear mixed integer program. We develop a combination of heuristics and exact method for solving the model.

  5. Solving cyclical nurse scheduling problem using preemptive goal programming

    NASA Astrophysics Data System (ADS)

    Sundari, V. E.; Mardiyati, S.

    2017-07-01

    Nurse scheduling system in a hospital is being modeled as a preemptive goal programming problem that is solved by using LINGO software with the objective function to minimize deviation variable at each goal. The scheduling is done cyclically, so every nurse is treated fairly since they have the same work shift portion with the other nurses. By paying attention to the hospital's rules regarding nursing work shift cyclically, it can be obtained that numbers of nurse needed in every ward are 18 nurses and the numbers of scheduling periods are 18 periods where every period consists of 21 days.

  6. A New Lagrangian Relaxation Method Considering Previous Hour Scheduling for Unit Commitment Problem

    NASA Astrophysics Data System (ADS)

    Khorasani, H.; Rashidinejad, M.; Purakbari-Kasmaie, M.; Abdollahi, A.

    2009-08-01

    Generation scheduling is a crucial challenge in power systems especially under new environment of liberalization of electricity industry. A new Lagrangian relaxation method for unit commitment (UC) has been presented for solving generation scheduling problem. This paper focuses on the economical aspect of UC problem, while the previous hour scheduling as a very important issue is studied. In this paper generation scheduling of present hour has been conducted by considering the previous hour scheduling. The impacts of hot/cold start-up cost have been taken in to account in this paper. Case studies and numerical analysis presents significant outcomes while it demonstrates the effectiveness of the proposed method.

  7. Better approximation guarantees for job-shop scheduling

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Goldberg, L.A.; Paterson, M.; Srinivasan, A.

    1997-06-01

    Job-shop scheduling is a classical NP-hard problem. Shmoys, Stein & Wein presented the first polynomial-time approximation algorithm for this problem that has a good (polylogarithmic) approximation guarantee. We improve the approximation guarantee of their work, and present further improvements for some important NP-hard special cases of this problem (e.g., in the preemptive case where machines can suspend work on operations and later resume). We also present NC algorithms with improved approximation guarantees for some NP-hard special cases.

  8. Research on schedulers for astronomical observatories

    NASA Astrophysics Data System (ADS)

    Colome, Josep; Colomer, Pau; Guàrdia, Josep; Ribas, Ignasi; Campreciós, Jordi; Coiffard, Thierry; Gesa, Lluis; Martínez, Francesc; Rodler, Florian

    2012-09-01

    The main task of a scheduler applied to astronomical observatories is the time optimization of the facility and the maximization of the scientific return. Scheduling of astronomical observations is an example of the classical task allocation problem known as the job-shop problem (JSP), where N ideal tasks are assigned to M identical resources, while minimizing the total execution time. A problem of higher complexity, called the Flexible-JSP (FJSP), arises when the tasks can be executed by different resources, i.e. by different telescopes, and it focuses on determining a routing policy (i.e., which machine to assign for each operation) other than the traditional scheduling decisions (i.e., to determine the starting time of each operation). In most cases there is no single best approach to solve the planning system and, therefore, various mathematical algorithms (Genetic Algorithms, Ant Colony Optimization algorithms, Multi-Objective Evolutionary algorithms, etc.) are usually considered to adapt the application to the system configuration and task execution constraints. The scheduling time-cycle is also an important ingredient to determine the best approach. A shortterm scheduler, for instance, has to find a good solution with the minimum computation time, providing the system with the capability to adapt the selected task to varying execution constraints (i.e., environment conditions). We present in this contribution an analysis of the task allocation problem and the solutions currently in use at different astronomical facilities. We also describe the schedulers for three different projects (CTA, CARMENES and TJO) where the conclusions of this analysis are applied to develop a suitable routine.

  9. Stochastic scheduling on a repairable manufacturing system

    NASA Astrophysics Data System (ADS)

    Li, Wei; Cao, Jinhua

    1995-08-01

    In this paper, we consider some stochastic scheduling problems with a set of stochastic jobs on a manufacturing system with a single machine that is subject to multiple breakdowns and repairs. When the machine processing a job fails, the job processing must restart some time later when the machine is repaired. For this typical manufacturing system, we find the optimal policies that minimize the following objective functions: (1) the weighed sum of the completion times; (2) the weighed number of late jobs having constant due dates; (3) the weighted number of late jobs having random due dates exponentially distributed, which generalize some previous results.

  10. Heuristic for Critical Machine Based a Lot Streaming for Two-Stage Hybrid Production Environment

    NASA Astrophysics Data System (ADS)

    Vivek, P.; Saravanan, R.; Chandrasekaran, M.; Pugazhenthi, R.

    2017-03-01

    Lot streaming in Hybrid flowshop [HFS] is encountered in many real world problems. This paper deals with a heuristic approach for Lot streaming based on critical machine consideration for a two stage Hybrid Flowshop. The first stage has two identical parallel machines and the second stage has only one machine. In the second stage machine is considered as a critical by valid reasons these kind of problems is known as NP hard. A mathematical model developed for the selected problem. The simulation modelling and analysis were carried out in Extend V6 software. The heuristic developed for obtaining optimal lot streaming schedule. The eleven cases of lot streaming were considered. The proposed heuristic was verified and validated by real time simulation experiments. All possible lot streaming strategies and possible sequence under each lot streaming strategy were simulated and examined. The heuristic consistently yielded optimal schedule consistently in all eleven cases. The identification procedure for select best lot streaming strategy was suggested.

  11. Active Solution Space and Search on Job-shop Scheduling Problem

    NASA Astrophysics Data System (ADS)

    Watanabe, Masato; Ida, Kenichi; Gen, Mitsuo

    In this paper we propose a new searching method of Genetic Algorithm for Job-shop scheduling problem (JSP). The coding method that represent job number in order to decide a priority to arrange a job to Gannt Chart (called the ordinal representation with a priority) in JSP, an active schedule is created by using left shift. We define an active solution at first. It is solution which can create an active schedule without using left shift, and set of its defined an active solution space. Next, we propose an algorithm named Genetic Algorithm with active solution space search (GA-asol) which can create an active solution while solution is evaluated, in order to search the active solution space effectively. We applied it for some benchmark problems to compare with other method. The experimental results show good performance.

  12. Inverse Problems in Geodynamics Using Machine Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Shahnas, M. H.; Yuen, D. A.; Pysklywec, R. N.

    2018-01-01

    During the past few decades numerical studies have been widely employed to explore the style of circulation and mixing in the mantle of Earth and other planets. However, in geodynamical studies there are many properties from mineral physics, geochemistry, and petrology in these numerical models. Machine learning, as a computational statistic-related technique and a subfield of artificial intelligence, has rapidly emerged recently in many fields of sciences and engineering. We focus here on the application of supervised machine learning (SML) algorithms in predictions of mantle flow processes. Specifically, we emphasize on estimating mantle properties by employing machine learning techniques in solving an inverse problem. Using snapshots of numerical convection models as training samples, we enable machine learning models to determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at midmantle depths. Employing support vector machine algorithms, we show that SML techniques can successfully predict the magnitude of mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex geodynamic problems in mantle dynamics by employing deep learning algorithms for putting constraints on properties such as viscosity, elastic parameters, and the nature of thermal and chemical anomalies.

  13. Job Shop Scheduling Focusing on Role of Buffer

    NASA Astrophysics Data System (ADS)

    Hino, Rei; Kusumi, Tetsuya; Yoo, Jae-Kyu; Shimizu, Yoshiaki

    A scheduling problem is formulated in order to consistently manage each manufacturing resource, including machine tools, assembly robots, AGV, storehouses, material shelves, and so on. The manufacturing resources are classified into three types: producer, location, and mover. This paper focuses especially on the role of the buffer, and the differences among these types are analyzed. A unified scheduling formulation is derived from the analytical results based on the resource’s roles. Scheduling procedures based on dispatching rules are also proposed in order to numerically evaluate job shop-type production having finite buffer capacity. The influences of the capacity of bottle-necked production devices and the buffer on productivity are discussed.

  14. Multi-trip vehicle routing and scheduling problem with time window in real life

    NASA Astrophysics Data System (ADS)

    Sze, San-Nah; Chiew, Kang-Leng; Sze, Jeeu-Fong

    2012-09-01

    This paper studies a manpower scheduling problem with multiple maintenance operations and vehicle routing considerations. Service teams located at a common service centre are required to travel to different customer sites. All customers must be served within given time window, which are known in advance. The scheduling process must take into consideration complex constraints such as a meal break during the team's shift, multiple travelling trips, synchronisation of service teams and working shifts. The main objective of this study is to develop a heuristic that can generate high quality solution in short time for large problem instances. A Two-stage Scheduling Heuristic is developed for different variants of the problem. Empirical results show that the proposed solution performs effectively and efficiently. In addition, our proposed approximation algorithm is very flexible and can be easily adapted to different scheduling environments and operational requirements.

  15. Extended precedence preservative crossover for job shop scheduling problems

    NASA Astrophysics Data System (ADS)

    Ong, Chung Sin; Moin, Noor Hasnah; Omar, Mohd

    2013-04-01

    Job shop scheduling problems (JSSP) is one of difficult combinatorial scheduling problems. A wide range of genetic algorithms based on the two parents crossover have been applied to solve the problem but multi parents (more than two parents) crossover in solving the JSSP is still lacking. This paper proposes the extended precedence preservative crossover (EPPX) which uses multi parents for recombination in the genetic algorithms. EPPX is a variation of the precedence preservative crossover (PPX) which is one of the crossovers that perform well to find the solutions for the JSSP. EPPX is based on a vector to determine the gene selected in recombination for the next generation. Legalization of children (offspring) can be eliminated due to the JSSP representation encoded by using permutation with repetition that guarantees the feasibility of chromosomes. The simulations are performed on a set of benchmarks from the literatures and the results are compared to ensure the sustainability of multi parents recombination in solving the JSSP.

  16. Scheduling job shop - A case study

    NASA Astrophysics Data System (ADS)

    Abas, M.; Abbas, A.; Khan, W. A.

    2016-08-01

    The scheduling in job shop is important for efficient utilization of machines in the manufacturing industry. There are number of algorithms available for scheduling of jobs which depend on machines tools, indirect consumables and jobs which are to be processed. In this paper a case study is presented for scheduling of jobs when parts are treated on available machines. Through time and motion study setup time and operation time are measured as total processing time for variety of products having different manufacturing processes. Based on due dates different level of priority are assigned to the jobs and the jobs are scheduled on the basis of priority. In view of the measured processing time, the times for processing of some new jobs are estimated and for efficient utilization of the machines available an algorithm is proposed and validated.

  17. A coherent Ising machine for 2000-node optimization problems

    NASA Astrophysics Data System (ADS)

    Inagaki, Takahiro; Haribara, Yoshitaka; Igarashi, Koji; Sonobe, Tomohiro; Tamate, Shuhei; Honjo, Toshimori; Marandi, Alireza; McMahon, Peter L.; Umeki, Takeshi; Enbutsu, Koji; Tadanaga, Osamu; Takenouchi, Hirokazu; Aihara, Kazuyuki; Kawarabayashi, Ken-ichi; Inoue, Kyo; Utsunomiya, Shoko; Takesue, Hiroki

    2016-11-01

    The analysis and optimization of complex systems can be reduced to mathematical problems collectively known as combinatorial optimization. Many such problems can be mapped onto ground-state search problems of the Ising model, and various artificial spin systems are now emerging as promising approaches. However, physical Ising machines have suffered from limited numbers of spin-spin couplings because of implementations based on localized spins, resulting in severe scalability problems. We report a 2000-spin network with all-to-all spin-spin couplings. Using a measurement and feedback scheme, we coupled time-multiplexed degenerate optical parametric oscillators to implement maximum cut problems on arbitrary graph topologies with up to 2000 nodes. Our coherent Ising machine outperformed simulated annealing in terms of accuracy and computation time for a 2000-node complete graph.

  18. Innately Split Model for Job-shop Scheduling Problem

    NASA Astrophysics Data System (ADS)

    Ikeda, Kokolo; Kobayashi, Sigenobu

    Job-shop Scheduling Problem (JSP) is one of the most difficult benchmark problems. GA approaches often fail searching the global optimum because of the deception UV-structure of JSPs. In this paper, we introduce a novel framework model of GA, Innately Split Model (ISM) which prevents UV-phenomenon, and discuss on its power particularly. Next we analyze the structure of JSPs with the help of the UV-structure hypothesys, and finally we show ISM's excellent performance on JSP.

  19. Application of TRIZ approach to machine vibration condition monitoring problems

    NASA Astrophysics Data System (ADS)

    Cempel, Czesław

    2013-12-01

    Up to now machine condition monitoring has not been seriously approached by TRIZ1TRIZ= Russian acronym for Inventive Problem Solving System, created by G. Altshuller ca 50 years ago. users, and the knowledge of TRIZ methodology has not been applied there intensively. However, there are some introductory papers of present author posted on Diagnostic Congress in Cracow (Cempel, in press [11]), and Diagnostyka Journal as well. But it seems to be further need to make such approach from different sides in order to see, if some new knowledge and technology will emerge. In doing this we need at first to define the ideal final result (IFR) of our innovation problem. As a next we need a set of parameters to describe the problems of system condition monitoring (CM) in terms of TRIZ language and set of inventive principles possible to apply, on the way to IFR. This means we should present the machine CM problem by means of contradiction and contradiction matrix. When specifying the problem parameters and inventive principles, one should use analogy and metaphorical thinking, which by definition is not exact but fuzzy, and leads sometimes to unexpected results and outcomes. The paper undertakes this important problem again and brings some new insight into system and machine CM problems. This may mean for example the minimal dimensionality of TRIZ engineering parameter set for the description of machine CM problems, and the set of most useful inventive principles applied to given engineering parameter and contradictions of TRIZ.

  20. Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem.

    PubMed

    Chen, Xiaopan; Kong, Yunfeng; Dang, Lanxue; Hou, Yane; Ye, Xinyue

    2015-01-01

    As a class of hard combinatorial optimization problems, the school bus routing problem has received considerable attention in the last decades. For a multi-school system, given the bus trips for each school, the school bus scheduling problem aims at optimizing bus schedules to serve all the trips within the school time windows. In this paper, we propose two approaches for solving the bi-objective school bus scheduling problem: an exact method of mixed integer programming (MIP) and a metaheuristic method which combines simulated annealing with local search. We develop MIP formulations for homogenous and heterogeneous fleet problems respectively and solve the models by MIP solver CPLEX. The bus type-based formulation for heterogeneous fleet problem reduces the model complexity in terms of the number of decision variables and constraints. The metaheuristic method is a two-stage framework for minimizing the number of buses to be used as well as the total travel distance of buses. We evaluate the proposed MIP and the metaheuristic method on two benchmark datasets, showing that on both instances, our metaheuristic method significantly outperforms the respective state-of-the-art methods.

  1. Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem

    PubMed Central

    Chen, Xiaopan; Kong, Yunfeng; Dang, Lanxue; Hou, Yane; Ye, Xinyue

    2015-01-01

    As a class of hard combinatorial optimization problems, the school bus routing problem has received considerable attention in the last decades. For a multi-school system, given the bus trips for each school, the school bus scheduling problem aims at optimizing bus schedules to serve all the trips within the school time windows. In this paper, we propose two approaches for solving the bi-objective school bus scheduling problem: an exact method of mixed integer programming (MIP) and a metaheuristic method which combines simulated annealing with local search. We develop MIP formulations for homogenous and heterogeneous fleet problems respectively and solve the models by MIP solver CPLEX. The bus type-based formulation for heterogeneous fleet problem reduces the model complexity in terms of the number of decision variables and constraints. The metaheuristic method is a two-stage framework for minimizing the number of buses to be used as well as the total travel distance of buses. We evaluate the proposed MIP and the metaheuristic method on two benchmark datasets, showing that on both instances, our metaheuristic method significantly outperforms the respective state-of-the-art methods. PMID:26176764

  2. Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)

    NASA Astrophysics Data System (ADS)

    Alizadeh Sahzabi, Vahid; Karimi, Iman; Alizadeh Sahzabi, Navid; Mamaani Barnaghi, Peiman

    2012-01-01

    In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.

  3. Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm

    NASA Astrophysics Data System (ADS)

    Piroozfard, Hamed; Wong, Kuan Yew

    2015-05-01

    The efforts of finding optimal schedules for the job shop scheduling problems are highly important for many real-world industrial applications. In this paper, a multi-objective based job shop scheduling problem by simultaneously minimizing makespan and tardiness is taken into account. The problem is considered to be more complex due to the multiple business criteria that must be satisfied. To solve the problem more efficiently and to obtain a set of non-dominated solutions, a meta-heuristic based non-dominated sorting genetic algorithm is presented. In addition, task based representation is used for solution encoding, and tournament selection that is based on rank and crowding distance is applied for offspring selection. Swapping and insertion mutations are employed to increase diversity of population and to perform intensive search. To evaluate the modified non-dominated sorting genetic algorithm, a set of modified benchmarking job shop problems obtained from the OR-Library is used, and the results are considered based on the number of non-dominated solutions and quality of schedules obtained by the algorithm.

  4. An efficient annealing in Boltzmann machine in Hopfield neural network

    NASA Astrophysics Data System (ADS)

    Kin, Teoh Yeong; Hasan, Suzanawati Abu; Bulot, Norhisam; Ismail, Mohammad Hafiz

    2012-09-01

    This paper proposes and implements Boltzmann machine in Hopfield neural network doing logic programming based on the energy minimization system. The temperature scheduling in Boltzmann machine enhancing the performance of doing logic programming in Hopfield neural network. The finest temperature is determined by observing the ratio of global solution and final hamming distance using computer simulations. The study shows that Boltzmann Machine model is more stable and competent in term of representing and solving difficult combinatory problems.

  5. An FMS Dynamic Production Scheduling Algorithm Considering Cutting Tool Failure and Cutting Tool Life

    NASA Astrophysics Data System (ADS)

    Setiawan, A.; Wangsaputra, R.; Martawirya, Y. Y.; Halim, A. H.

    2016-02-01

    This paper deals with Flexible Manufacturing System (FMS) production rescheduling due to unavailability of cutting tools caused either of cutting tool failure or life time limit. The FMS consists of parallel identical machines integrated with an automatic material handling system and it runs fully automatically. Each machine has a same cutting tool configuration that consists of different geometrical cutting tool types on each tool magazine. The job usually takes two stages. Each stage has sequential operations allocated to machines considering the cutting tool life. In the real situation, the cutting tool can fail before the cutting tool life is reached. The objective in this paper is to develop a dynamic scheduling algorithm when a cutting tool is broken during unmanned and a rescheduling needed. The algorithm consists of four steps. The first step is generating initial schedule, the second step is determination the cutting tool failure time, the third step is determination of system status at cutting tool failure time and the fourth step is the rescheduling for unfinished jobs. The approaches to solve the problem are complete-reactive scheduling and robust-proactive scheduling. The new schedules result differences starting time and completion time of each operations from the initial schedule.

  6. Wave scheduling - Decentralized scheduling of task forces in multicomputers

    NASA Technical Reports Server (NTRS)

    Van Tilborg, A. M.; Wittie, L. D.

    1984-01-01

    Decentralized operating systems that control large multicomputers need techniques to schedule competing parallel programs called task forces. Wave scheduling is a probabilistic technique that uses a hierarchical distributed virtual machine to schedule task forces by recursively subdividing and issuing wavefront-like commands to processing elements capable of executing individual tasks. Wave scheduling is highly resistant to processing element failures because it uses many distributed schedulers that dynamically assign scheduling responsibilities among themselves. The scheduling technique is trivially extensible as more processing elements join the host multicomputer. A simple model of scheduling cost is used by every scheduler node to distribute scheduling activity and minimize wasted processing capacity by using perceived workload to vary decentralized scheduling rules. At low to moderate levels of network activity, wave scheduling is only slightly less efficient than a central scheduler in its ability to direct processing elements to accomplish useful work.

  7. Travel with CPAP machines: how frequent and what are the problems?

    PubMed

    Bodington, Richard; Johnson, Owen; Carveth-Johnson, Pippa; Faruqi, Shoaib

    2018-01-01

    Obstructive sleep apnoea syndrome is a common condition for which continuous positive airways pressure (CPAP) is the standard treatment. The condition affects a population of which a substantial proportion will be travelling. We use a questionnaire survey of CPAP users to gain understanding regarding the behaviours, attitudes and problems surrounding travel with CPAP machines during travel and while abroad. All CPAP patients on our database at a UK district general hospital reviewed over a period of 4 years were sent a postal questionnaire. A response rate of 53% was achieved giving data on 588 trips. In the last 2 years, 63.7% of respondents had travelled; reasons for not travelling were CPAP related in only five cases. Travellers took their CPAP machines on 81% of trips. A similar proportion of patients took their CPAP machines regardless of the mode of travel, destination or length of holiday. Problems with checking in the CPAP machine were encountered in 4% of trips, all as part of air travel. Just over a third of patients faced problems either with the power cord, adapter or transport of the CPAP machine. Of those taking overnight flights, half did not sleep and none used their CPAP machines in flight. CPAP usage while away did not differ to usage at home. This is the first report to describe in some detail CPAP machine use and associated problems in travel and while away. The data may aid the targeting of brief interventions in CPAP clinics as well as helping to standardize the process of check-in in order to help travellers with CPAP machines. © International Society of Travel Medicine, 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  8. Artificial immune algorithm for multi-depot vehicle scheduling problems

    NASA Astrophysics Data System (ADS)

    Wu, Zhongyi; Wang, Donggen; Xia, Linyuan; Chen, Xiaoling

    2008-10-01

    In the fast-developing logistics and supply chain management fields, one of the key problems in the decision support system is that how to arrange, for a lot of customers and suppliers, the supplier-to-customer assignment and produce a detailed supply schedule under a set of constraints. Solutions to the multi-depot vehicle scheduling problems (MDVRP) help in solving this problem in case of transportation applications. The objective of the MDVSP is to minimize the total distance covered by all vehicles, which can be considered as delivery costs or time consumption. The MDVSP is one of nondeterministic polynomial-time hard (NP-hard) problem which cannot be solved to optimality within polynomial bounded computational time. Many different approaches have been developed to tackle MDVSP, such as exact algorithm (EA), one-stage approach (OSA), two-phase heuristic method (TPHM), tabu search algorithm (TSA), genetic algorithm (GA) and hierarchical multiplex structure (HIMS). Most of the methods mentioned above are time consuming and have high risk to result in local optimum. In this paper, a new search algorithm is proposed to solve MDVSP based on Artificial Immune Systems (AIS), which are inspirited by vertebrate immune systems. The proposed AIS algorithm is tested with 30 customers and 6 vehicles located in 3 depots. Experimental results show that the artificial immune system algorithm is an effective and efficient method for solving MDVSP problems.

  9. A new Nawaz-Enscore-Ham-based heuristic for permutation flow-shop problems with bicriteria of makespan and machine idle time

    NASA Astrophysics Data System (ADS)

    Liu, Weibo; Jin, Yan; Price, Mark

    2016-10-01

    A new heuristic based on the Nawaz-Enscore-Ham algorithm is proposed in this article for solving a permutation flow-shop scheduling problem. A new priority rule is proposed by accounting for the average, mean absolute deviation, skewness and kurtosis, in order to fully describe the distribution style of processing times. A new tie-breaking rule is also introduced for achieving effective job insertion with the objective of minimizing both makespan and machine idle time. Statistical tests illustrate better solution quality of the proposed algorithm compared to existing benchmark heuristics.

  10. Solving Open Job-Shop Scheduling Problems by SAT Encoding

    NASA Astrophysics Data System (ADS)

    Koshimura, Miyuki; Nabeshima, Hidetomo; Fujita, Hiroshi; Hasegawa, Ryuzo

    This paper tries to solve open Job-Shop Scheduling Problems (JSSP) by translating them into Boolean Satisfiability Testing Problems (SAT). The encoding method is essentially the same as the one proposed by Crawford and Baker. The open problems are ABZ8, ABZ9, YN1, YN2, YN3, and YN4. We proved that the best known upper bounds 678 of ABZ9 and 884 of YN1 are indeed optimal. We also improved the upper bound of YN2 and lower bounds of ABZ8, YN2, YN3 and YN4.

  11. JIGSAW: Preference-directed, co-operative scheduling

    NASA Technical Reports Server (NTRS)

    Linden, Theodore A.; Gaw, David

    1992-01-01

    Techniques that enable humans and machines to cooperate in the solution of complex scheduling problems have evolved out of work on the daily allocation and scheduling of Tactical Air Force resources. A generalized, formal model of these applied techniques is being developed. It is called JIGSAW by analogy with the multi-agent, constructive process used when solving jigsaw puzzles. JIGSAW begins from this analogy and extends it by propagating local preferences into global statistics that dynamically influence the value and variable ordering decisions. The statistical projections also apply to abstract resources and time periods--allowing more opportunities to find a successful variable ordering by reserving abstract resources and deferring the choice of a specific resource or time period.

  12. Single product lot-sizing on unrelated parallel machines with non-decreasing processing times

    NASA Astrophysics Data System (ADS)

    Eremeev, A.; Kovalyov, M.; Kuznetsov, P.

    2018-01-01

    We consider a problem in which at least a given quantity of a single product has to be partitioned into lots, and lots have to be assigned to unrelated parallel machines for processing. In one version of the problem, the maximum machine completion time should be minimized, in another version of the problem, the sum of machine completion times is to be minimized. Machine-dependent lower and upper bounds on the lot size are given. The product is either assumed to be continuously divisible or discrete. The processing time of each machine is defined by an increasing function of the lot volume, given as an oracle. Setup times and costs are assumed to be negligibly small, and therefore, they are not considered. We derive optimal polynomial time algorithms for several special cases of the problem. An NP-hard case is shown to admit a fully polynomial time approximation scheme. An application of the problem in energy efficient processors scheduling is considered.

  13. Parallel-Batch Scheduling and Transportation Coordination with Waiting Time Constraint

    PubMed Central

    Gong, Hua; Chen, Daheng; Xu, Ke

    2014-01-01

    This paper addresses a parallel-batch scheduling problem that incorporates transportation of raw materials or semifinished products before processing with waiting time constraint. The orders located at the different suppliers are transported by some vehicles to a manufacturing facility for further processing. One vehicle can load only one order in one shipment. Each order arriving at the facility must be processed in the limited waiting time. The orders are processed in batches on a parallel-batch machine, where a batch contains several orders and the processing time of the batch is the largest processing time of the orders in it. The goal is to find a schedule to minimize the sum of the total flow time and the production cost. We prove that the general problem is NP-hard in the strong sense. We also demonstrate that the problem with equal processing times on the machine is NP-hard. Furthermore, a dynamic programming algorithm in pseudopolynomial time is provided to prove its ordinarily NP-hardness. An optimal algorithm in polynomial time is presented to solve a special case with equal processing times and equal transportation times for each order. PMID:24883385

  14. Non-Evolutionary Algorithms for Scheduling Dependent Tasks in Distributed Heterogeneous Computing Environments

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Wayne F. Boyer; Gurdeep S. Hura

    2005-09-01

    The Problem of obtaining an optimal matching and scheduling of interdependent tasks in distributed heterogeneous computing (DHC) environments is well known to be an NP-hard problem. In a DHC system, task execution time is dependent on the machine to which it is assigned and task precedence constraints are represented by a directed acyclic graph. Recent research in evolutionary techniques has shown that genetic algorithms usually obtain more efficient schedules that other known algorithms. We propose a non-evolutionary random scheduling (RS) algorithm for efficient matching and scheduling of inter-dependent tasks in a DHC system. RS is a succession of randomized taskmore » orderings and a heuristic mapping from task order to schedule. Randomized task ordering is effectively a topological sort where the outcome may be any possible task order for which the task precedent constraints are maintained. A detailed comparison to existing evolutionary techniques (GA and PSGA) shows the proposed algorithm is less complex than evolutionary techniques, computes schedules in less time, requires less memory and fewer tuning parameters. Simulation results show that the average schedules produced by RS are approximately as efficient as PSGA schedules for all cases studied and clearly more efficient than PSGA for certain cases. The standard formulation for the scheduling problem addressed in this paper is Rm|prec|Cmax.,« less

  15. Completable scheduling: An integrated approach to planning and scheduling

    NASA Technical Reports Server (NTRS)

    Gervasio, Melinda T.; Dejong, Gerald F.

    1992-01-01

    The planning problem has traditionally been treated separately from the scheduling problem. However, as more realistic domains are tackled, it becomes evident that the problem of deciding on an ordered set of tasks to achieve a set of goals cannot be treated independently of the problem of actually allocating resources to the tasks. Doing so would result in losing the robustness and flexibility needed to deal with imperfectly modeled domains. Completable scheduling is an approach which integrates the two problems by allowing an a priori planning module to defer particular planning decisions, and consequently the associated scheduling decisions, until execution time. This allows a completable scheduling system to maximize plan flexibility by allowing runtime information to be taken into consideration when making planning and scheduling decision. Furthermore, through the criteria of achievability placed on deferred decision, a completable scheduling system is able to retain much of the goal-directedness and guarantees of achievement afforded by a priori planning. The completable scheduling approach is further enhanced by the use of contingent explanation-based learning, which enables a completable scheduling system to learn general completable plans from example and improve its performance through experience. Initial experimental results show that completable scheduling outperforms classical scheduling as well as pure reactive scheduling in a simple scheduling domain.

  16. Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model

    NASA Astrophysics Data System (ADS)

    Nouri, Houssem Eddine; Belkahla Driss, Olfa; Ghédira, Khaled

    2018-03-01

    The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic multiagent model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

  17. Due-Window Assignment Scheduling with Variable Job Processing Times

    PubMed Central

    Wu, Yu-Bin

    2015-01-01

    We consider a common due-window assignment scheduling problem jobs with variable job processing times on a single machine, where the processing time of a job is a function of its position in a sequence (i.e., learning effect) or its starting time (i.e., deteriorating effect). The problem is to determine the optimal due-windows, and the processing sequence simultaneously to minimize a cost function includes earliness, tardiness, the window location, window size, and weighted number of tardy jobs. We prove that the problem can be solved in polynomial time. PMID:25918745

  18. A novel discrete PSO algorithm for solving job shop scheduling problem to minimize makespan

    NASA Astrophysics Data System (ADS)

    Rameshkumar, K.; Rajendran, C.

    2018-02-01

    In this work, a discrete version of PSO algorithm is proposed to minimize the makespan of a job-shop. A novel schedule builder has been utilized to generate active schedules. The discrete PSO is tested using well known benchmark problems available in the literature. The solution produced by the proposed algorithms is compared with best known solution published in the literature and also compared with hybrid particle swarm algorithm and variable neighborhood search PSO algorithm. The solution construction methodology adopted in this study is found to be effective in producing good quality solutions for the various benchmark job-shop scheduling problems.

  19. Data-driven advice for applying machine learning to bioinformatics problems

    PubMed Central

    Olson, Randal S.; La Cava, William; Mustahsan, Zairah; Varik, Akshay; Moore, Jason H.

    2017-01-01

    As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems. PMID:29218881

  20. Neighbourhood generation mechanism applied in simulated annealing to job shop scheduling problems

    NASA Astrophysics Data System (ADS)

    Cruz-Chávez, Marco Antonio

    2015-11-01

    This paper presents a neighbourhood generation mechanism for the job shop scheduling problems (JSSPs). In order to obtain a feasible neighbour with the generation mechanism, it is only necessary to generate a permutation of an adjacent pair of operations in a scheduling of the JSSP. If there is no slack time between the adjacent pair of operations that is permuted, then it is proven, through theory and experimentation, that the new neighbour (schedule) generated is feasible. It is demonstrated that the neighbourhood generation mechanism is very efficient and effective in a simulated annealing.

  1. A Study on Real-Time Scheduling Methods in Holonic Manufacturing Systems

    NASA Astrophysics Data System (ADS)

    Iwamura, Koji; Taimizu, Yoshitaka; Sugimura, Nobuhiro

    Recently, new architectures of manufacturing systems have been proposed to realize flexible control structures of the manufacturing systems, which can cope with the dynamic changes in the volume and the variety of the products and also the unforeseen disruptions, such as failures of manufacturing resources and interruptions by high priority jobs. They are so called as the autonomous distributed manufacturing system, the biological manufacturing system and the holonic manufacturing system. Rule-based scheduling methods were proposed and applied to the real-time production scheduling problems of the HMS (Holonic Manufacturing System) in the previous report. However, there are still remaining problems from the viewpoint of the optimization of the whole production schedules. New procedures are proposed, in the present paper, to select the production schedules, aimed at generating effective production schedules in real-time. The proposed methods enable the individual holons to select suitable machining operations to be carried out in the next time period. Coordination process among the holons is also proposed to carry out the coordination based on the effectiveness values of the individual holons.

  2. Performance of Quantum Annealers on Hard Scheduling Problems

    NASA Astrophysics Data System (ADS)

    Pokharel, Bibek; Venturelli, Davide; Rieffel, Eleanor

    Quantum annealers have been employed to attack a variety of optimization problems. We compared the performance of the current D-Wave 2X quantum annealer to that of the previous generation D-Wave Two quantum annealer on scheduling-type planning problems. Further, we compared the effect of different anneal times, embeddings of the logical problem, and different settings of the ferromagnetic coupling JF across the logical vertex-model on the performance of the D-Wave 2X quantum annealer. Our results show that at the best settings, the scaling of expected anneal time to solution for D-WAVE 2X is better than that of the DWave Two, but still inferior to that of state of the art classical solvers on these problems. We discuss the implication of our results for the design and programming of future quantum annealers. Supported by NASA Ames Research Center.

  3. A Hybrid Cellular Genetic Algorithm for Multi-objective Crew Scheduling Problem

    NASA Astrophysics Data System (ADS)

    Jolai, Fariborz; Assadipour, Ghazal

    Crew scheduling is one of the important problems of the airline industry. This problem aims to cover a number of flights by crew members, such that all the flights are covered. In a robust scheduling the assignment should be so that the total cost, delays, and unbalanced utilization are minimized. As the problem is NP-hard and the objectives are in conflict with each other, a multi-objective meta-heuristic called CellDE, which is a hybrid cellular genetic algorithm, is implemented as the optimization method. The proposed algorithm provides the decision maker with a set of non-dominated or Pareto-optimal solutions, and enables them to choose the best one according to their preferences. A set of problems of different sizes is generated and solved using the proposed algorithm. Evaluating the performance of the proposed algorithm, three metrics are suggested, and the diversity and the convergence of the achieved Pareto front are appraised. Finally a comparison is made between CellDE and PAES, another meta-heuristic algorithm. The results show the superiority of CellDE.

  4. A hybrid algorithm optimization approach for machine loading problem in flexible manufacturing system

    NASA Astrophysics Data System (ADS)

    Kumar, Vijay M.; Murthy, ANN; Chandrashekara, K.

    2012-05-01

    The production planning problem of flexible manufacturing system (FMS) concerns with decisions that have to be made before an FMS begins to produce parts according to a given production plan during an upcoming planning horizon. The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to the relevant machines are made. Such problems are not only combinatorial optimization problems, but also happen to be non-deterministic polynomial-time-hard, making it difficult to obtain satisfactory solutions using traditional optimization techniques. In this paper, an attempt has been made to address the machine loading problem with objectives of minimization of system unbalance and maximization of throughput simultaneously while satisfying the system constraints related to available machining time and tool slot designing and using a meta-hybrid heuristic technique based on genetic algorithm and particle swarm optimization. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as throughput and system utilization.

  5. Effective Iterated Greedy Algorithm for Flow-Shop Scheduling Problems with Time lags

    NASA Astrophysics Data System (ADS)

    ZHAO, Ning; YE, Song; LI, Kaidian; CHEN, Siyu

    2017-05-01

    Flow shop scheduling problem with time lags is a practical scheduling problem and attracts many studies. Permutation problem(PFSP with time lags) is concentrated but non-permutation problem(non-PFSP with time lags) seems to be neglected. With the aim to minimize the makespan and satisfy time lag constraints, efficient algorithms corresponding to PFSP and non-PFSP problems are proposed, which consist of iterated greedy algorithm for permutation(IGTLP) and iterated greedy algorithm for non-permutation (IGTLNP). The proposed algorithms are verified using well-known simple and complex instances of permutation and non-permutation problems with various time lag ranges. The permutation results indicate that the proposed IGTLP can reach near optimal solution within nearly 11% computational time of traditional GA approach. The non-permutation results indicate that the proposed IG can reach nearly same solution within less than 1% computational time compared with traditional GA approach. The proposed research combines PFSP and non-PFSP together with minimal and maximal time lag consideration, which provides an interesting viewpoint for industrial implementation.

  6. A new parallel DNA algorithm to solve the task scheduling problem based on inspired computational model.

    PubMed

    Wang, Zhaocai; Ji, Zuwen; Wang, Xiaoming; Wu, Tunhua; Huang, Wei

    2017-12-01

    As a promising approach to solve the computationally intractable problem, the method based on DNA computing is an emerging research area including mathematics, computer science and molecular biology. The task scheduling problem, as a well-known NP-complete problem, arranges n jobs to m individuals and finds the minimum execution time of last finished individual. In this paper, we use a biologically inspired computational model and describe a new parallel algorithm to solve the task scheduling problem by basic DNA molecular operations. In turn, we skillfully design flexible length DNA strands to represent elements of the allocation matrix, take appropriate biological experiment operations and get solutions of the task scheduling problem in proper length range with less than O(n 2 ) time complexity. Copyright © 2017. Published by Elsevier B.V.

  7. Discrete harmony search algorithm for scheduling and rescheduling the reprocessing problems in remanufacturing: a case study

    NASA Astrophysics Data System (ADS)

    Gao, Kaizhou; Wang, Ling; Luo, Jianping; Jiang, Hua; Sadollah, Ali; Pan, Quanke

    2018-06-01

    In this article, scheduling and rescheduling problems with increasing processing time and new job insertion are studied for reprocessing problems in the remanufacturing process. To handle the unpredictability of reprocessing time, an experience-based strategy is used. Rescheduling strategies are applied for considering the effect of increasing reprocessing time and the new subassembly insertion. To optimize the scheduling and rescheduling objective, a discrete harmony search (DHS) algorithm is proposed. To speed up the convergence rate, a local search method is designed. The DHS is applied to two real-life cases for minimizing the maximum completion time and the mean of earliness and tardiness (E/T). These two objectives are also considered together as a bi-objective problem. Computational optimization results and comparisons show that the proposed DHS is able to solve the scheduling and rescheduling problems effectively and productively. Using the proposed approach, satisfactory optimization results can be achieved for scheduling and rescheduling on a real-life shop floor.

  8. Constraint-Based Scheduling System

    NASA Technical Reports Server (NTRS)

    Zweben, Monte; Eskey, Megan; Stock, Todd; Taylor, Will; Kanefsky, Bob; Drascher, Ellen; Deale, Michael; Daun, Brian; Davis, Gene

    1995-01-01

    Report describes continuing development of software for constraint-based scheduling system implemented eventually on massively parallel computer. Based on machine learning as means of improving scheduling. Designed to learn when to change search strategy by analyzing search progress and learning general conditions under which resource bottleneck occurs.

  9. Minimizing conflicts: A heuristic repair method for constraint-satisfaction and scheduling problems

    NASA Technical Reports Server (NTRS)

    Minton, Steve; Johnston, Mark; Philips, Andrew; Laird, Phil

    1992-01-01

    This paper describes a simple heuristic approach to solving large-scale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a value-ordering heuristic, the min-conflicts heuristic, that attempts to minimize the number of constraint violations after each step. The heuristic can be used with a variety of different search strategies. We demonstrate empirically that on the n-queens problem, a technique based on this approach performs orders of magnitude better than traditional backtracking techniques. We also describe a scheduling application where the approach has been used successfully. A theoretical analysis is presented both to explain why this method works well on certain types of problems and to predict when it is likely to be most effective.

  10. Department of Defense Tri-Service Precision Machine-Tool Program. Quarterly report, February--April 1978

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Not Available

    1978-06-01

    Following a planning period during which the Lawrence Livermore Laboratory and the Department of Defense managing sponsor, the USAF Materials Laboratory, agreed on work statements, the Department of Defense Tri-Service Precision Machine-Tool Program began in February 1978. Milestones scheduled for the first quarter have been met. Tasks and manpower requirements for two basic projects, precision-machining commercialization (PMC) and a machine-tool task force (MTTF), were defined. Progress by PMC includes: (1) documentation of existing precision machine-tool technology by initiation and compilation of a bibliography containing several hundred entries: (2) identification of the problems and needs of precision turning-machine builders and ofmore » precision turning-machine users interested in developing high-precision machining capability; and (3) organization of the schedule and content of the first seminar, to be held in October 1978, which will bring together representatives from the machine-tool and optics communities to address the problems and begin the process of high-precision machining commercialization. Progress by MTTF includes: (1) planning for the organization of a team effort of approximately 60 to 80 international experts to contribute in various ways to project objectives, namely, to summarize state-of-the-art cutting-machine-tool technology and to identify areas where future R and D should prove technically and economically profitable; (2) preparation of a comprehensive plan to achieve those objectives; and (3) preliminary arrangements for a plenary session, also in October, when the task force will meet to formalize the details for implementing the plan.« less

  11. Nonlinear programming for classification problems in machine learning

    NASA Astrophysics Data System (ADS)

    Astorino, Annabella; Fuduli, Antonio; Gaudioso, Manlio

    2016-10-01

    We survey some nonlinear models for classification problems arising in machine learning. In the last years this field has become more and more relevant due to a lot of practical applications, such as text and web classification, object recognition in machine vision, gene expression profile analysis, DNA and protein analysis, medical diagnosis, customer profiling etc. Classification deals with separation of sets by means of appropriate separation surfaces, which is generally obtained by solving a numerical optimization model. While linear separability is the basis of the most popular approach to classification, the Support Vector Machine (SVM), in the recent years using nonlinear separating surfaces has received some attention. The objective of this work is to recall some of such proposals, mainly in terms of the numerical optimization models. In particular we tackle the polyhedral, ellipsoidal, spherical and conical separation approaches and, for some of them, we also consider the semisupervised versions.

  12. A short-term operating room surgery scheduling problem integrating multiple nurses roster constraints.

    PubMed

    Xiang, Wei; Yin, Jiao; Lim, Gino

    2015-02-01

    Operating room (OR) surgery scheduling determines the individual surgery's operation start time and assigns the required resources to each surgery over a schedule period, considering several constraints related to a complete surgery flow and the multiple resources involved. This task plays a decisive role in providing timely treatments for the patients while balancing hospital resource utilization. The originality of the present study is to integrate the surgery scheduling problem with real-life nurse roster constraints such as their role, specialty, qualification and availability. This article proposes a mathematical model and an ant colony optimization (ACO) approach to efficiently solve such surgery scheduling problems. A modified ACO algorithm with a two-level ant graph model is developed to solve such combinatorial optimization problems because of its computational complexity. The outer ant graph represents surgeries, while the inner graph is a dynamic resource graph. Three types of pheromones, i.e. sequence-related, surgery-related, and resource-related pheromone, fitting for a two-level model are defined. The iteration-best and feasible update strategy and local pheromone update rules are adopted to emphasize the information related to the good solution in makespan, and the balanced utilization of resources as well. The performance of the proposed ACO algorithm is then evaluated using the test cases from (1) the published literature data with complete nurse roster constraints, and 2) the real data collected from a hospital in China. The scheduling results using the proposed ACO approach are compared with the test case from both the literature and the real life hospital scheduling. Comparison results with the literature shows that the proposed ACO approach has (1) an 1.5-h reduction in end time; (2) a reduction in variation of resources' working time, i.e. 25% for ORs, 50% for nurses in shift 1 and 86% for nurses in shift 2; (3) an 0.25h reduction in

  13. Integrated resource scheduling in a distributed scheduling environment

    NASA Technical Reports Server (NTRS)

    Zoch, David; Hall, Gardiner

    1988-01-01

    The Space Station era presents a highly-complex multi-mission planning and scheduling environment exercised over a highly distributed system. In order to automate the scheduling process, customers require a mechanism for communicating their scheduling requirements to NASA. A request language that a remotely-located customer can use to specify his scheduling requirements to a NASA scheduler, thus automating the customer-scheduler interface, is described. This notation, Flexible Envelope-Request Notation (FERN), allows the user to completely specify his scheduling requirements such as resource usage, temporal constraints, and scheduling preferences and options. The FERN also contains mechanisms for representing schedule and resource availability information, which are used in the inter-scheduler inconsistency resolution process. Additionally, a scheduler is described that can accept these requests, process them, generate schedules, and return schedule and resource availability information to the requester. The Request-Oriented Scheduling Engine (ROSE) was designed to function either as an independent scheduler or as a scheduling element in a network of schedulers. When used in a network of schedulers, each ROSE communicates schedule and resource usage information to other schedulers via the FERN notation, enabling inconsistencies to be resolved between schedulers. Individual ROSE schedules are created by viewing the problem as a constraint satisfaction problem with a heuristically guided search strategy.

  14. The engine maintenance scheduling by using reliability centered maintenance method and the identification of 5S application in PT. XYZ

    NASA Astrophysics Data System (ADS)

    Sembiring, N.; Panjaitan, N.; Saragih, A. F.

    2018-02-01

    PT. XYZ is a manufacturing company that produces fresh fruit bunches (FFB) to Crude Palm Oil (CPO) and Palm Kernel Oil (PKO). PT. XYZ consists of six work stations: receipt station, sterilizing station, thressing station, pressing station, clarification station, and kernelery station. So far, the company is still implementing corrective maintenance maintenance system for production machines where the machine repair is done after damage occurs. Problems at PT. XYZ is the absence of scheduling engine maintenance in a planned manner resulting in the engine often damaged which can disrupt the smooth production. Another factor that is the problem in this research is the kernel station environment that becomes less convenient for operators such as there are machines and equipment not used in the production area, slippery, muddy, scattered fibers, incomplete use of PPE, and lack of employee discipline. The most commonly damaged machine is in the seed processing station (kernel station) which is cake breaker conveyor machine. The solution of this problem is to propose a schedule plan for maintenance of the machine by using the method of reliability centered maintenance and also the application of 5S. The result of the application of Reliability Centered maintenance method is obtained four components that must be treated scheduled (time directed), namely: for bearing component is 37 days, gearbox component is 97 days, CBC pen component is 35 days and conveyor pedal component is 32 days While after identification the application of 5S obtained the proposed corporate environmental improvement measures in accordance with the principles of 5S where unused goods will be moved from the production area, grouping goods based on their use, determining the procedure of cleaning the production area, conducting inspection in the use of PPE, and making 5S slogans.

  15. What work schedule characteristics constitute a problem to the individual? A representative study of Swedish shift workers.

    PubMed

    Åkerstedt, Torbjörn; Kecklund, Göran

    2017-03-01

    The purpose was to investigate which detailed characteristics of shift schedules that are seen as problems to those exposed. A representative national sample of non-day workers (N = 2031) in Sweden was asked whether they had each of a number of particular work schedule characteristics and, if yes, to what extent this constituted a "big problem in life". It was also inquired whether the individual's work schedules had negative consequences for fatigue, sleep and social life. The characteristic with the highest percentage reporting a big problem was "short notice (<1 month) of a new work schedule" (30.5%), <11 h off between shifts (27.8%), and split duty (>1.5 h break at mid-shift, 27.2%). Overtime (>10 h/week), night work, morning work, day/night shifts showed lower prevalences of being a "big problem". Women indicated more problems in general. Short notice was mainly related to negative social effects, while <11 h off between shifts was related to disturbed sleep, fatigue and social difficulties. It was concluded that schedules involving unpredictable working hours (short notice), short daily rest between shifts, and split duty shifts constitute big problems. The results challenge current views of what aspects of shift work need improvement, and negative social consequences seem more important than those related to health. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Nonstandard maternal work schedules during infancy: Implications for children's early behavior problems

    PubMed Central

    Daniel, Stephanie S.; Grzywacz, Joseph G.; Leerkes, Esther; Tucker, Jenna; Han, Wen-Jui

    2009-01-01

    This paper examines the associations between maternal nonstandard work schedules during infancy and children's early behavior problems, and the extent to which infant temperament may moderate these associations. Hypothesized associations were tested using data from the National Institute of Child Health and Human Development (NICHD) Study of Early Child Care (Phase I). Analyses focused on mothers who returned to work by the time the child was 6 months of age, and who worked an average of at least 35 h per week from 6 through 36 months. At 24 and 36 months, children whose mothers worked a nonstandard schedule had higher internalizing and externalizing behaviors. Modest, albeit inconsistent, evidence suggests that temperamentally reactive children may be more vulnerable to maternal work schedules. Maternal depressive symptoms partially mediated associations between nonstandard maternal work schedules and child behavior outcomes. PMID:19233479

  17. Characterization of robotics parallel algorithms and mapping onto a reconfigurable SIMD machine

    NASA Technical Reports Server (NTRS)

    Lee, C. S. G.; Lin, C. T.

    1989-01-01

    The kinematics, dynamics, Jacobian, and their corresponding inverse computations are six essential problems in the control of robot manipulators. Efficient parallel algorithms for these computations are discussed and analyzed. Their characteristics are identified and a scheme on the mapping of these algorithms to a reconfigurable parallel architecture is presented. Based on the characteristics including type of parallelism, degree of parallelism, uniformity of the operations, fundamental operations, data dependencies, and communication requirement, it is shown that most of the algorithms for robotic computations possess highly regular properties and some common structures, especially the linear recursive structure. Moreover, they are well-suited to be implemented on a single-instruction-stream multiple-data-stream (SIMD) computer with reconfigurable interconnection network. The model of a reconfigurable dual network SIMD machine with internal direct feedback is introduced. A systematic procedure internal direct feedback is introduced. A systematic procedure to map these computations to the proposed machine is presented. A new scheduling problem for SIMD machines is investigated and a heuristic algorithm, called neighborhood scheduling, that reorders the processing sequence of subtasks to reduce the communication time is described. Mapping results of a benchmark algorithm are illustrated and discussed.

  18. A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems

    NASA Astrophysics Data System (ADS)

    Thammano, Arit; Teekeng, Wannaporn

    2015-05-01

    The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.

  19. IOPS advisor: Research in progress on knowledge-intensive methods for irregular operations airline scheduling

    NASA Technical Reports Server (NTRS)

    Borse, John E.; Owens, Christopher C.

    1992-01-01

    Our research focuses on the problem of recovering from perturbations in large-scale schedules, specifically on the ability of a human-machine partnership to dynamically modify an airline schedule in response to unanticipated disruptions. This task is characterized by massive interdependencies and a large space of possible actions. Our approach is to apply the following: qualitative, knowledge-intensive techniques relying on a memory of stereotypical failures and appropriate recoveries; and quantitative techniques drawn from the Operations Research community's work on scheduling. Our main scientific challenge is to represent schedules, failures, and repairs so as to make both sets of techniques applicable to the same data. This paper outlines ongoing research in which we are cooperating with United Airlines to develop our understanding of the scientific issues underlying the practicalities of dynamic, real-time schedule repair.

  20. An Algorithm for the Weighted Earliness-Tardiness Unconstrained Project Scheduling Problem

    NASA Astrophysics Data System (ADS)

    Afshar Nadjafi, Behrouz; Shadrokh, Shahram

    This research considers a project scheduling problem with the object of minimizing weighted earliness-tardiness penalty costs, taking into account a deadline for the project and precedence relations among the activities. An exact recursive method has been proposed for solving the basic form of this problem. We present a new depth-first branch and bound algorithm for extended form of the problem, which time value of money is taken into account by discounting the cash flows. The algorithm is extended with two bounding rules in order to reduce the size of the branch and bound tree. Finally, some test problems are solved and computational results are reported.

  1. Performance evaluation of different types of particle representation procedures of Particle Swarm Optimization in Job-shop Scheduling Problems

    NASA Astrophysics Data System (ADS)

    Izah Anuar, Nurul; Saptari, Adi

    2016-02-01

    This paper addresses the types of particle representation (encoding) procedures in a population-based stochastic optimization technique in solving scheduling problems known in the job-shop manufacturing environment. It intends to evaluate and compare the performance of different particle representation procedures in Particle Swarm Optimization (PSO) in the case of solving Job-shop Scheduling Problems (JSP). Particle representation procedures refer to the mapping between the particle position in PSO and the scheduling solution in JSP. It is an important step to be carried out so that each particle in PSO can represent a schedule in JSP. Three procedures such as Operation and Particle Position Sequence (OPPS), random keys representation and random-key encoding scheme are used in this study. These procedures have been tested on FT06 and FT10 benchmark problems available in the OR-Library, where the objective function is to minimize the makespan by the use of MATLAB software. Based on the experimental results, it is discovered that OPPS gives the best performance in solving both benchmark problems. The contribution of this paper is the fact that it demonstrates to the practitioners involved in complex scheduling problems that different particle representation procedures can have significant effects on the performance of PSO in solving JSP.

  2. A Novel Strategy Using Factor Graphs and the Sum-Product Algorithm for Satellite Broadcast Scheduling Problems

    NASA Astrophysics Data System (ADS)

    Chen, Jung-Chieh

    This paper presents a low complexity algorithmic framework for finding a broadcasting schedule in a low-altitude satellite system, i. e., the satellite broadcast scheduling (SBS) problem, based on the recent modeling and computational methodology of factor graphs. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, in this paper, we transform the SBS problem into an LDPC-like problem through a factor graph instead of using the conventional neural network approaches to solve the SBS problem. Based on a factor graph framework, the soft-information, describing the probability that each satellite will broadcast information to a terminal at a specific time slot, is exchanged among the local processing in the proposed framework via the sum-product algorithm to iteratively optimize the satellite broadcasting schedule. Numerical results show that the proposed approach not only can obtain optimal solution but also enjoys the low complexity suitable for integral-circuit implementation.

  3. Reliability analysis of component of affination centrifugal 1 machine by using reliability engineering

    NASA Astrophysics Data System (ADS)

    Sembiring, N.; Ginting, E.; Darnello, T.

    2017-12-01

    Problems that appear in a company that produces refined sugar, the production floor has not reached the level of critical machine availability because it often suffered damage (breakdown). This results in a sudden loss of production time and production opportunities. This problem can be solved by Reliability Engineering method where the statistical approach to historical damage data is performed to see the pattern of the distribution. The method can provide a value of reliability, rate of damage, and availability level, of an machine during the maintenance time interval schedule. The result of distribution test to time inter-damage data (MTTF) flexible hose component is lognormal distribution while component of teflon cone lifthing is weibull distribution. While from distribution test to mean time of improvement (MTTR) flexible hose component is exponential distribution while component of teflon cone lifthing is weibull distribution. The actual results of the flexible hose component on the replacement schedule per 720 hours obtained reliability of 0.2451 and availability 0.9960. While on the critical components of teflon cone lifthing actual on the replacement schedule per 1944 hours obtained reliability of 0.4083 and availability 0.9927.

  4. Bidding-based autonomous process planning and scheduling

    NASA Astrophysics Data System (ADS)

    Gu, Peihua; Balasubramanian, Sivaram; Norrie, Douglas H.

    1995-08-01

    Improving productivity through computer integrated manufacturing systems (CIMS) and concurrent engineering requires that the islands of automation in an enterprise be completely integrated. The first step in this direction is to integrate design, process planning, and scheduling. This can be achieved through a bidding-based process planning approach. The product is represented in a STEP model with detailed design and administrative information including design specifications, batch size, and due dates. Upon arrival at the manufacturing facility, the product registered in the shop floor manager which is essentially a coordinating agent. The shop floor manager broadcasts the product's requirements to the machines. The shop contains autonomous machines that have knowledge about their functionality, capabilities, tooling, and schedule. Each machine has its own process planner and responds to the product's request in a different way that is consistent with its capabilities and capacities. When more than one machine offers certain process(es) for the same requirements, they enter into negotiation. Based on processing time, due date, and cost, one of the machines wins the contract. The successful machine updates its schedule and advises the product to request raw material for processing. The concept was implemented using a multi-agent system with the task decomposition and planning achieved through contract nets. The examples are included to illustrate the approach.

  5. Solving a Production Scheduling Problem by Means of Two Biobjective Metaheuristic Procedures

    NASA Astrophysics Data System (ADS)

    Toncovich, Adrián; Oliveros Colay, María José; Moreno, José María; Corral, Jiménez; Corral, Rafael

    2009-11-01

    Production planning and scheduling problems emphasize the need for the availability of management tools that can help to assure proper service levels to customers, maintaining, at the same time, the production costs at acceptable levels and maximizing the utilization of the production facilities. In this case, a production scheduling problem that arises in the context of the activities of a company dedicated to the manufacturing of furniture for children and teenagers is addressed. Two bicriteria metaheuristic procedures are proposed to solve the sequencing problem in a production equipment that constitutes the bottleneck of the production process of the company. The production scheduling problem can be characterized as a general flow shop with sequence dependant setup times and additional inventory constraints. Two objectives are simultaneously taken into account when the quality of the candidate solutions is evaluated: the minimization of completion time of all jobs, or makespan, and the minimization of the total flow time of all jobs. Both procedures are based on a local search strategy that responds to the structure of the simulated annealing metaheuristic. In this case, both metaheuristic approaches generate a set of solutions that provides an approximation to the optimal Pareto front. In order to evaluate the performance of the proposed techniques a series of experiments was conducted. After analyzing the results, it can be said that the solutions provided by both approaches are adequate from the viewpoint of the quality as well as the computational effort involved in their generation. Nevertheless, a further refinement of the proposed procedures should be implemented with the aim of facilitating a quasi-automatic definition of the solution parameters.

  6. Sort-Mid tasks scheduling algorithm in grid computing.

    PubMed

    Reda, Naglaa M; Tawfik, A; Marzok, Mohamed A; Khamis, Soheir M

    2015-11-01

    Scheduling tasks on heterogeneous resources distributed over a grid computing system is an NP-complete problem. The main aim for several researchers is to develop variant scheduling algorithms for achieving optimality, and they have shown a good performance for tasks scheduling regarding resources selection. However, using of the full power of resources is still a challenge. In this paper, a new heuristic algorithm called Sort-Mid is proposed. It aims to maximizing the utilization and minimizing the makespan. The new strategy of Sort-Mid algorithm is to find appropriate resources. The base step is to get the average value via sorting list of completion time of each task. Then, the maximum average is obtained. Finally, the task has the maximum average is allocated to the machine that has the minimum completion time. The allocated task is deleted and then, these steps are repeated until all tasks are allocated. Experimental tests show that the proposed algorithm outperforms almost other algorithms in terms of resources utilization and makespan.

  7. Capability of the Maximax&Maximin selection operator in the evolutionary algorithm for a nurse scheduling problem

    NASA Astrophysics Data System (ADS)

    Ramli, Razamin; Tein, Lim Huai

    2016-08-01

    A good work schedule can improve hospital operations by providing better coverage with appropriate staffing levels in managing nurse personnel. Hence, constructing the best nurse work schedule is the appropriate effort. In doing so, an improved selection operator in the Evolutionary Algorithm (EA) strategy for a nurse scheduling problem (NSP) is proposed. The smart and efficient scheduling procedures were considered. Computation of the performance of each potential solution or schedule was done through fitness evaluation. The best so far solution was obtained via special Maximax&Maximin (MM) parent selection operator embedded in the EA, which fulfilled all constraints considered in the NSP.

  8. Automated problem scheduling and reduction of synchronization delay effects

    NASA Technical Reports Server (NTRS)

    Saltz, Joel H.

    1987-01-01

    It is anticipated that in order to make effective use of many future high performance architectures, programs will have to exhibit at least a medium grained parallelism. A framework is presented for partitioning very sparse triangular systems of linear equations that is designed to produce favorable preformance results in a wide variety of parallel architectures. Efficient methods for solving these systems are of interest because: (1) they provide a useful model problem for use in exploring heuristics for the aggregation, mapping and scheduling of relatively fine grained computations whose data dependencies are specified by directed acrylic graphs, and (2) because such efficient methods can find direct application in the development of parallel algorithms for scientific computation. Simple expressions are derived that describe how to schedule computational work with varying degrees of granularity. The Encore Multimax was used as a hardware simulator to investigate the performance effects of using the partitioning techniques presented in shared memory architectures with varying relative synchronization costs.

  9. A best on-line algorithm for single machine scheduling the equal length jobs with the special chain precedence and delivery time

    NASA Astrophysics Data System (ADS)

    Gu, Cunchang; Mu, Yundong

    2013-03-01

    In this paper, we consider a single machine on-line scheduling problem with the special chains precedence and delivery time. All jobs arrive over time. The chains chainsi arrive at time ri , it is known that the processing and delivery time of each job on the chain satisfy one special condition CD a forehand: if the job J(i)j is the predecessor of the job J(i)k on the chain chaini, then they satisfy p(i)j = p(i)k = p >= qj >= qk , i = 1,2, ---,n , where pj and qj denote the processing time and the delivery time of the job Jj respectively. Obviously, if the arrival jobs have no chains precedence, it shows that the length of the corresponding chain is 1. The objective is to minimize the time by which all jobs have been delivered. We provide an on-line algorithm with a competitive ratio of √2 , and the result is the best possible.

  10. Problems in modeling man machine control behavior in biodynamic environments

    NASA Technical Reports Server (NTRS)

    Jex, H. R.

    1972-01-01

    Reviewed are some current problems in modeling man-machine control behavior in a biodynamic environment. It is given in two parts: (1) a review of the models which are appropriate for manual control behavior and the added elements necessary to deal with biodynamic interfaces; and (2) a review of some biodynamic interface pilot/vehicle problems which have occurred, been solved, or need to be solved.

  11. A time-shared machine repair problem with mixed spares under N-policy

    NASA Astrophysics Data System (ADS)

    Jain, Madhu; Shekhar, Chandra; Shukla, Shalini

    2016-06-01

    The present investigation deals with a machine repair problem consisting of cold and warm standby machines. The machines are subject to breakdown and are repaired by the permanent repairman operating under N-policy. There is provision of one additional removable repairman who is called upon when the work load of failed machines crosses a certain threshold level and is removed as soon as the work load again ceases to that level. Both repairmen recover the failed machines by following the time sharing concept which means that the repairmen share their repair job simultaneously among all the failed machines that have joined the system for repair. Markovian model has been developed by considering the queue dependent rates and solved analytically using the recursive technique. Various performance indices are derived which are further used to obtain the cost function. By taking illustration, numerical simulation and sensitivity analysis have been provided.

  12. Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem

    PubMed Central

    Liu, Xunying; Zhang, Chao; Woodland, Phil; Fonteneau, Elisabeth

    2017-01-01

    There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental ‘machine states’, generated as the ASR analysis progresses over time, to the incremental ‘brain states’, measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain. PMID:28945744

  13. A modify ant colony optimization for the grid jobs scheduling problem with QoS requirements

    NASA Astrophysics Data System (ADS)

    Pu, Xun; Lu, XianLiang

    2011-10-01

    Job scheduling with customers' quality of service (QoS) requirement is challenging in grid environment. In this paper, we present a modify Ant colony optimization (MACO) for the Job scheduling problem in grid. Instead of using the conventional construction approach to construct feasible schedules, the proposed algorithm employs a decomposition method to satisfy the customer's deadline and cost requirements. Besides, a new mechanism of service instances state updating is embedded to improve the convergence of MACO. Experiments demonstrate the effectiveness of the proposed algorithm.

  14. Batch Scheduling for Hybrid Assembly Differentiation Flow Shop to Minimize Total Actual Flow Time

    NASA Astrophysics Data System (ADS)

    Maulidya, R.; Suprayogi; Wangsaputra, R.; Halim, A. H.

    2018-03-01

    A hybrid assembly differentiation flow shop is a three-stage flow shop consisting of Machining, Assembly and Differentiation Stages and producing different types of products. In the machining stage, parts are processed in batches on different (unrelated) machines. In the assembly stage, each part of the different parts is assembled into an assembly product. Finally, the assembled products will further be processed into different types of final products in the differentiation stage. In this paper, we develop a batch scheduling model for a hybrid assembly differentiation flow shop to minimize the total actual flow time defined as the total times part spent in the shop floor from the arrival times until its due date. We also proposed a heuristic algorithm for solving the problems. The proposed algorithm is tested using a set of hypothetic data. The solution shows that the algorithm can solve the problems effectively.

  15. Solving a Higgs optimization problem with quantum annealing for machine learning.

    PubMed

    Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

    2017-10-18

    The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

  16. Solving a Higgs optimization problem with quantum annealing for machine learning

    NASA Astrophysics Data System (ADS)

    Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

    2017-10-01

    The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

  17. Automated telescope scheduling

    NASA Technical Reports Server (NTRS)

    Johnston, Mark D.

    1988-01-01

    With the ever increasing level of automation of astronomical telescopes the benefits and feasibility of automated planning and scheduling are becoming more apparent. Improved efficiency and increased overall telescope utilization are the most obvious goals. Automated scheduling at some level has been done for several satellite observatories, but the requirements on these systems were much less stringent than on modern ground or satellite observatories. The scheduling problem is particularly acute for Hubble Space Telescope: virtually all observations must be planned in excruciating detail weeks to months in advance. Space Telescope Science Institute has recently made significant progress on the scheduling problem by exploiting state-of-the-art artificial intelligence software technology. What is especially interesting is that this effort has already yielded software that is well suited to scheduling groundbased telescopes, including the problem of optimizing the coordinated scheduling of more than one telescope.

  18. Enhancements of evolutionary algorithm for the complex requirements of a nurse scheduling problem

    NASA Astrophysics Data System (ADS)

    Tein, Lim Huai; Ramli, Razamin

    2014-12-01

    Over the years, nurse scheduling is a noticeable problem that is affected by the global nurse turnover crisis. The more nurses are unsatisfied with their working environment the more severe the condition or implication they tend to leave. Therefore, the current undesirable work schedule is partly due to that working condition. Basically, there is a lack of complimentary requirement between the head nurse's liability and the nurses' need. In particular, subject to highly nurse preferences issue, the sophisticated challenge of doing nurse scheduling is failure to stimulate tolerance behavior between both parties during shifts assignment in real working scenarios. Inevitably, the flexibility in shifts assignment is hard to achieve for the sake of satisfying nurse diverse requests with upholding imperative nurse ward coverage. Hence, Evolutionary Algorithm (EA) is proposed to cater for this complexity in a nurse scheduling problem (NSP). The restriction of EA is discussed and thus, enhancement on the EA operators is suggested so that the EA would have the characteristic of a flexible search. This paper consists of three types of constraints which are the hard, semi-hard and soft constraints that can be handled by the EA with enhanced parent selection and specialized mutation operators. These operators and EA as a whole contribute to the efficiency of constraint handling, fitness computation as well as flexibility in the search, which correspond to the employment of exploration and exploitation principles.

  19. Aspects of job scheduling

    NASA Technical Reports Server (NTRS)

    Phillips, K.

    1976-01-01

    A mathematical model for job scheduling in a specified context is presented. The model uses both linear programming and combinatorial methods. While designed with a view toward optimization of scheduling of facility and plant operations at the Deep Space Communications Complex, the context is sufficiently general to be widely applicable. The general scheduling problem including options for scheduling objectives is discussed and fundamental parameters identified. Mathematical algorithms for partitioning problems germane to scheduling are presented.

  20. Psychometric Properties of the Disability Assessment Schedule (DAS) for Behavior Problems: An Independent Investigation

    ERIC Educational Resources Information Center

    Tsakanikos, Elias; Underwood, Lisa; Sturmey, Peter; Bouras, Nick; McCarthy, Jane

    2011-01-01

    The present study employed the Disability Assessment Schedule (DAS) to assess problem behaviors in a large sample of adults with ID (N = 568) and evaluate the psychometric properties of this instrument. Although the DAS problem behaviors were found to be internally consistent (Cronbach's [alpha] = 0.87), item analysis revealed one weak item…

  1. Analysis of Issues for Project Scheduling by Multiple, Dispersed Schedulers (distributed Scheduling) and Requirements for Manual Protocols and Computer-based Support

    NASA Technical Reports Server (NTRS)

    Richards, Stephen F.

    1991-01-01

    Although computerized operations have significant gains realized in many areas, one area, scheduling, has enjoyed few benefits from automation. The traditional methods of industrial engineering and operations research have not proven robust enough to handle the complexities associated with the scheduling of realistic problems. To address this need, NASA has developed the computer-aided scheduling system (COMPASS), a sophisticated, interactive scheduling tool that is in wide-spread use within NASA and the contractor community. Therefore, COMPASS provides no explicit support for the large class of problems in which several people, perhaps at various locations, build separate schedules that share a common pool of resources. This research examines the issue of distributing scheduling, as applied to application domains characterized by the partial ordering of tasks, limited resources, and time restrictions. The focus of this research is on identifying issues related to distributed scheduling, locating applicable problem domains within NASA, and suggesting areas for ongoing research. The issues that this research identifies are goals, rescheduling requirements, database support, the need for communication and coordination among individual schedulers, the potential for expert system support for scheduling, and the possibility of integrating artificially intelligent schedulers into a network of human schedulers.

  2. A 16-bit Coherent Ising Machine for One-Dimensional Ring and Cubic Graph Problems

    NASA Astrophysics Data System (ADS)

    Takata, Kenta; Marandi, Alireza; Hamerly, Ryan; Haribara, Yoshitaka; Maruo, Daiki; Tamate, Shuhei; Sakaguchi, Hiromasa; Utsunomiya, Shoko; Yamamoto, Yoshihisa

    2016-09-01

    Many tasks in our modern life, such as planning an efficient travel, image processing and optimizing integrated circuit design, are modeled as complex combinatorial optimization problems with binary variables. Such problems can be mapped to finding a ground state of the Ising Hamiltonian, thus various physical systems have been studied to emulate and solve this Ising problem. Recently, networks of mutually injected optical oscillators, called coherent Ising machines, have been developed as promising solvers for the problem, benefiting from programmability, scalability and room temperature operation. Here, we report a 16-bit coherent Ising machine based on a network of time-division-multiplexed femtosecond degenerate optical parametric oscillators. The system experimentally gives more than 99.6% of success rates for one-dimensional Ising ring and nondeterministic polynomial-time (NP) hard instances. The experimental and numerical results indicate that gradual pumping of the network combined with multiple spectral and temporal modes of the femtosecond pulses can improve the computational performance of the Ising machine, offering a new path for tackling larger and more complex instances.

  3. Reactive Scheduling in Multipurpose Batch Plants

    NASA Astrophysics Data System (ADS)

    Narayani, A.; Shaik, Munawar A.

    2010-10-01

    Scheduling is an important operation in process industries for improving resource utilization resulting in direct economic benefits. It has a two-fold objective of fulfilling customer orders within the specified time as well as maximizing the plant profit. Unexpected disturbances such as machine breakdown, arrival of rush orders and cancellation of orders affect the schedule of the plant. Reactive scheduling is generation of a new schedule which has minimum deviation from the original schedule in spite of the occurrence of unexpected events in the plant operation. Recently, Shaik & Floudas (2009) proposed a novel unified model for short-term scheduling of multipurpose batch plants using unit-specific event-based continuous time representation. In this paper, we extend the model of Shaik & Floudas (2009) to handle reactive scheduling.

  4. Sort-Mid tasks scheduling algorithm in grid computing

    PubMed Central

    Reda, Naglaa M.; Tawfik, A.; Marzok, Mohamed A.; Khamis, Soheir M.

    2014-01-01

    Scheduling tasks on heterogeneous resources distributed over a grid computing system is an NP-complete problem. The main aim for several researchers is to develop variant scheduling algorithms for achieving optimality, and they have shown a good performance for tasks scheduling regarding resources selection. However, using of the full power of resources is still a challenge. In this paper, a new heuristic algorithm called Sort-Mid is proposed. It aims to maximizing the utilization and minimizing the makespan. The new strategy of Sort-Mid algorithm is to find appropriate resources. The base step is to get the average value via sorting list of completion time of each task. Then, the maximum average is obtained. Finally, the task has the maximum average is allocated to the machine that has the minimum completion time. The allocated task is deleted and then, these steps are repeated until all tasks are allocated. Experimental tests show that the proposed algorithm outperforms almost other algorithms in terms of resources utilization and makespan. PMID:26644937

  5. Interactive computer aided shift scheduling.

    PubMed

    Gaertner, J

    2001-12-01

    This paper starts with a discussion of computer aided shift scheduling. After a brief review of earlier approaches, two conceptualizations of this field are introduced: First, shift scheduling as a field that ranges from extremely stable rosters at one pole to rather market-like approaches on the other pole. Unfortunately, already small alterations of a scheduling problem (e.g., the number of groups, the number of shifts) may call for rather different approaches and tools. Second, their environment shapes scheduling problems and scheduling has to be done within idiosyncratic organizational settings. This calls for the amalgamation of scheduling with other tasks (e.g., accounting) and for reflections whether better solutions might become possible by changes in the problem definition (e.g., other service levels, organizational changes). Therefore shift scheduling should be understood as a highly connected problem. Building upon these two conceptualizations, a few examples of software that ease scheduling in some areas of this field are given and future research questions are outlined.

  6. Secure Autonomous Automated Scheduling (SAAS). Rev. 1.1

    NASA Technical Reports Server (NTRS)

    Walke, Jon G.; Dikeman, Larry; Sage, Stephen P.; Miller, Eric M.

    2010-01-01

    This report describes network-centric operations, where a virtual mission operations center autonomously receives sensor triggers, and schedules space and ground assets using Internet-based technologies and service-oriented architectures. For proof-of-concept purposes, sensor triggers are received from the United States Geological Survey (USGS) to determine targets for space-based sensors. The Surrey Satellite Technology Limited (SSTL) Disaster Monitoring Constellation satellite, the UK-DMC, is used as the space-based sensor. The UK-DMC's availability is determined via machine-to-machine communications using SSTL's mission planning system. Access to/from the UK-DMC for tasking and sensor data is via SSTL's and Universal Space Network's (USN) ground assets. The availability and scheduling of USN's assets can also be performed autonomously via machine-to-machine communications. All communication, both on the ground and between ground and space, uses open Internet standards

  7. Full glowworm swarm optimization algorithm for whole-set orders scheduling in single machine.

    PubMed

    Yu, Zhang; Yang, Xiaomei

    2013-01-01

    By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency.

  8. New scheduling rules for a dynamic flexible flow line problem with sequence-dependent setup times

    NASA Astrophysics Data System (ADS)

    Kia, Hamidreza; Ghodsypour, Seyed Hassan; Davoudpour, Hamid

    2017-09-01

    In the literature, the application of multi-objective dynamic scheduling problem and simple priority rules are widely studied. Although these rules are not efficient enough due to simplicity and lack of general insight, composite dispatching rules have a very suitable performance because they result from experiments. In this paper, a dynamic flexible flow line problem with sequence-dependent setup times is studied. The objective of the problem is minimization of mean flow time and mean tardiness. A 0-1 mixed integer model of the problem is formulated. Since the problem is NP-hard, four new composite dispatching rules are proposed to solve it by applying genetic programming framework and choosing proper operators. Furthermore, a discrete-event simulation model is made to examine the performances of scheduling rules considering four new heuristic rules and the six adapted heuristic rules from the literature. It is clear from the experimental results that composite dispatching rules that are formed from genetic programming have a better performance in minimization of mean flow time and mean tardiness than others.

  9. Integrated production and distribution scheduling problems related to fixed delivery departure dates and weights of late orders.

    PubMed

    Li, Shanlin; Li, Maoqin

    2015-01-01

    We consider an integrated production and distribution scheduling problem faced by a typical make-to-order manufacturer which relies on a third-party logistics (3PL) provider for finished product delivery to customers. In the beginning of a planning horizon, the manufacturer has received a set of orders to be processed on a single production line. Completed orders are delivered to customers by a finite number of vehicles provided by the 3PL company which follows a fixed daily or weekly shipping schedule such that the vehicles have fixed departure dates which are not part of the decisions. The problem is to find a feasible schedule that minimizes one of the following objective functions when processing times and weights are oppositely ordered: (1) the total weight of late orders and (2) the number of vehicles used subject to the condition that the total weight of late orders is minimum. We show that both problems are solvable in polynomial time.

  10. Integrated Production and Distribution Scheduling Problems Related to Fixed Delivery Departure Dates and Weights of Late Orders

    PubMed Central

    Li, Shanlin; Li, Maoqin

    2015-01-01

    We consider an integrated production and distribution scheduling problem faced by a typical make-to-order manufacturer which relies on a third-party logistics (3PL) provider for finished product delivery to customers. In the beginning of a planning horizon, the manufacturer has received a set of orders to be processed on a single production line. Completed orders are delivered to customers by a finite number of vehicles provided by the 3PL company which follows a fixed daily or weekly shipping schedule such that the vehicles have fixed departure dates which are not part of the decisions. The problem is to find a feasible schedule that minimizes one of the following objective functions when processing times and weights are oppositely ordered: (1) the total weight of late orders and (2) the number of vehicles used subject to the condition that the total weight of late orders is minimum. We show that both problems are solvable in polynomial time. PMID:25785285

  11. Analysis of tasks for dynamic man/machine load balancing in advanced helicopters

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Jorgensen, C.C.

    1987-10-01

    This report considers task allocation requirements imposed by advanced helicopter designs incorporating mixes of human pilots and intelligent machines. Specifically, it develops an analogy between load balancing using distributed non-homogeneous multiprocessors and human team functions. A taxonomy is presented which can be used to identify task combinations likely to cause overload for dynamic scheduling and process allocation mechanisms. Designer criteria are given for function decomposition, separation of control from data, and communication handling for dynamic tasks. Possible effects of n-p complete scheduling problems are noted and a class of combinatorial optimization methods are examined.

  12. 49 CFR 214.533 - Schedule of repairs subject to availability of parts.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... Maintenance Machines and Hi-Rail Vehicles § 214.533 Schedule of repairs subject to availability of parts. (a... 49 Transportation 4 2011-10-01 2011-10-01 false Schedule of repairs subject to availability of... maintenance machine or a hi-rail vehicle by the end of the next business day following the report of the...

  13. Constraint-based scheduling

    NASA Technical Reports Server (NTRS)

    Zweben, Monte

    1991-01-01

    The GERRY scheduling system developed by NASA Ames with assistance from the Lockheed Space Operations Company, and the Lockheed Artificial Intelligence Center, uses a method called constraint-based iterative repair. Using this technique, one encodes both hard rules and preference criteria into data structures called constraints. GERRY repeatedly attempts to improve schedules by seeking repairs for violated constraints. The system provides a general scheduling framework which is being tested on two NASA applications. The larger of the two is the Space Shuttle Ground Processing problem which entails the scheduling of all the inspection, repair, and maintenance tasks required to prepare the orbiter for flight. The other application involves power allocation for the NASA Ames wind tunnels. Here the system will be used to schedule wind tunnel tests with the goal of minimizing power costs. In this paper, we describe the GERRY system and its application to the Space Shuttle problem. We also speculate as to how the system would be used for manufacturing, transportation, and military problems.

  14. Constraint-based scheduling

    NASA Technical Reports Server (NTRS)

    Zweben, Monte

    1991-01-01

    The GERRY scheduling system developed by NASA Ames with assistance from the Lockheed Space Operations Company, and the Lockheed Artificial Intelligence Center, uses a method called constraint based iterative repair. Using this technique, one encodes both hard rules and preference criteria into data structures called constraints. GERRY repeatedly attempts to improve schedules by seeking repairs for violated constraints. The system provides a general scheduling framework which is being tested on two NASA applications. The larger of the two is the Space Shuttle Ground Processing problem which entails the scheduling of all inspection, repair, and maintenance tasks required to prepare the orbiter for flight. The other application involves power allocations for the NASA Ames wind tunnels. Here the system will be used to schedule wind tunnel tests with the goal of minimizing power costs. In this paper, we describe the GERRY system and its applications to the Space Shuttle problem. We also speculate as to how the system would be used for manufacturing, transportation, and military problems.

  15. Constraint-based scheduling

    NASA Technical Reports Server (NTRS)

    Zweben, Monte

    1993-01-01

    The GERRY scheduling system developed by NASA Ames with assistance from the Lockheed Space Operations Company, and the Lockheed Artificial Intelligence Center, uses a method called constraint-based iterative repair. Using this technique, one encodes both hard rules and preference criteria into data structures called constraints. GERRY repeatedly attempts to improve schedules by seeking repairs for violated constraints. The system provides a general scheduling framework which is being tested on two NASA applications. The larger of the two is the Space Shuttle Ground Processing problem which entails the scheduling of all the inspection, repair, and maintenance tasks required to prepare the orbiter for flight. The other application involves power allocation for the NASA Ames wind tunnels. Here the system will be used to schedule wind tunnel tests with the goal of minimizing power costs. In this paper, we describe the GERRY system and its application to the Space Shuttle problem. We also speculate as to how the system would be used for manufacturing, transportation, and military problems.

  16. A Two-Stage Stochastic Mixed-Integer Programming Approach to the Smart House Scheduling Problem

    NASA Astrophysics Data System (ADS)

    Ozoe, Shunsuke; Tanaka, Yoichi; Fukushima, Masao

    A “Smart House” is a highly energy-optimized house equipped with photovoltaic systems (PV systems), electric battery systems, fuel cell cogeneration systems (FC systems), electric vehicles (EVs) and so on. Smart houses are attracting much attention recently thanks to their enhanced ability to save energy by making full use of renewable energy and by achieving power grid stability despite an increased power draw for installed PV systems. Yet running a smart house's power system, with its multiple power sources and power storages, is no simple task. In this paper, we consider the problem of power scheduling for a smart house with a PV system, an FC system and an EV. We formulate the problem as a mixed integer programming problem, and then extend it to a stochastic programming problem involving recourse costs to cope with uncertain electricity demand, heat demand and PV power generation. Using our method, we seek to achieve the optimal power schedule running at the minimum expected operation cost. We present some results of numerical experiments with data on real-life demands and PV power generation to show the effectiveness of our method.

  17. An element search ant colony technique for solving virtual machine placement problem

    NASA Astrophysics Data System (ADS)

    Srija, J.; Rani John, Rose; Kanaga, Grace Mary, Dr.

    2017-09-01

    The data centres in the cloud environment play a key role in providing infrastructure for ubiquitous computing, pervasive computing, mobile computing etc. This computing technique tries to utilize the available resources in order to provide services. Hence maintaining the resource utilization without wastage of power consumption has become a challenging task for the researchers. In this paper we propose the direct guidance ant colony system for effective mapping of virtual machines to the physical machine with maximal resource utilization and minimal power consumption. The proposed algorithm has been compared with the existing ant colony approach which is involved in solving virtual machine placement problem and thus the proposed algorithm proves to provide better result than the existing technique.

  18. Reinforcement learning in scheduling

    NASA Technical Reports Server (NTRS)

    Dietterich, Tom G.; Ok, Dokyeong; Zhang, Wei; Tadepalli, Prasad

    1994-01-01

    The goal of this research is to apply reinforcement learning methods to real-world problems like scheduling. In this preliminary paper, we show that learning to solve scheduling problems such as the Space Shuttle Payload Processing and the Automatic Guided Vehicle (AGV) scheduling can be usefully studied in the reinforcement learning framework. We discuss some of the special challenges posed by the scheduling domain to these methods and propose some possible solutions we plan to implement.

  19. Trait Mindfulness, Problem-Gambling Severity, Altered State of Awareness and Urge to Gamble in Poker-Machine Gamblers.

    PubMed

    McKeith, Charles F A; Rock, Adam J; Clark, Gavin I

    2017-06-01

    In Australia, poker-machine gamblers represent a disproportionate number of problem gamblers. To cultivate a greater understanding of the psychological mechanisms involved in poker-machine gambling, a repeated measures cue-reactivity protocol was administered. A community sample of 38 poker-machine gamblers was assessed for problem-gambling severity and trait mindfulness. Participants were also assessed regarding altered state of awareness (ASA) and urge to gamble at baseline, following a neutral cue, and following a gambling cue. Results indicated that: (a) urge to gamble significantly increased from neutral cue to gambling cue, while controlling for baseline urge; (b) cue-reactive ASA did not significantly mediate the relationship between problem-gambling severity and cue-reactive urge (from neutral cue to gambling cue); (c) trait mindfulness was significantly negatively associated with both problem-gambling severity and cue-reactive urge (i.e., from neutral cue to gambling cue, while controlling for baseline urge); and (d) trait mindfulness did not significantly moderate the effect of problem-gambling severity on cue-reactive urge (from neutral cue to gambling cue). This is the first study to demonstrate a negative association between trait mindfulness and cue-reactive urge to gamble in a population of poker-machine gamblers. Thus, this association merits further evaluation both in relation to poker-machine gambling and other gambling modalities.

  20. On Several Fundamental Problems of Optimization, Estimation, and Scheduling in Wireless Communications

    NASA Astrophysics Data System (ADS)

    Gao, Qian

    For both the conventional radio frequency and the comparably recent optical wireless communication systems, extensive effort from the academia had been made in improving the network spectrum efficiency and/or reducing the error rate. To achieve these goals, many fundamental challenges such as power efficient constellation design, nonlinear distortion mitigation, channel training design, network scheduling and etc. need to be properly addressed. In this dissertation, novel schemes are proposed accordingly to deal with specific problems falling in category of these challenges. Rigorous proofs and analyses are provided for each of our work to make a fair comparison with the corresponding peer works to clearly demonstrate the advantages. The first part of this dissertation considers a multi-carrier optical wireless system employing intensity modulation (IM) and direct detection (DD). A block-wise constellation design is presented, which treats the DC-bias that conventionally used solely for biasing purpose as an information basis. Our scheme, we term it MSM-JDCM, takes advantage of the compactness of sphere packing in a higher dimensional space, and in turn power efficient constellations are obtained by solving an advanced convex optimization problem. Besides the significant power gains, the MSM-JDCM has many other merits such as being capable of mitigating nonlinear distortion by including a peak-to-power ratio (PAPR) constraint, minimizing inter-symbol-interference (ISI) caused by frequency-selective fading with a novel precoder designed and embedded, and further reducing the bit-error-rate (BER) by combining with an optimized labeling scheme. The second part addresses several optimization problems in a multi-color visible light communication system, including power efficient constellation design, joint pre-equalizer and constellation design, and modeling of different structured channels with cross-talks. Our novel constellation design scheme, termed CSK-Advanced, is

  1. Application of the SNoW machine learning paradigm to a set of transportation imaging problems

    NASA Astrophysics Data System (ADS)

    Paul, Peter; Burry, Aaron M.; Wang, Yuheng; Kozitsky, Vladimir

    2012-01-01

    Machine learning methods have been successfully applied to image object classification problems where there is clear distinction between classes and where a comprehensive set of training samples and ground truth are readily available. The transportation domain is an area where machine learning methods are particularly applicable, since the classification problems typically have well defined class boundaries and, due to high traffic volumes in most applications, massive roadway data is available. Though these classes tend to be well defined, the particular image noises and variations can be challenging. Another challenge is the extremely high accuracy typically required in most traffic applications. Incorrect assignment of fines or tolls due to imaging mistakes is not acceptable in most applications. For the front seat vehicle occupancy detection problem, classification amounts to determining whether one face (driver only) or two faces (driver + passenger) are detected in the front seat of a vehicle on a roadway. For automatic license plate recognition, the classification problem is a type of optical character recognition problem encompassing multiple class classification. The SNoW machine learning classifier using local SMQT features is shown to be successful in these two transportation imaging applications.

  2. A generalized network flow model for the multi-mode resource-constrained project scheduling problem with discounted cash flows

    NASA Astrophysics Data System (ADS)

    Chen, Miawjane; Yan, Shangyao; Wang, Sin-Siang; Liu, Chiu-Lan

    2015-02-01

    An effective project schedule is essential for enterprises to increase their efficiency of project execution, to maximize profit, and to minimize wastage of resources. Heuristic algorithms have been developed to efficiently solve the complicated multi-mode resource-constrained project scheduling problem with discounted cash flows (MRCPSPDCF) that characterize real problems. However, the solutions obtained in past studies have been approximate and are difficult to evaluate in terms of optimality. In this study, a generalized network flow model, embedded in a time-precedence network, is proposed to formulate the MRCPSPDCF with the payment at activity completion times. Mathematically, the model is formulated as an integer network flow problem with side constraints, which can be efficiently solved for optimality, using existing mathematical programming software. To evaluate the model performance, numerical tests are performed. The test results indicate that the model could be a useful planning tool for project scheduling in the real world.

  3. Proposed algorithm to improve job shop production scheduling using ant colony optimization method

    NASA Astrophysics Data System (ADS)

    Pakpahan, Eka KA; Kristina, Sonna; Setiawan, Ari

    2017-12-01

    This paper deals with the determination of job shop production schedule on an automatic environment. On this particular environment, machines and material handling system are integrated and controlled by a computer center where schedule were created and then used to dictate the movement of parts and the operations at each machine. This setting is usually designed to have an unmanned production process for a specified interval time. We consider here parts with various operations requirement. Each operation requires specific cutting tools. These parts are to be scheduled on machines each having identical capability, meaning that each machine is equipped with a similar set of cutting tools therefore is capable of processing any operation. The availability of a particular machine to process a particular operation is determined by the remaining life time of its cutting tools. We proposed an algorithm based on the ant colony optimization method and embedded them on matlab software to generate production schedule which minimize the total processing time of the parts (makespan). We test the algorithm on data provided by real industry and the process shows a very short computation time. This contributes a lot to the flexibility and timelines targeted on an automatic environment.

  4. A Multiple Ant Colony Metahuristic for the Air Refueling Tanker Assignment Problem

    DTIC Science & Technology

    2002-03-01

    Problem The tanker assignment problem can be modeled as a job shop scheduling problem ( JSSP ). The JSSP is made up of n jobs, composed of m ordered...points) to be processed on all the machines (tankers). The problem with using JSSP is that the tanker assignment problem has multiple objectives... JSSP will minimize the time it takes for all jobs, but this may take an inordinate number of tankers. Thus using JSSP alone is not necessarily a good

  5. More reliable protein NMR peak assignment via improved 2-interval scheduling.

    PubMed

    Chen, Zhi-Zhong; Lin, Guohui; Rizzi, Romeo; Wen, Jianjun; Xu, Dong; Xu, Ying; Jiang, Tao

    2005-03-01

    Protein NMR peak assignment refers to the process of assigning a group of "spin systems" obtained experimentally to a protein sequence of amino acids. The automation of this process is still an unsolved and challenging problem in NMR protein structure determination. Recently, protein NMR peak assignment has been formulated as an interval scheduling problem (ISP), where a protein sequence P of amino acids is viewed as a discrete time interval I (the amino acids on P one-to-one correspond to the time units of I), each subset S of spin systems that are known to originate from consecutive amino acids from P is viewed as a "job" j(s), the preference of assigning S to a subsequence P of consecutive amino acids on P is viewed as the profit of executing job j(s) in the subinterval of I corresponding to P, and the goal is to maximize the total profit of executing the jobs (on a single machine) during I. The interval scheduling problem is max SNP-hard in general; but in the real practice of protein NMR peak assignment, each job j(s) usually requires at most 10 consecutive time units, and typically the jobs that require one or two consecutive time units are the most difficult to assign/schedule. In order to solve these most difficult assignments, we present an efficient 13/7-approximation algorithm for the special case of the interval scheduling problem where each job takes one or two consecutive time units. Combining this algorithm with a greedy filtering strategy for handling long jobs (i.e., jobs that need more than two consecutive time units), we obtain a new efficient heuristic for protein NMR peak assignment. Our experimental study shows that the new heuristic produces the best peak assignment in most of the cases, compared with the NMR peak assignment algorithms in the recent literature. The above algorithm is also the first approximation algorithm for a nontrivial case of the well-known interval scheduling problem that breaks the ratio 2 barrier.

  6. Task Scheduling in Desktop Grids: Open Problems

    NASA Astrophysics Data System (ADS)

    Chernov, Ilya; Nikitina, Natalia; Ivashko, Evgeny

    2017-12-01

    We survey the areas of Desktop Grid task scheduling that seem to be insufficiently studied so far and are promising for efficiency, reliability, and quality of Desktop Grid computing. These topics include optimal task grouping, "needle in a haystack" paradigm, game-theoretical scheduling, domain-imposed approaches, special optimization of the final stage of the batch computation, and Enterprise Desktop Grids.

  7. An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

    PubMed Central

    Rao, Yunqing; Qi, Dezhong; Li, Jinling

    2013-01-01

    For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem. PMID:24489491

  8. An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

    PubMed

    Rao, Yunqing; Qi, Dezhong; Li, Jinling

    2013-01-01

    For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

  9. 21 CFR 1310.16 - Exemptions for certain scheduled listed chemical products.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... 21 Food and Drugs 9 2011-04-01 2011-04-01 false Exemptions for certain scheduled listed chemical... RECORDS AND REPORTS OF LISTED CHEMICALS AND CERTAIN MACHINES § 1310.16 Exemptions for certain scheduled listed chemical products. (a) Upon the application of a manufacturer of a scheduled listed chemical...

  10. An Optimizing Space Data-Communications Scheduling Method and Algorithm with Interference Mitigation, Generalized for a Broad Class of Optimization Problems

    NASA Technical Reports Server (NTRS)

    Rash, James L.

    2010-01-01

    NASA's space data-communications infrastructure, the Space Network and the Ground Network, provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft via orbiting relay satellites and ground stations. An implementation of the methods and algorithms disclosed herein will be a system that produces globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary search, a class of probabilistic strategies for searching large solution spaces, constitutes the essential technology in this disclosure. Also disclosed are methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithm itself. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally, with applicability to a very broad class of combinatorial optimization problems.

  11. Testing Task Schedulers on Linux System

    NASA Astrophysics Data System (ADS)

    Jelenković, Leonardo; Groš, Stjepan; Jakobović, Domagoj

    Testing task schedulers on Linux operating system proves to be a challenging task. There are two main problems. The first one is to identify which properties of the scheduler to test. The second problem is how to perform it, e.g., which API to use that is sufficiently precise and in the same time supported on most platforms. This paper discusses the problems in realizing test framework for testing task schedulers and presents one potential solution. Observed behavior of the scheduler is the one used for “normal” task scheduling (SCHED_OTHER), unlike one used for real-time tasks (SCHED_FIFO, SCHED_RR).

  12. An Exploratory Study of Problem Gambling on Casino versus Non-Casino Electronic Gaming Machines

    ERIC Educational Resources Information Center

    Clarke, Dave; Pulford, Justin; Bellringer, Maria; Abbott, Max; Hodgins, David C.

    2012-01-01

    Electronic gaming machines (EGMs) have been frequently associated with problem gambling. Little research has compared the relative contribution of casino EGMs versus non-casino EGMs on current problem gambling, after controlling for demographic factors and gambling behaviour. Our exploratory study obtained data from questionnaires administered to…

  13. Decomposability and scalability in space-based observatory scheduling

    NASA Technical Reports Server (NTRS)

    Muscettola, Nicola; Smith, Stephen F.

    1992-01-01

    In this paper, we discuss issues of problem and model decomposition within the HSTS scheduling framework. HSTS was developed and originally applied in the context of the Hubble Space Telescope (HST) scheduling problem, motivated by the limitations of the current solution and, more generally, the insufficiency of classical planning and scheduling approaches in this problem context. We first summarize the salient architectural characteristics of HSTS and their relationship to previous scheduling and AI planning research. Then, we describe some key problem decomposition techniques supported by HSTS and underlying our integrated planning and scheduling approach, and we discuss the leverage they provide in solving space-based observatory scheduling problems.

  14. DTS: Building custom, intelligent schedulers

    NASA Technical Reports Server (NTRS)

    Hansson, Othar; Mayer, Andrew

    1994-01-01

    DTS is a decision-theoretic scheduler, built on top of a flexible toolkit -- this paper focuses on how the toolkit might be reused in future NASA mission schedulers. The toolkit includes a user-customizable scheduling interface, and a 'Just-For-You' optimization engine. The customizable interface is built on two metaphors: objects and dynamic graphs. Objects help to structure problem specifications and related data, while dynamic graphs simplify the specification of graphical schedule editors (such as Gantt charts). The interface can be used with any 'back-end' scheduler, through dynamically-loaded code, interprocess communication, or a shared database. The 'Just-For-You' optimization engine includes user-specific utility functions, automatically compiled heuristic evaluations, and a postprocessing facility for enforcing scheduling policies. The optimization engine is based on BPS, the Bayesian Problem-Solver (1,2), which introduced a similar approach to solving single-agent and adversarial graph search problems.

  15. A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling.

    PubMed

    Deng, Qianwang; Gong, Guiliang; Gong, Xuran; Zhang, Like; Liu, Wei; Ren, Qinghua

    2017-01-01

    Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N , in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.

  16. A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling

    PubMed Central

    Deng, Qianwang; Gong, Xuran; Zhang, Like; Liu, Wei; Ren, Qinghua

    2017-01-01

    Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed. PMID:28458687

  17. An Optimizing Space Data-Communications Scheduling Method and Algorithm with Interference Mitigation, Generalized for a Broad Class of Optimization Problems

    NASA Technical Reports Server (NTRS)

    Rash, James

    2014-01-01

    NASA's space data-communications infrastructure-the Space Network and the Ground Network-provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft. The Space Network operates several orbiting geostationary platforms (the Tracking and Data Relay Satellite System (TDRSS)), each with its own servicedelivery antennas onboard. The Ground Network operates service-delivery antennas at ground stations located around the world. Together, these networks enable data transfer between user spacecraft and their mission control centers on Earth. Scheduling data-communications events for spacecraft that use the NASA communications infrastructure-the relay satellites and the ground stations-can be accomplished today with software having an operational heritage dating from the 1980s or earlier. An implementation of the scheduling methods and algorithms disclosed and formally specified herein will produce globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary algorithms, a class of probabilistic strategies for searching large solution spaces, is the essential technology invoked and exploited in this disclosure. Also disclosed are secondary methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithms themselves. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure within the expected range of future users and space- or ground-based service-delivery assets. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally. The generalized methods and algorithms are applicable to a very broad class of combinatorial

  18. A survey of planning and scheduling research at the NASA Ames Research Center

    NASA Technical Reports Server (NTRS)

    Zweben, Monte

    1989-01-01

    NASA Ames Research Center has a diverse program in planning and scheduling. Some research projects as well as some applications are highlighted. Topics addressed include machine learning techniques, action representations and constraint-based scheduling systems. The applications discussed are planetary rovers, Hubble Space Telescope scheduling, and Pioneer Venus orbit scheduling.

  19. SOFIA's Choice: Automating the Scheduling of Airborne Observations

    NASA Technical Reports Server (NTRS)

    Frank, Jeremy; Norvig, Peter (Technical Monitor)

    1999-01-01

    This paper describes the problem of scheduling observations for an airborne telescope. Given a set of prioritized observations to choose from, and a wide range of complex constraints governing legitimate choices and orderings, how can we efficiently and effectively create a valid flight plan which supports high priority observations? This problem is quite different from scheduling problems which are routinely solved automatically in industry. For instance, the problem requires making choices which lead to other choices later, and contains many interacting complex constraints over both discrete and continuous variables. Furthermore, new types of constraints may be added as the fundamental problem changes. As a result of these features, this problem cannot be solved by traditional scheduling techniques. The problem resembles other problems in NASA and industry, from observation scheduling for rovers and other science instruments to vehicle routing. The remainder of the paper is organized as follows. In 2 we describe the observatory in order to provide some background. In 3 we describe the problem of scheduling a single flight. In 4 we compare flight planning and other scheduling problems and argue that traditional techniques are not sufficient to solve this problem. We also mention similar complex scheduling problems which may benefit from efforts to solve this problem. In 5 we describe an approach for solving this problem based on research into a similar problem, that of scheduling observations for a space-borne probe. In 6 we discuss extensions of the flight planning problem as well as other problems which are similar to flight planning. In 7 we conclude and discuss future work.

  20. Heuristic algorithms for solving of the tool routing problem for CNC cutting machines

    NASA Astrophysics Data System (ADS)

    Chentsov, P. A.; Petunin, A. A.; Sesekin, A. N.; Shipacheva, E. N.; Sholohov, A. E.

    2015-11-01

    The article is devoted to the problem of minimizing the path of the cutting tool to shape cutting machines began. This problem can be interpreted as a generalized traveling salesman problem. Earlier version of the dynamic programming method to solve this problem was developed. Unfortunately, this method allows to process an amount not exceeding thirty circuits. In this regard, the task of constructing quasi-optimal route becomes relevant. In this paper we propose options for quasi-optimal greedy algorithms. Comparison of the results of exact and approximate algorithms is given.

  1. Investigations into Generalization of Constraint-Based Scheduling Theories with Applications to Space Telescope Observation Scheduling

    NASA Technical Reports Server (NTRS)

    Muscettola, Nicola; Smith, Steven S.

    1996-01-01

    This final report summarizes research performed under NASA contract NCC 2-531 toward generalization of constraint-based scheduling theories and techniques for application to space telescope observation scheduling problems. Our work into theories and techniques for solution of this class of problems has led to the development of the Heuristic Scheduling Testbed System (HSTS), a software system for integrated planning and scheduling. Within HSTS, planning and scheduling are treated as two complementary aspects of the more general process of constructing a feasible set of behaviors of a target system. We have validated the HSTS approach by applying it to the generation of observation schedules for the Hubble Space Telescope. This report summarizes the HSTS framework and its application to the Hubble Space Telescope domain. First, the HSTS software architecture is described, indicating (1) how the structure and dynamics of a system is modeled in HSTS, (2) how schedules are represented at multiple levels of abstraction, and (3) the problem solving machinery that is provided. Next, the specific scheduler developed within this software architecture for detailed management of Hubble Space Telescope operations is presented. Finally, experimental performance results are given that confirm the utility and practicality of the approach.

  2. A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems

    PubMed Central

    Zhang, Le; Wu, Jinnan

    2014-01-01

    This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature. PMID:24672389

  3. A PSO-based hybrid metaheuristic for permutation flowshop scheduling problems.

    PubMed

    Zhang, Le; Wu, Jinnan

    2014-01-01

    This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.

  4. Compiling Planning into Scheduling: A Sketch

    NASA Technical Reports Server (NTRS)

    Bedrax-Weiss, Tania; Crawford, James M.; Smith, David E.

    2004-01-01

    Although there are many approaches for compiling a planning problem into a static CSP or a scheduling problem, current approaches essentially preserve the structure of the planning problem in the encoding. In this pape: we present a fundamentally different encoding that more accurately resembles a scheduling problem. We sketch the approach and argue, based on an example, that it is possible to automate the generation of such an encoding for problems with certain properties and thus produce a compiler of planning into scheduling problems. Furthermore we argue that many NASA problems exhibit these properties and that such a compiler would provide benefits to both theory and practice.

  5. Production scheduling with ant colony optimization

    NASA Astrophysics Data System (ADS)

    Chernigovskiy, A. S.; Kapulin, D. V.; Noskova, E. E.; Yamskikh, T. N.; Tsarev, R. Yu

    2017-10-01

    The optimum solution of the production scheduling problem for manufacturing processes at an enterprise is crucial as it allows one to obtain the required amount of production within a specified time frame. Optimum production schedule can be found using a variety of optimization algorithms or scheduling algorithms. Ant colony optimization is one of well-known techniques to solve the global multi-objective optimization problem. In the article, the authors present a solution of the production scheduling problem by means of an ant colony optimization algorithm. A case study of the algorithm efficiency estimated against some others production scheduling algorithms is presented. Advantages of the ant colony optimization algorithm and its beneficial effect on the manufacturing process are provided.

  6. A modified generalized extremal optimization algorithm for the quay crane scheduling problem with interference constraints

    NASA Astrophysics Data System (ADS)

    Guo, Peng; Cheng, Wenming; Wang, Yi

    2014-10-01

    The quay crane scheduling problem (QCSP) determines the handling sequence of tasks at ship bays by a set of cranes assigned to a container vessel such that the vessel's service time is minimized. A number of heuristics or meta-heuristics have been proposed to obtain the near-optimal solutions to overcome the NP-hardness of the problem. In this article, the idea of generalized extremal optimization (GEO) is adapted to solve the QCSP with respect to various interference constraints. The resulting GEO is termed the modified GEO. A randomized searching method for neighbouring task-to-QC assignments to an incumbent task-to-QC assignment is developed in executing the modified GEO. In addition, a unidirectional search decoding scheme is employed to transform a task-to-QC assignment to an active quay crane schedule. The effectiveness of the developed GEO is tested on a suite of benchmark problems introduced by K.H. Kim and Y.M. Park in 2004 (European Journal of Operational Research, Vol. 156, No. 3). Compared with other well-known existing approaches, the experiment results show that the proposed modified GEO is capable of obtaining the optimal or near-optimal solution in a reasonable time, especially for large-sized problems.

  7. New fuzzy support vector machine for the class imbalance problem in medical datasets classification.

    PubMed

    Gu, Xiaoqing; Ni, Tongguang; Wang, Hongyuan

    2014-01-01

    In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin. Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper. Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP.

  8. Current problems in the dynamics and design of mechanisms and machines

    NASA Astrophysics Data System (ADS)

    Kestel'Man, V. N.

    The papers contained in this volume deal with possible ways of improving the dynamic and structural properties of machines and mechanisms and also with problems associated with the design of aircraft equipment. Topics discussed include estimation of the stressed state of a model of an orbital film structure, a study of the operation of an aerodynamic angle transducer in flow of a hot gas, calculation of the efficiency of aircraft gear drives, and dynamic accuracy of a controlled manipulator. Papers are also presented on optimal synthesis of mechanical systems with variable properties, synthesis of mechanisms using initial kinematic chains, and using shape memory materials in the design of machines and mechanisms. (For individual items see A93-31202 to A93-31214)

  9. A survey of planning and scheduling research at the NASA Ames Research Center

    NASA Technical Reports Server (NTRS)

    Zweben, Monte

    1988-01-01

    NASA Ames Research Center has a diverse program in planning and scheduling. This paper highlights some of our research projects as well as some of our applications. Topics addressed include machine learning techniques, action representations and constraint-based scheduling systems. The applications discussed are planetary rovers, Hubble Space Telescope scheduling, and Pioneer Venus orbit scheduling.

  10. Dypas: A dynamic payload scheduler for shuttle missions

    NASA Technical Reports Server (NTRS)

    Davis, Stephen

    1988-01-01

    Decision and analysis systems have had broad and very practical application areas in the human decision making process. These software systems range from the help sections in simple accounting packages, to the more complex computer configuration programs. Dypas is a decision and analysis system that aids prelaunch shutlle scheduling, and has added functionality to aid the rescheduling done in flight. Dypas is written in Common Lisp on a Symbolics Lisp machine. Dypas differs from other scheduling programs in that it can draw its knowledge from different rule bases and apply them to different rule interpretation schemes. The system has been coded with Flavors, an object oriented extension to Common Lisp on the Symbolics hardware. This allows implementation of objects (experiments) to better match the problem definition, and allows a more coherent solution space to be developed. Dypas was originally developed to test a programmer's aptitude toward Common Lisp and the Symbolics software environment. Since then the system has grown into a large software effort with several programmers and researchers thrown into the effort. Dypas is currently using two expert systems and three inferencing procedures to generate a many object schedule. The paper will review the abilities of Dypas and comment on its functionality.

  11. Concurrent Reinforcement Schedules for Problem Behavior and Appropriate Behavior: Experimental Applications of the Matching Law

    ERIC Educational Resources Information Center

    Borrero, Carrie S. W.; Vollmer, Timothy R.; Borrero, John C.; Bourret, Jason C.; Sloman, Kimberly N.; Samaha, Andrew L.; Dallery, Jesse

    2010-01-01

    This study evaluated how children who exhibited functionally equivalent problem and appropriate behavior allocate responding to experimentally arranged reinforcer rates. Relative reinforcer rates were arranged on concurrent variable-interval schedules and effects on relative response rates were interpreted using the generalized matching equation.…

  12. On the integrability of the motion of 3D-Swinging Atwood machine and related problems

    NASA Astrophysics Data System (ADS)

    Elmandouh, A. A.

    2016-03-01

    In the present article, we study the problem of the motion of 3D- Swinging Atwood machine. A new integrable case for this problem is announced. We point out a new integrable case describing the motion of a heavy particle on a titled cone.

  13. An effective hybrid immune algorithm for solving the distributed permutation flow-shop scheduling problem

    NASA Astrophysics Data System (ADS)

    Xu, Ye; Wang, Ling; Wang, Shengyao; Liu, Min

    2014-09-01

    In this article, an effective hybrid immune algorithm (HIA) is presented to solve the distributed permutation flow-shop scheduling problem (DPFSP). First, a decoding method is proposed to transfer a job permutation sequence to a feasible schedule considering both factory dispatching and job sequencing. Secondly, a local search with four search operators is presented based on the characteristics of the problem. Thirdly, a special crossover operator is designed for the DPFSP, and mutation and vaccination operators are also applied within the framework of the HIA to perform an immune search. The influence of parameter setting on the HIA is investigated based on the Taguchi method of design of experiment. Extensive numerical testing results based on 420 small-sized instances and 720 large-sized instances are provided. The effectiveness of the HIA is demonstrated by comparison with some existing heuristic algorithms and the variable neighbourhood descent methods. New best known solutions are obtained by the HIA for 17 out of 420 small-sized instances and 585 out of 720 large-sized instances.

  14. Artificial intelligence approaches to astronomical observation scheduling

    NASA Technical Reports Server (NTRS)

    Johnston, Mark D.; Miller, Glenn

    1988-01-01

    Automated scheduling will play an increasing role in future ground- and space-based observatory operations. Due to the complexity of the problem, artificial intelligence technology currently offers the greatest potential for the development of scheduling tools with sufficient power and flexibility to handle realistic scheduling situations. Summarized here are the main features of the observatory scheduling problem, how artificial intelligence (AI) techniques can be applied, and recent progress in AI scheduling for Hubble Space Telescope.

  15. User requirements for a patient scheduling system

    NASA Technical Reports Server (NTRS)

    Zimmerman, W.

    1979-01-01

    A rehabilitation institute's needs and wants from a scheduling system were established by (1) studying the existing scheduling system and the variables that affect patient scheduling, (2) conducting a human-factors study to establish the human interfaces that affect patients' meeting prescribed therapy schedules, and (3) developing and administering a questionnaire to the staff which pertains to the various interface problems in order to identify staff requirements to minimize scheduling problems and other factors that may limit the effectiveness of any new scheduling system.

  16. Techniques for cash management in scheduling manufacturing operations

    NASA Astrophysics Data System (ADS)

    Morady Gohareh, Mehdy; Shams Gharneh, Naser; Ghasemy Yaghin, Reza

    2017-06-01

    The objective in traditional scheduling is usually time based. Minimizing the makespan, total flow times, total tardi costs, etc. are instances of these objectives. In manufacturing, processing each job entails a cost paying and price receiving. Thus, the objective should include some notion of managing the flow of cash. We have defined two new objectives: maximization of average and minimum available cash. For single machine scheduling, it is demonstrated that scheduling jobs in decreasing order of profit ratios maximizes the former and improves productivity. Moreover, scheduling jobs in increasing order of costs and breaking ties in decreasing order of prices maximizes the latter and creates protection against financial instability.

  17. Stochastic flow shop scheduling of overlapping jobs on tandem machines in application to optimizing the US Army's deliberate nuclear, biological, and chemical decontamination process, (final report). Master's thesis

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Novikov, V.

    1991-05-01

    The U.S. Army's detailed equipment decontamination process is a stochastic flow shop which has N independent non-identical jobs (vehicles) which have overlapping processing times. This flow shop consists of up to six non-identical machines (stations). With the exception of one station, the processing times of the jobs are random variables. Based on an analysis of the processing times, the jobs for the 56 Army heavy division companies were scheduled according to the best shortest expected processing time - longest expected processing time (SEPT-LEPT) sequence. To assist in this scheduling the Gap Comparison Heuristic was developed to select the best SEPT-LEPTmore » schedule. This schedule was then used in balancing the detailed equipment decon line in order to find the best possible site configuration subject to several constraints. The detailed troop decon line, in which all jobs are independent and identically distributed, was then balanced. Lastly, an NBC decon optimization computer program was developed using the scheduling and line balancing results. This program serves as a prototype module for the ANBACIS automated NBC decision support system.... Decontamination, Stochastic flow shop, Scheduling, Stochastic scheduling, Minimization of the makespan, SEPT-LEPT Sequences, Flow shop line balancing, ANBACIS.« less

  18. Multi-objective flexible job shop scheduling problem using variable neighborhood evolutionary algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Chun; Ji, Zhicheng; Wang, Yan

    2017-07-01

    In this paper, multi-objective flexible job shop scheduling problem (MOFJSP) was studied with the objects to minimize makespan, total workload and critical workload. A variable neighborhood evolutionary algorithm (VNEA) was proposed to obtain a set of Pareto optimal solutions. First, two novel crowded operators in terms of the decision space and object space were proposed, and they were respectively used in mating selection and environmental selection. Then, two well-designed neighborhood structures were used in local search, which consider the problem characteristics and can hold fast convergence. Finally, extensive comparison was carried out with the state-of-the-art methods specially presented for solving MOFJSP on well-known benchmark instances. The results show that the proposed VNEA is more effective than other algorithms in solving MOFJSP.

  19. Process Development and Micro-Machining of MARBLE Foam-Cored Rexolite Hemi-Shell Ablator Capsules

    DOE PAGES

    Randolph, Randall Blaine; Oertel, John A.; Schmidt, Derek William; ...

    2016-06-30

    For this study, machined CH hemi-shell ablator capsules have been successfully produced by the MST-7 Target Fabrication Team at Los Alamos National Laboratory. Process development and micro-machining techniques have been developed to produce capsules for both the Omega and National Ignition Facility (NIF) campaigns. These capsules are gas filled up to 10 atm and consist of a machined plastic hemi-shell outer layer that accommodates various specially engineered low-density polystyrene foam cores. Machining and assembly of the two-part, step-jointed plastic hemi-shell outer layer required development of new techniques, processes, and tooling while still meeting very aggressive shot schedules for both campaigns.more » Finally, problems encountered and process improvements will be discussed that describe this very unique, complex capsule design approach through the first Omega proof-of-concept version to the larger NIF version.« less

  20. Maximizing the nurses' preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

    NASA Astrophysics Data System (ADS)

    Jafari, Hamed; Salmasi, Nasser

    2015-09-01

    The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital's demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses' preferences for working shifts and weekends off by considering several important factors such as hospital's policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first, the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49 % compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses.

  1. Experiments with a decision-theoretic scheduler

    NASA Technical Reports Server (NTRS)

    Hansson, Othar; Holt, Gerhard; Mayer, Andrew

    1992-01-01

    This paper describes DTS, a decision-theoretic scheduler designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems, and using probabilistic inference to aggregate this information in light of features of a given problem. BPS, the Bayesian Problem-Solver, introduced a similar approach to solving single-agent and adversarial graph search problems, yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.

  2. A task scheduler framework for self-powered wireless sensors.

    PubMed

    Nordman, Mikael M

    2003-10-01

    The cost and inconvenience of cabling is a factor limiting widespread use of intelligent sensors. Recent developments in short-range, low-power radio seem to provide an opening to this problem, making development of wireless sensors feasible. However, for these sensors the energy availability is a main concern. The common solution is either to use a battery or to harvest ambient energy. The benefit of harvested ambient energy is that the energy feeder can be considered as lasting a lifetime, thus it saves the user from concerns related to energy management. The problem is, however, the unpredictability and unsteady behavior of ambient energy sources. This becomes a main concern for sensors that run multiple tasks at different priorities. This paper proposes a new scheduler framework that enables the reliable assignment of task priorities and scheduling in sensors powered by ambient energy. The framework being based on environment parameters, virtual queues, and a state machine with transition conditions, dynamically manages task execution according to priorities. The framework is assessed in a test system powered by a solar panel. The results show the functionality of the framework and how task execution reliably is handled without violating the priority scheme that has been assigned to it.

  3. An adaptive large neighborhood search procedure applied to the dynamic patient admission scheduling problem.

    PubMed

    Lusby, Richard Martin; Schwierz, Martin; Range, Troels Martin; Larsen, Jesper

    2016-11-01

    The aim of this paper is to provide an improved method for solving the so-called dynamic patient admission scheduling (DPAS) problem. This is a complex scheduling problem that involves assigning a set of patients to hospital beds over a given time horizon in such a way that several quality measures reflecting patient comfort and treatment efficiency are maximized. Consideration must be given to uncertainty in the length of stays of patients as well as the possibility of emergency patients. We develop an adaptive large neighborhood search (ALNS) procedure to solve the problem. This procedure utilizes a Simulated Annealing framework. We thoroughly test the performance of the proposed ALNS approach on a set of 450 publicly available problem instances. A comparison with the current state-of-the-art indicates that the proposed methodology provides solutions that are of comparable quality for small and medium sized instances (up to 1000 patients); the two approaches provide solutions that differ in quality by approximately 1% on average. The ALNS procedure does, however, provide solutions in a much shorter time frame. On larger instances (between 1000-4000 patients) the improvement in solution quality by the ALNS procedure is substantial, approximately 3-14% on average, and as much as 22% on a single instance. The time taken to find such results is, however, in the worst case, a factor 12 longer on average than the time limit which is granted to the current state-of-the-art. The proposed ALNS procedure is an efficient and flexible method for solving the DPAS problem. Copyright © 2016 Elsevier B.V. All rights reserved.

  4. Scheduler Design Criteria: Requirements and Considerations

    NASA Technical Reports Server (NTRS)

    Lee, Hanbong

    2016-01-01

    This presentation covers fundamental requirements and considerations for developing schedulers in airport operations. We first introduce performance and functional requirements for airport surface schedulers. Among various optimization problems in airport operations, we focus on airport surface scheduling problem, including runway and taxiway operations. We then describe a basic methodology for airport surface scheduling such as node-link network model and scheduling algorithms previously developed. Next, we explain how to design a mathematical formulation in more details, which consists of objectives, decision variables, and constraints. Lastly, we review other considerations, including optimization tools, computational performance, and performance metrics for evaluation.

  5. Linux Kernel Co-Scheduling For Bulk Synchronous Parallel Applications

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Jones, Terry R

    2011-01-01

    This paper describes a kernel scheduling algorithm that is based on co-scheduling principles and that is intended for parallel applications running on 1000 cores or more where inter-node scalability is key. Experimental results for a Linux implementation on a Cray XT5 machine are presented.1 The results indicate that Linux is a suitable operating system for this new scheduling scheme, and that this design provides a dramatic improvement in scaling performance for synchronizing collective operations at scale.

  6. 49 CFR 214.533 - Schedule of repairs subject to availability of parts.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... Maintenance Machines and Hi-Rail Vehicles § 214.533 Schedule of repairs subject to availability of parts. (a... maintenance machine or a hi-rail vehicle by the end of the next business day following the report of the... maintenance machine or hi-rail vehicle within seven calendar days after receiving the necessary part. The...

  7. Processing time tolerance-based ACO algorithm for solving job-shop scheduling problem

    NASA Astrophysics Data System (ADS)

    Luo, Yabo; Waden, Yongo P.

    2017-06-01

    Ordinarily, Job Shop Scheduling Problem (JSSP) is known as NP-hard problem which has uncertainty and complexity that cannot be handled by a linear method. Thus, currently studies on JSSP are concentrated mainly on applying different methods of improving the heuristics for optimizing the JSSP. However, there still exist many problems for efficient optimization in the JSSP, namely, low efficiency and poor reliability, which can easily trap the optimization process of JSSP into local optima. Therefore, to solve this problem, a study on Ant Colony Optimization (ACO) algorithm combined with constraint handling tactics is carried out in this paper. Further, the problem is subdivided into three parts: (1) Analysis of processing time tolerance-based constraint features in the JSSP which is performed by the constraint satisfying model; (2) Satisfying the constraints by considering the consistency technology and the constraint spreading algorithm in order to improve the performance of ACO algorithm. Hence, the JSSP model based on the improved ACO algorithm is constructed; (3) The effectiveness of the proposed method based on reliability and efficiency is shown through comparative experiments which are performed on benchmark problems. Consequently, the results obtained by the proposed method are better, and the applied technique can be used in optimizing JSSP.

  8. Automated Scheduling Via Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Biefeld, Eric W.; Cooper, Lynne P.

    1991-01-01

    Artificial-intelligence software that automates scheduling developed in Operations Mission Planner (OMP) research project. Software used in both generation of new schedules and modification of existing schedules in view of changes in tasks and/or available resources. Approach based on iterative refinement. Although project focused upon scheduling of operations of scientific instruments and other equipment aboard spacecraft, also applicable to such terrestrial problems as scheduling production in factory.

  9. Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

    PubMed Central

    Li, Jun-qing; Pan, Quan-ke; Mao, Kun

    2014-01-01

    A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

  10. Asymptotic analysis of SPTA-based algorithms for no-wait flow shop scheduling problem with release dates.

    PubMed

    Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang

    2014-01-01

    We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms.

  11. Asymptotic Analysis of SPTA-Based Algorithms for No-Wait Flow Shop Scheduling Problem with Release Dates

    PubMed Central

    Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang

    2014-01-01

    We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms. PMID:24764774

  12. A Novel Joint Problem of Routing, Scheduling, and Variable-Width Channel Allocation in WMNs

    PubMed Central

    Liu, Wan-Yu; Chou, Chun-Hung

    2014-01-01

    This paper investigates a novel joint problem of routing, scheduling, and channel allocation for single-radio multichannel wireless mesh networks in which multiple channel widths can be adjusted dynamically through a new software technology so that more concurrent transmissions and suppressed overlapping channel interference can be achieved. Although the previous works have studied this joint problem, their linear programming models for the problem were not incorporated with some delicate constraints. As a result, this paper first constructs a linear programming model with more practical concerns and then proposes a simulated annealing approach with a novel encoding mechanism, in which the configurations of multiple time slots are devised to characterize the dynamic transmission process. Experimental results show that our approach can find the same or similar solutions as the optimal solutions for smaller-scale problems and can efficiently find good-quality solutions for a variety of larger-scale problems. PMID:24982990

  13. Two-MILP models for scheduling elective surgeries within a private healthcare facility.

    PubMed

    Khlif Hachicha, Hejer; Zeghal Mansour, Farah

    2016-11-05

    This paper deals with an Integrated Elective Surgery-Scheduling Problem (IESSP) that arises in a privately operated healthcare facility. It aims to optimize the resource utilization of the entire surgery process including pre-operative, per-operative and post-operative activities. Moreover, it addresses a specific feature of private facilities where surgeons are independent service providers and may conduct their surgeries in different private healthcare facilities. Thus, the problem requires the assignment of surgery patients to hospital beds, operating rooms and recovery beds as well as their sequencing over a 1-day period while taking into account surgeons' availability constraints. We present two Mixed Integer Linear Programs (MILP) that model the IESSP as a three-stage hybrid flow-shop scheduling problem with recirculation, resource synchronization, dedicated machines, and blocking constraints. To assess the empirical performance of the proposed models, we conducted experiments on real-world data of a Tunisian private clinic: Clinique Ennasr and on randomly generated instances. Two criteria were minimised: the patients' average length of stay and the number of patients' overnight stays. The computational results show that the proposed models can solve instances with up to 44 surgical cases in a reasonable CPU time using a general-purpose MILP solver.

  14. Space communications scheduler: A rule-based approach to adaptive deadline scheduling

    NASA Technical Reports Server (NTRS)

    Straguzzi, Nicholas

    1990-01-01

    Job scheduling is a deceptively complex subfield of computer science. The highly combinatorial nature of the problem, which is NP-complete in nearly all cases, requires a scheduling program to intelligently transverse an immense search tree to create the best possible schedule in a minimal amount of time. In addition, the program must continually make adjustments to the initial schedule when faced with last-minute user requests, cancellations, unexpected device failures, quests, cancellations, unexpected device failures, etc. A good scheduler must be quick, flexible, and efficient, even at the expense of generating slightly less-than-optimal schedules. The Space Communication Scheduler (SCS) is an intelligent rule-based scheduling system. SCS is an adaptive deadline scheduler which allocates modular communications resources to meet an ordered set of user-specified job requests on board the NASA Space Station. SCS uses pattern matching techniques to detect potential conflicts through algorithmic and heuristic means. As a result, the system generates and maintains high density schedules without relying heavily on backtracking or blind search techniques. SCS is suitable for many common real-world applications.

  15. Single-machine common/slack due window assignment problems with linear decreasing processing times

    NASA Astrophysics Data System (ADS)

    Zhang, Xingong; Lin, Win-Chin; Wu, Wen-Hsiang; Wu, Chin-Chia

    2017-08-01

    This paper studies linear non-increasing processing times and the common/slack due window assignment problems on a single machine, where the actual processing time of a job is a linear non-increasing function of its starting time. The aim is to minimize the sum of the earliness cost, tardiness cost, due window location and due window size. Some optimality results are discussed for the common/slack due window assignment problems and two O(n log n) time algorithms are presented to solve the two problems. Finally, two examples are provided to illustrate the correctness of the corresponding algorithms.

  16. Bridging the Gap Between Planning and Scheduling

    NASA Technical Reports Server (NTRS)

    Smith, David E.; Frank, Jeremy; Jonsson, Ari K.; Norvig, Peter (Technical Monitor)

    2000-01-01

    Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast. Scheduling research has focused on much larger problems where there is little action choice, but the resulting ordering problem is hard. In this paper, we give an overview of M planning and scheduling techniques, focusing on their similarities, differences, and limitations. We also argue that many difficult practical problems lie somewhere between planning and scheduling, and that neither area has the right set of tools for solving these vexing problems.

  17. 20 CFR 402.165 - Fee schedule.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... 20 Employees' Benefits 2 2011-04-01 2011-04-01 false Fee schedule. 402.165 Section 402.165 Employees' Benefits SOCIAL SECURITY ADMINISTRATION AVAILABILITY OF INFORMATION AND RECORDS TO THE PUBLIC... costs of operating the machine, plus the actual cost of the materials used, plus charges for the time...

  18. The Ames-Lockheed orbiter processing scheduling system

    NASA Technical Reports Server (NTRS)

    Zweben, Monte; Gargan, Robert

    1991-01-01

    A general purpose scheduling system and its application to Space Shuttle Orbiter Processing at the Kennedy Space Center (KSC) are described. Orbiter processing entails all the inspection, testing, repair, and maintenance necessary to prepare the Shuttle for launch and takes place within the Orbiter Processing Facility (OPF) at KSC, the Vehicle Assembly Building (VAB), and on the launch pad. The problems are extremely combinatoric in that there are thousands of tasks, resources, and other temporal considerations that must be coordinated. Researchers are building a scheduling tool that they hope will be an integral part of automating the planning and scheduling process at KSC. The scheduling engine is domain independent and is also being applied to Space Shuttle cargo processing problems as well as wind tunnel scheduling problems.

  19. Linux Kernel Co-Scheduling and Bulk Synchronous Parallelism

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Jones, Terry R

    2012-01-01

    This paper describes a kernel scheduling algorithm that is based on coscheduling principles and that is intended for parallel applications running on 1000 cores or more. Experimental results for a Linux implementation on a Cray XT5 machine are presented. The results indicate that Linux is a suitable operating system for this new scheduling scheme, and that this design provides a dramatic improvement in scaling performance for synchronizing collective operations at scale.

  20. A Novel Algorithm Combining Finite State Method and Genetic Algorithm for Solving Crude Oil Scheduling Problem

    PubMed Central

    Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun

    2014-01-01

    A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method. PMID:24772031

  1. Simultaneous planning of the project scheduling and material procurement problem under the presence of multiple suppliers

    NASA Astrophysics Data System (ADS)

    Tabrizi, Babak H.; Ghaderi, Seyed Farid

    2016-09-01

    Simultaneous planning of project scheduling and material procurement can improve the project execution costs. Hence, the issue has been addressed here by a mixed-integer programming model. The proposed model facilitates the procurement decisions by accounting for a number of suppliers offering a distinctive discount formula from which to purchase the required materials. It is aimed at developing schedules with the best net present value regarding the obtained benefit and costs of the project execution. A genetic algorithm is applied to deal with the problem, in addition to a modified version equipped with a variable neighbourhood search. The underlying factors of the solution methods are calibrated by the Taguchi method to obtain robust solutions. The performance of the aforementioned methods is compared for different problem sizes, in which the utilized local search proved efficient. Finally, a sensitivity analysis is carried out to check the effect of inflation on the objective function value.

  2. Protocols for distributive scheduling

    NASA Technical Reports Server (NTRS)

    Richards, Stephen F.; Fox, Barry

    1993-01-01

    The increasing complexity of space operations and the inclusion of interorganizational and international groups in the planning and control of space missions lead to requirements for greater communication, coordination, and cooperation among mission schedulers. These schedulers must jointly allocate scarce shared resources among the various operational and mission oriented activities while adhering to all constraints. This scheduling environment is complicated by such factors as the presence of varying perspectives and conflicting objectives among the schedulers, the need for different schedulers to work in parallel, and limited communication among schedulers. Smooth interaction among schedulers requires the use of protocols that govern such issues as resource sharing, authority to update the schedule, and communication of updates. This paper addresses the development and characteristics of such protocols and their use in a distributed scheduling environment that incorporates computer-aided scheduling tools. An example problem is drawn from the domain of space shuttle mission planning.

  3. A discrete artificial bee colony algorithm incorporating differential evolution for the flow-shop scheduling problem with blocking

    NASA Astrophysics Data System (ADS)

    Han, Yu-Yan; Gong, Dunwei; Sun, Xiaoyan

    2015-07-01

    A flow-shop scheduling problem with blocking has important applications in a variety of industrial systems but is underrepresented in the research literature. In this study, a novel discrete artificial bee colony (ABC) algorithm is presented to solve the above scheduling problem with a makespan criterion by incorporating the ABC with differential evolution (DE). The proposed algorithm (DE-ABC) contains three key operators. One is related to the employed bee operator (i.e. adopting mutation and crossover operators of discrete DE to generate solutions with good quality); the second is concerned with the onlooker bee operator, which modifies the selected solutions using insert or swap operators based on the self-adaptive strategy; and the last is for the local search, that is, the insert-neighbourhood-based local search with a small probability is adopted to improve the algorithm's capability in exploitation. The performance of the proposed DE-ABC algorithm is empirically evaluated by applying it to well-known benchmark problems. The experimental results show that the proposed algorithm is superior to the compared algorithms in minimizing the makespan criterion.

  4. Evaluation of scheduling techniques for payload activity planning

    NASA Technical Reports Server (NTRS)

    Bullington, Stanley F.

    1991-01-01

    Two tasks related to payload activity planning and scheduling were performed. The first task involved making a comparison of space mission activity scheduling problems with production scheduling problems. The second task consisted of a statistical analysis of the output of runs of the Experiment Scheduling Program (ESP). Details of the work which was performed on these two tasks are presented.

  5. Block Scheduling in High Schools.

    ERIC Educational Resources Information Center

    Irmsher, Karen

    1996-01-01

    Block Scheduling has been considered a cure for a lengthy list of educational problems. This report reviews the literature on block schedules and describes some Oregon high schools that have integrated block scheduling. Major disadvantages included resistance to change and requirements that teachers change their teaching strategies. There is…

  6. Quantum annealing versus classical machine learning applied to a simplified computational biology problem

    PubMed Central

    Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

    2018-01-01

    Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems. PMID:29652405

  7. Quantum annealing versus classical machine learning applied to a simplified computational biology problem

    NASA Astrophysics Data System (ADS)

    Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

    2018-03-01

    Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to classify and rank binding affinities. Using simplified data sets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified data sets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems.

  8. Construction machine control guidance implementation strategy.

    DOT National Transportation Integrated Search

    2010-07-01

    Machine Controlled Guidance (MCG) technology may be used in roadway and bridge construction to improve construction efficiencies, potentially resulting in reduced project costs and accelerated schedules. The technology utilizes a Global Positioning S...

  9. A Genetic Algorithm Tool (splicer) for Complex Scheduling Problems and the Space Station Freedom Resupply Problem

    NASA Technical Reports Server (NTRS)

    Wang, Lui; Valenzuela-Rendon, Manuel

    1993-01-01

    The Space Station Freedom will require the supply of items in a regular fashion. A schedule for the delivery of these items is not easy to design due to the large span of time involved and the possibility of cancellations and changes in shuttle flights. This paper presents the basic concepts of a genetic algorithm model, and also presents the results of an effort to apply genetic algorithms to the design of propellant resupply schedules. As part of this effort, a simple simulator and an encoding by which a genetic algorithm can find near optimal schedules have been developed. Additionally, this paper proposes ways in which robust schedules, i.e., schedules that can tolerate small changes, can be found using genetic algorithms.

  10. The school bus routing and scheduling problem with transfers

    PubMed Central

    Doerner, Karl F.; Parragh, Sophie N.

    2015-01-01

    In this article, we study the school bus routing and scheduling problem with transfers arising in the field of nonperiodic public transportation systems. It deals with the transportation of pupils from home to their school in the morning taking the possibility that pupils may change buses into account. Allowing transfers has several consequences. On the one hand, it allows more flexibility in the bus network structure and can, therefore, help to reduce operating costs. On the other hand, transfers have an impact on the service level: the perceived service quality is lower due to the existence of transfers; however, at the same time, user ride times may be reduced and, thus, transfers may also have a positive impact on service quality. The main objective is the minimization of the total operating costs. We develop a heuristic solution framework to solve this problem and compare it with two solution concepts that do not consider transfers. The impact of transfers on the service level in terms of time loss (or user ride time) and the number of transfers is analyzed. Our results show that allowing transfers reduces total operating costs significantly while average and maximum user ride times are comparable to solutions without transfers. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 65(2), 180–203 2015 PMID:28163329

  11. Achieving spectrum conservation for the minimum-span and minimum-order frequency assignment problems

    NASA Technical Reports Server (NTRS)

    Heyward, Ann O.

    1992-01-01

    Effective and efficient solutions of frequency assignment problems assumes increasing importance as the radiofrequency spectrum experiences ever increasing utilization by diverse communications services, requiring that the most efficient use of this resource be achieved. The research presented explores a general approach to the frequency assignment problem, in which such problems are categorized by the appropriate spectrum conserving objective function, and are each treated as an N-job, M-machine scheduling problem appropriate for the objective. Results obtained and presented illustrate that such an approach presents an effective means of achieving spectrum conserving frequency assignments for communications systems in a variety of environments.

  12. The MICRO-BOSS scheduling system: Current status and future efforts

    NASA Technical Reports Server (NTRS)

    Sadeh, Norman M.

    1992-01-01

    In this paper, a micro-opportunistic approach to factory scheduling was described that closely monitors the evolution of bottlenecks during the construction of the schedule and continuously redirects search towards the bottleneck that appears to be most critical. This approach differs from earlier opportunistic approaches, as it does not require scheduling large resource subproblems or large job subproblems before revising the current scheduling strategy. This micro-opportunistic approach was implemented in the context of the MICRO-BOSS factory scheduling system. A study comparing MICRO-BOSS against a macro-opportunistic scheduler suggests that the additional flexibility of the micro-opportunistic approach to scheduling generally yields important reductions in both tardiness and inventory. Current research efforts include: adaptation of MICRO-BOSS to deal with sequence-dependent setups and development of micro-opportunistic reactive scheduling techniques that will enable the system to patch the schedule in the presence of contingencies such as machine breakdowns, raw materials arriving late, job cancellations, etc.

  13. Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment.

    PubMed

    Abdullahi, Mohammed; Ngadi, Md Asri

    2016-01-01

    Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.

  14. Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment

    PubMed Central

    Abdullahi, Mohammed; Ngadi, Md Asri

    2016-01-01

    Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan. PMID:27348127

  15. On the asymptotic optimality and improved strategies of SPTB heuristic for open-shop scheduling problem

    NASA Astrophysics Data System (ADS)

    Bai, Danyu; Zhang, Zhihai

    2014-08-01

    This article investigates the open-shop scheduling problem with the optimal criterion of minimising the sum of quadratic completion times. For this NP-hard problem, the asymptotic optimality of the shortest processing time block (SPTB) heuristic is proven in the sense of limit. Moreover, three different improvements, namely, the job-insert scheme, tabu search and genetic algorithm, are introduced to enhance the quality of the original solution generated by the SPTB heuristic. At the end of the article, a series of numerical experiments demonstrate the convergence of the heuristic, the performance of the improvements and the effectiveness of the quadratic objective.

  16. Scheduling: A guide for program managers

    NASA Technical Reports Server (NTRS)

    1994-01-01

    The following topics are discussed concerning scheduling: (1) milestone scheduling; (2) network scheduling; (3) program evaluation and review technique; (4) critical path method; (5) developing a network; (6) converting an ugly duckling to a swan; (7) network scheduling problem; (8) (9) network scheduling when resources are limited; (10) multi-program considerations; (11) influence on program performance; (12) line-of-balance technique; (13) time management; (14) recapitulization; and (15) analysis.

  17. Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.

    PubMed

    Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

    2018-04-06

    Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.

  18. Deep Space Network Scheduling Using Evolutionary Computational Methods

    NASA Technical Reports Server (NTRS)

    Guillaume, Alexandre; Lee, Seugnwon; Wang, Yeou-Fang; Terrile, Richard J.

    2007-01-01

    The paper presents the specific approach taken to formulate the problem in terms of gene encoding, fitness function, and genetic operations. The genome is encoded such that a subset of the scheduling constraints is automatically satisfied. Several fitness functions are formulated to emphasize different aspects of the scheduling problem. The optimal solutions of the different fitness functions demonstrate the trade-off of the scheduling problem and provide insight into a conflict resolution process.

  19. Decision-theoretic control of EUVE telescope scheduling

    NASA Technical Reports Server (NTRS)

    Hansson, Othar; Mayer, Andrew

    1993-01-01

    This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems and using probabilistic inference to aggregate this information in light of the features of a given problem. The Bayesian Problem-Solver (BPS) introduced a similar approach to solving single agent and adversarial graph search patterns yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.

  20. Automatic Generation of Heuristics for Scheduling

    NASA Technical Reports Server (NTRS)

    Morris, Robert A.; Bresina, John L.; Rodgers, Stuart M.

    1997-01-01

    This paper presents a technique, called GenH, that automatically generates search heuristics for scheduling problems. The impetus for developing this technique is the growing consensus that heuristics encode advice that is, at best, useful in solving most, or typical, problem instances, and, at worst, useful in solving only a narrowly defined set of instances. In either case, heuristic problem solvers, to be broadly applicable, should have a means of automatically adjusting to the idiosyncrasies of each problem instance. GenH generates a search heuristic for a given problem instance by hill-climbing in the space of possible multi-attribute heuristics, where the evaluation of a candidate heuristic is based on the quality of the solution found under its guidance. We present empirical results obtained by applying GenH to the real world problem of telescope observation scheduling. These results demonstrate that GenH is a simple and effective way of improving the performance of an heuristic scheduler.

  1. Constraint-based integration of planning and scheduling for space-based observatory management

    NASA Technical Reports Server (NTRS)

    Muscettola, Nicola; Smith, Steven F.

    1994-01-01

    Progress toward the development of effective, practical solutions to space-based observatory scheduling problems within the HSTS scheduling framework is reported. HSTS was developed and originally applied in the context of the Hubble Space Telescope (HST) short-term observation scheduling problem. The work was motivated by the limitations of the current solution and, more generally, by the insufficiency of classical planning and scheduling approaches in this problem context. HSTS has subsequently been used to develop improved heuristic solution techniques in related scheduling domains and is currently being applied to develop a scheduling tool for the upcoming Submillimeter Wave Astronomy Satellite (SWAS) mission. The salient architectural characteristics of HSTS and their relationship to previous scheduling and AI planning research are summarized. Then, some key problem decomposition techniques underlying the integrated planning and scheduling approach to the HST problem are described; research results indicate that these techniques provide leverage in solving space-based observatory scheduling problems. Finally, more recently developed constraint-posting scheduling procedures and the current SWAS application focus are summarized.

  2. Dynamism in a Semiconductor Industrial Machine Allocation Problem using a Hybrid of the Bio-inspired and Musical-Harmony Approach

    NASA Astrophysics Data System (ADS)

    Kalsom Yusof, Umi; Nor Akmal Khalid, Mohd

    2015-05-01

    Semiconductor industries need to constantly adjust to the rapid pace of change in the market. Most manufactured products usually have a very short life cycle. These scenarios imply the need to improve the efficiency of capacity planning, an important aspect of the machine allocation plan known for its complexity. Various studies have been performed to balance productivity and flexibility in the flexible manufacturing system (FMS). Many approaches have been developed by the researchers to determine the suitable balance between exploration (global improvement) and exploitation (local improvement). However, not much work has been focused on the domain of machine allocation problem that considers the effects of machine breakdowns. This paper develops a model to minimize the effect of machine breakdowns, thus increasing the productivity. The objectives are to minimize system unbalance and makespan as well as increase throughput while satisfying the technological constraints such as machine time availability. To examine the effectiveness of the proposed model, results for throughput, system unbalance and makespan on real industrial datasets were performed with applications of intelligence techniques, that is, a hybrid of genetic algorithm and harmony search. The result aims to obtain a feasible solution to the domain problem.

  3. Production scheduling with discrete and renewable additional resources

    NASA Astrophysics Data System (ADS)

    Kalinowski, K.; Grabowik, C.; Paprocka, I.; Kempa, W.

    2015-11-01

    In this paper an approach to planning of additional resources when scheduling operations are discussed. The considered resources are assumed to be discrete and renewable. In most research in scheduling domain, the basic and often the only type of regarded resources is a workstation. It can be understood as a machine, a device or even as a separated space on the shop floor. In many cases, during the detailed scheduling of operations the need of using more than one resource, required for its implementation, can be indicated. Resource requirements for an operation may relate to different resources or resources of the same type. Additional resources are most often referred to these human resources, tools or equipment, for which the limited availability in the manufacturing system may have an influence on the execution dates of some operations. In the paper the concept of the division into basic and additional resources and their planning method was shown. A situation in which sets of basic and additional resources are not separable - the same additional resource may be a basic resource for another operation is also considered. Scheduling of operations, including greater amount of resources can cause many difficulties, depending on whether the resource is involved in the entire time of operation, only in the selected part(s) of operation (e.g. as auxiliary staff at setup time) or cyclic - e.g. when an operator supports more than one machine, or supervises the execution of several operations. For this reason the dates and work times of resources participation in the operation can be different. Presented issues are crucial when modelling of production scheduling environment and designing of structures for the purpose of scheduling software development.

  4. Job Scheduling with Efficient Resource Monitoring in Cloud Datacenter

    PubMed Central

    Loganathan, Shyamala; Mukherjee, Saswati

    2015-01-01

    Cloud computing is an on-demand computing model, which uses virtualization technology to provide cloud resources to users in the form of virtual machines through internet. Being an adaptable technology, cloud computing is an excellent alternative for organizations for forming their own private cloud. Since the resources are limited in these private clouds maximizing the utilization of resources and giving the guaranteed service for the user are the ultimate goal. For that, efficient scheduling is needed. This research reports on an efficient data structure for resource management and resource scheduling technique in a private cloud environment and discusses a cloud model. The proposed scheduling algorithm considers the types of jobs and the resource availability in its scheduling decision. Finally, we conducted simulations using CloudSim and compared our algorithm with other existing methods, like V-MCT and priority scheduling algorithms. PMID:26473166

  5. Job Scheduling with Efficient Resource Monitoring in Cloud Datacenter.

    PubMed

    Loganathan, Shyamala; Mukherjee, Saswati

    2015-01-01

    Cloud computing is an on-demand computing model, which uses virtualization technology to provide cloud resources to users in the form of virtual machines through internet. Being an adaptable technology, cloud computing is an excellent alternative for organizations for forming their own private cloud. Since the resources are limited in these private clouds maximizing the utilization of resources and giving the guaranteed service for the user are the ultimate goal. For that, efficient scheduling is needed. This research reports on an efficient data structure for resource management and resource scheduling technique in a private cloud environment and discusses a cloud model. The proposed scheduling algorithm considers the types of jobs and the resource availability in its scheduling decision. Finally, we conducted simulations using CloudSim and compared our algorithm with other existing methods, like V-MCT and priority scheduling algorithms.

  6. The nurse scheduling problem: a goal programming and nonlinear optimization approaches

    NASA Astrophysics Data System (ADS)

    Hakim, L.; Bakhtiar, T.; Jaharuddin

    2017-01-01

    Nurses scheduling is an activity of allocating nurses to conduct a set of tasks at certain room at a hospital or health centre within a certain period. One of obstacles in the nurse scheduling is the lack of resources in order to fulfil the needs of the hospital. Nurse scheduling which is undertaken manually will be at risk of not fulfilling some nursing rules set by the hospital. Therefore, this study aimed to perform scheduling models that satisfy all the specific rules set by the management of Bogor State Hospital. We have developed three models to overcome the scheduling needs. Model 1 is designed to schedule nurses who are solely assigned to a certain inpatient unit and Model 2 is constructed to manage nurses who are assigned to an inpatient room as well as at Polyclinic room as conjunct nurses. As the assignment of nurses on each shift is uneven, then we propose Model 3 to minimize the variance of the workload in order to achieve equitable assignment on every shift. The first two models are formulated in goal programming framework, while the last model is in nonlinear optimization form.

  7. Mission scheduling

    NASA Technical Reports Server (NTRS)

    Gaspin, Christine

    1989-01-01

    How a neural network can work, compared to a hybrid system based on an operations research and artificial intelligence approach, is investigated through a mission scheduling problem. The characteristic features of each system are discussed.

  8. A Network Flow Approach to the Initial Skills Training Scheduling Problem

    DTIC Science & Technology

    2007-12-01

    include (but are not limited to) queuing theory, stochastic analysis and simulation. After the demand schedule has been estimated, it can be ...software package has already been purchased and is in use by AFPC, AFPC has requested that the new algorithm be programmed in this language as well ...the discussed outputs from those schedules. Required Inputs A single input file details the students to be scheduled as well as the courses

  9. Optimization of municipal waste collection scheduling and routing using vehicle assignment problem (case study of Surabaya city waste collection)

    NASA Astrophysics Data System (ADS)

    Ramdhani, M. N.; Baihaqi, I.; Siswanto, N.

    2018-04-01

    Waste collection and disposal become a major problem for many metropolitan cities. Growing population, limited vehicles, and increased road traffic make the waste transportation become more complex. Waste collection involves some key considerations, such as vehicle assignment, vehicle routes, and vehicle scheduling. In the scheduling process, each vehicle has a scheduled departure that serve each route. Therefore, vehicle’s assignments should consider the time required to finish one assigment on that route. The objective of this study is to minimize the number of vehicles needed to serve all routes by developing a mathematical model which uses assignment problem approach. The first step is to generated possible routes from the existing routes, followed by vehicle assignments for those certain routes. The result of the model shows fewer vehicles required to perform waste collection asa well as the the number of journeys that the vehicle to collect the waste to the landfill. The comparison of existing conditions with the model result indicates that the latter’s has better condition than the existing condition because each vehicle with certain route has an equal workload, all the result’s model has the maximum of two journeys for each route.

  10. Bidirectional extreme learning machine for regression problem and its learning effectiveness.

    PubMed

    Yang, Yimin; Wang, Yaonan; Yuan, Xiaofang

    2012-09-01

    It is clear that the learning effectiveness and learning speed of neural networks are in general far slower than required, which has been a major bottleneck for many applications. Recently, a simple and efficient learning method, referred to as extreme learning machine (ELM), was proposed by Huang , which has shown that, compared to some conventional methods, the training time of neural networks can be reduced by a thousand times. However, one of the open problems in ELM research is whether the number of hidden nodes can be further reduced without affecting learning effectiveness. This brief proposes a new learning algorithm, called bidirectional extreme learning machine (B-ELM), in which some hidden nodes are not randomly selected. In theory, this algorithm tends to reduce network output error to 0 at an extremely early learning stage. Furthermore, we find a relationship between the network output error and the network output weights in the proposed B-ELM. Simulation results demonstrate that the proposed method can be tens to hundreds of times faster than other incremental ELM algorithms.

  11. Planning and Scheduling for Fleets of Earth Observing Satellites

    NASA Technical Reports Server (NTRS)

    Frank, Jeremy; Jonsson, Ari; Morris, Robert; Smith, David E.; Norvig, Peter (Technical Monitor)

    2001-01-01

    We address the problem of scheduling observations for a collection of earth observing satellites. This scheduling task is a difficult optimization problem, potentially involving many satellites, hundreds of requests, constraints on when and how to service each request, and resources such as instruments, recording devices, transmitters, and ground stations. High-fidelity models are required to ensure the validity of schedules; at the same time, the size and complexity of the problem makes it unlikely that systematic optimization search methods will be able to solve them in a reasonable time. This paper presents a constraint-based approach to solving the Earth Observing Satellites (EOS) scheduling problem, and proposes a stochastic heuristic search method for solving it.

  12. Scheduling for Parallel Supercomputing: A Historical Perspective of Achievable Utilization

    NASA Technical Reports Server (NTRS)

    Jones, James Patton; Nitzberg, Bill

    1999-01-01

    The NAS facility has operated parallel supercomputers for the past 11 years, including the Intel iPSC/860, Intel Paragon, Thinking Machines CM-5, IBM SP-2, and Cray Origin 2000. Across this wide variety of machine architectures, across a span of 10 years, across a large number of different users, and through thousands of minor configuration and policy changes, the utilization of these machines shows three general trends: (1) scheduling using a naive FIFO first-fit policy results in 40-60% utilization, (2) switching to the more sophisticated dynamic backfilling scheduling algorithm improves utilization by about 15 percentage points (yielding about 70% utilization), and (3) reducing the maximum allowable job size further increases utilization. Most surprising is the consistency of these trends. Over the lifetime of the NAS parallel systems, we made hundreds, perhaps thousands, of small changes to hardware, software, and policy, yet, utilization was affected little. In particular these results show that the goal of achieving near 100% utilization while supporting a real parallel supercomputing workload is unrealistic.

  13. Integrated scheduling and resource management. [for Space Station Information System

    NASA Technical Reports Server (NTRS)

    Ward, M. T.

    1987-01-01

    This paper examines the problem of integrated scheduling during the Space Station era. Scheduling for Space Station entails coordinating the support of many distributed users who are sharing common resources and pursuing individual and sometimes conflicting objectives. This paper compares the scheduling integration problems of current missions with those anticipated for the Space Station era. It examines the facilities and the proposed operations environment for Space Station. It concludes that the pattern of interdependecies among the users and facilities, which are the source of the integration problem is well structured, allowing a dividing of the larger problem into smaller problems. It proposes an architecture to support integrated scheduling by scheduling efficiently at local facilities as a function of dependencies with other facilities of the program. A prototype is described that is being developed to demonstrate this integration concept.

  14. Capacitated vehicle-routing problem model for scheduled solid waste collection and route optimization using PSO algorithm.

    PubMed

    Hannan, M A; Akhtar, Mahmuda; Begum, R A; Basri, H; Hussain, A; Scavino, Edgar

    2018-01-01

    Waste collection widely depends on the route optimization problem that involves a large amount of expenditure in terms of capital, labor, and variable operational costs. Thus, the more waste collection route is optimized, the more reduction in different costs and environmental effect will be. This study proposes a modified particle swarm optimization (PSO) algorithm in a capacitated vehicle-routing problem (CVRP) model to determine the best waste collection and route optimization solutions. In this study, threshold waste level (TWL) and scheduling concepts are applied in the PSO-based CVRP model under different datasets. The obtained results from different datasets show that the proposed algorithmic CVRP model provides the best waste collection and route optimization in terms of travel distance, total waste, waste collection efficiency, and tightness at 70-75% of TWL. The obtained results for 1 week scheduling show that 70% of TWL performs better than all node consideration in terms of collected waste, distance, tightness, efficiency, fuel consumption, and cost. The proposed optimized model can serve as a valuable tool for waste collection and route optimization toward reducing socioeconomic and environmental impacts. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Improving Energy Efficiency in CNC Machining

    NASA Astrophysics Data System (ADS)

    Pavanaskar, Sushrut S.

    We present our work on analyzing and improving the energy efficiency of multi-axis CNC milling process. Due to the differences in energy consumption behavior, we treat 3- and 5-axis CNC machines separately in our work. For 3-axis CNC machines, we first propose an energy model that estimates the energy requirement for machining a component on a specified 3-axis CNC milling machine. Our model makes machine-specific predictions of energy requirements while also considering the geometric aspects of the machining toolpath. Our model - and the associated software tool - facilitate direct comparison of various alternative toolpath strategies based on their energy-consumption performance. Further, we identify key factors in toolpath planning that affect energy consumption in CNC machining. We then use this knowledge to propose and demonstrate a novel toolpath planning strategy that may be used to generate new toolpaths that are inherently energy-efficient, inspired by research on digital micrography -- a form of computational art. For 5-axis CNC machines, the process planning problem consists of several sub-problems that researchers have traditionally solved separately to obtain an approximate solution. After illustrating the need to solve all sub-problems simultaneously for a truly optimal solution, we propose a unified formulation based on configuration space theory. We apply our formulation to solve a problem variant that retains key characteristics of the full problem but has lower dimensionality, allowing visualization in 2D. Given the complexity of the full 5-axis toolpath planning problem, our unified formulation represents an important step towards obtaining a truly optimal solution. With this work on the two types of CNC machines, we demonstrate that without changing the current infrastructure or business practices, machine-specific, geometry-based, customized toolpath planning can save energy in CNC machining.

  16. Vehicle and driver scheduling for public transit.

    DOT National Transportation Integrated Search

    2009-08-01

    The problem of driver scheduling involves the construction of a legal set of shifts, including allowance : of overtime, which cover the blocks in a particular vehicle schedule. A shift is the work scheduled to be performed by : a driver in one day, w...

  17. Improving Resource Selection and Scheduling Using Predictions. Chapter 1

    NASA Technical Reports Server (NTRS)

    Smith, Warren

    2003-01-01

    The introduction of computational grids has resulted in several new problems in the area of scheduling that can be addressed using predictions. The first problem is selecting where to run an application on the many resources available in a grid. Our approach to help address this problem is to provide predictions of when an application would start to execute if submitted to specific scheduled computer systems. The second problem is gaining simultaneous access to multiple computer systems so that distributed applications can be executed. We help address this problem by investigating how to support advance reservations in local scheduling systems. Our approaches to both of these problems are based on predictions for the execution time of applications on space- shared parallel computers. As a side effect of this work, we also discuss how predictions of application run times can be used to improve scheduling performance.

  18. Empirical results on scheduling and dynamic backtracking

    NASA Technical Reports Server (NTRS)

    Boddy, Mark S.; Goldman, Robert P.

    1994-01-01

    At the Honeywell Technology Center (HTC), we have been working on a scheduling problem related to commercial avionics. This application is large, complex, and hard to solve. To be a little more concrete: 'large' means almost 20,000 activities, 'complex' means several activity types, periodic behavior, and assorted types of temporal constraints, and 'hard to solve' means that we have been unable to eliminate backtracking through the use of search heuristics. At this point, we can generate solutions, where solutions exist, or report failure and sometimes why the system failed. To the best of our knowledge, this is among the largest and most complex scheduling problems to have been solved as a constraint satisfaction problem, at least that has appeared in the published literature. This abstract is a preliminary report on what we have done and how. In the next section, we present our approach to treating scheduling as a constraint satisfaction problem. The following sections present the application in more detail and describe how we solve scheduling problems in the application domain. The implemented system makes use of Ginsberg's Dynamic Backtracking algorithm, with some minor extensions to improve its utility for scheduling. We describe those extensions and the performance of the resulting system. The paper concludes with some general remarks, open questions and plans for future work.

  19. Schedule Matters: Understanding the Relationship between Schedule Delays and Costs on Overruns

    NASA Technical Reports Server (NTRS)

    Majerowicz, Walt; Shinn, Stephen A.

    2016-01-01

    This paper examines the relationship between schedule delays and cost overruns on complex projects. It is generally accepted by many project practitioners that cost overruns are directly related to schedule delays. But what does "directly related to" actually mean? Some reasons or root causes for schedule delays and associated cost overruns are obvious, if only in hindsight. For example, unrealistic estimates, supply chain difficulties, insufficient schedule margin, technical problems, scope changes, or the occurrence of risk events can negatively impact schedule performance. Other factors driving schedule delays and cost overruns may be less obvious and more difficult to quantify. Examples of these less obvious factors include project complexity, flawed estimating assumptions, over-optimism, political factors, "black swan" events, or even poor leadership and communication. Indeed, is it even possible the schedule itself could be a source of delay and subsequent cost overrun? Through literature review, surveys of project practitioners, and the authors' own experience on NASA programs and projects, the authors will categorize and examine the various factors affecting the relationship between project schedule delays and cost growth. The authors will also propose some ideas for organizations to consider to help create an awareness of the factors which could cause or influence schedule delays and associated cost growth on complex projects.

  20. Machine Learning.

    ERIC Educational Resources Information Center

    Kirrane, Diane E.

    1990-01-01

    As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

  1. Scheduling Future Water Supply Investments Under Uncertainty

    NASA Astrophysics Data System (ADS)

    Huskova, I.; Matrosov, E. S.; Harou, J. J.; Kasprzyk, J. R.; Reed, P. M.

    2014-12-01

    Uncertain hydrological impacts of climate change, population growth and institutional changes pose a major challenge to planning of water supply systems. Planners seek optimal portfolios of supply and demand management schemes but also when to activate assets whilst considering many system goals and plausible futures. Incorporation of scheduling into the planning under uncertainty problem strongly increases its complexity. We investigate some approaches to scheduling with many-objective heuristic search. We apply a multi-scenario many-objective scheduling approach to the Thames River basin water supply system planning problem in the UK. Decisions include which new supply and demand schemes to implement, at what capacity and when. The impact of different system uncertainties on scheme implementation schedules are explored, i.e. how the choice of future scenarios affects the search process and its outcomes. The activation of schemes is influenced by the occurrence of extreme hydrological events in the ensemble of plausible scenarios and other factors. The approach and results are compared with a previous study where only the portfolio problem is addressed (without scheduling).

  2. Designing a fuzzy scheduler for hard real-time systems

    NASA Technical Reports Server (NTRS)

    Yen, John; Lee, Jonathan; Pfluger, Nathan; Natarajan, Swami

    1992-01-01

    In hard real-time systems, tasks have to be performed not only correctly, but also in a timely fashion. If timing constraints are not met, there might be severe consequences. Task scheduling is the most important problem in designing a hard real-time system, because the scheduling algorithm ensures that tasks meet their deadlines. However, the inherent nature of uncertainty in dynamic hard real-time systems increases the problems inherent in scheduling. In an effort to alleviate these problems, we have developed a fuzzy scheduler to facilitate searching for a feasible schedule. A set of fuzzy rules are proposed to guide the search. The situation we are trying to address is the performance of the system when no feasible solution can be found, and therefore, certain tasks will not be executed. We wish to limit the number of important tasks that are not scheduled.

  3. Decision theory for computing variable and value ordering decisions for scheduling problems

    NASA Technical Reports Server (NTRS)

    Linden, Theodore A.

    1993-01-01

    Heuristics that guide search are critical when solving large planning and scheduling problems, but most variable and value ordering heuristics are sensitive to only one feature of the search state. One wants to combine evidence from all features of the search state into a subjective probability that a value choice is best, but there has been no solid semantics for merging evidence when it is conceived in these terms. Instead, variable and value ordering decisions should be viewed as problems in decision theory. This led to two key insights: (1) The fundamental concept that allows heuristic evidence to be merged is the net incremental utility that will be achieved by assigning a value to a variable. Probability distributions about net incremental utility can merge evidence from the utility function, binary constraints, resource constraints, and other problem features. The subjective probability that a value is the best choice is then derived from probability distributions about net incremental utility. (2) The methods used for rumor control in Bayesian Networks are the primary way to prevent cycling in the computation of probable net incremental utility. These insights lead to semantically justifiable ways to compute heuristic variable and value ordering decisions that merge evidence from all available features of the search state.

  4. Future aircraft networks and schedules

    NASA Astrophysics Data System (ADS)

    Shu, Yan

    2011-07-01

    Because of the importance of air transportation scheduling, the emergence of small aircraft and the vision of future fuel-efficient aircraft, this thesis has focused on the study of aircraft scheduling and network design involving multiple types of aircraft and flight services. It develops models and solution algorithms for the schedule design problem and analyzes the computational results. First, based on the current development of small aircraft and on-demand flight services, this thesis expands a business model for integrating on-demand flight services with the traditional scheduled flight services. This thesis proposes a three-step approach to the design of aircraft schedules and networks from scratch under the model. In the first step, both a frequency assignment model for scheduled flights that incorporates a passenger path choice model and a frequency assignment model for on-demand flights that incorporates a passenger mode choice model are created. In the second step, a rough fleet assignment model that determines a set of flight legs, each of which is assigned an aircraft type and a rough departure time is constructed. In the third step, a timetable model that determines an exact departure time for each flight leg is developed. Based on the models proposed in the three steps, this thesis creates schedule design instances that involve almost all the major airports and markets in the United States. The instances of the frequency assignment model created in this thesis are large-scale non-convex mixed-integer programming problems, and this dissertation develops an overall network structure and proposes iterative algorithms for solving these instances. The instances of both the rough fleet assignment model and the timetable model created in this thesis are large-scale mixed-integer programming problems, and this dissertation develops subproblem schemes for solving these instances. Based on these solution algorithms, this dissertation also presents

  5. Dawn Usage, Scheduling, and Governance Model

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Louis, S

    2009-11-02

    This document describes Dawn use, scheduling, and governance concerns. Users started running full-machine science runs in early April 2009 during the initial open shakedown period. Scheduling Dawn while in the Open Computing Facility (OCF) was controlled and coordinated via phone calls, emails, and a small number of controlled banks. With Dawn moving to the Secure Computing Facility (SCF) in fall of 2009, a more detailed scheduling and governance model is required. The three major objectives are: (1) Ensure Dawn resources are allocated on a program priority-driven basis; (2) Utilize Dawn resources on the job mixes for which they were intended;more » and (3) Minimize idle cycles through use of partitions, banks and proper job mix. The SCF workload for Dawn will be inherently different than Purple or BG/L, and therefore needs a different approach. Dawn's primary function is to permit adequate access for tri-lab code development in preparation for Sequoia, and in particular for weapons multi-physics codes in support of UQ. A second purpose is to provide time allocations for large-scale science runs and for UQ suite calculations to advance SSP program priorities. This proposed governance model will be the basis for initial time allocation of Dawn computing resources for the science and UQ workloads that merit priority on this class of resource, either because they cannot be reasonably attempted on any other resources due to size of problem, or because of the unavailability of sizable allocations on other ASC capability or capacity platforms. This proposed model intends to make the most effective use of Dawn as possible, but without being overly constrained by more formal proposal processes such as those now used for Purple CCCs.« less

  6. A new scheduling algorithm for parallel sparse LU factorization with static pivoting

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Grigori, Laura; Li, Xiaoye S.

    2002-08-20

    In this paper we present a static scheduling algorithm for parallel sparse LU factorization with static pivoting. The algorithm is divided into mapping and scheduling phases, using the symmetric pruned graphs of L' and U to represent dependencies. The scheduling algorithm is designed for driving the parallel execution of the factorization on a distributed-memory architecture. Experimental results and comparisons with SuperLU{_}DIST are reported after applying this algorithm on real world application matrices on an IBM SP RS/6000 distributed memory machine.

  7. Multi-Objective Scheduling for the Cluster II Constellation

    NASA Technical Reports Server (NTRS)

    Johnston, Mark D.; Giuliano, Mark

    2011-01-01

    This paper describes the application of the MUSE multiobjecctive scheduling framework to the Cluster II WBD scheduling domain. Cluster II is an ESA four-spacecraft constellation designed to study the plasma environment of the Earth and it's magnetosphere. One of the instruments on each of the four spacecraft is the Wide Band Data (WBD) plasma wave experiment. We have applied the MUSE evolutionary algorithm to the scheduling problem represented by this instrument, and the result has been adopted and utilized by the WBD schedulers for nearly a year. This paper describes the WBD scheduling problem, its representation in MUSE, and some of the visualization elements that provide insight into objective value tradeoffs.

  8. Preliminary Evaluation of BIM-based Approaches for Schedule Delay Analysis

    NASA Astrophysics Data System (ADS)

    Chou, Hui-Yu; Yang, Jyh-Bin

    2017-10-01

    The problem of schedule delay commonly occurs in construction projects. The quality of delay analysis depends on the availability of schedule-related information and delay evidence. More information used in delay analysis usually produces more accurate and fair analytical results. How to use innovative techniques to improve the quality of schedule delay analysis results have received much attention recently. As Building Information Modeling (BIM) technique has been quickly developed, using BIM and 4D simulation techniques have been proposed and implemented. Obvious benefits have been achieved especially in identifying and solving construction consequence problems in advance of construction. This study preforms an intensive literature review to discuss the problems encountered in schedule delay analysis and the possibility of using BIM as a tool in developing a BIM-based approach for schedule delay analysis. This study believes that most of the identified problems can be dealt with by BIM technique. Research results could be a fundamental of developing new approaches for resolving schedule delay disputes.

  9. Cost-efficient scheduling of FAST observations

    NASA Astrophysics Data System (ADS)

    Luo, Qi; Zhao, Laiping; Yu, Ce; Xiao, Jian; Sun, Jizhou; Zhu, Ming; Zhong, Yi

    2018-03-01

    A cost-efficient schedule for the Five-hundred-meter Aperture Spherical radio Telescope (FAST) requires to maximize the number of observable proposals and the overall scientific priority, and minimize the overall slew-cost generated by telescope shifting, while taking into account the constraints including the astronomical objects visibility, user-defined observable times, avoiding Radio Frequency Interference (RFI). In this contribution, first we solve the problem of maximizing the number of observable proposals and scientific priority by modeling it as a Minimum Cost Maximum Flow (MCMF) problem. The optimal schedule can be found by any MCMF solution algorithm. Then, for minimizing the slew-cost of the generated schedule, we devise a maximally-matchable edges detection-based method to reduce the problem size, and propose a backtracking algorithm to find the perfect matching with minimum slew-cost. Experiments on a real dataset from NASA/IPAC Extragalactic Database (NED) show that, the proposed scheduler can increase the usage of available times with high scientific priority and reduce the slew-cost significantly in a very short time.

  10. A Decentralized Scheduling Policy for a Dynamically Reconfigurable Production System

    NASA Astrophysics Data System (ADS)

    Giordani, Stefano; Lujak, Marin; Martinelli, Francesco

    In this paper, the static layout of a traditional multi-machine factory producing a set of distinct goods is integrated with a set of mobile production units - robots. The robots dynamically change their work position to increment the product rate of the different typologies of products in respect to the fluctuations of the demands and production costs during a given time horizon. Assuming that the planning time horizon is subdivided into a finite number of time periods, this particularly flexible layout requires the definition and the solution of a complex scheduling problem, involving for each period of the planning time horizon, the determination of the position of the robots, i.e., the assignment to the respective tasks in order to minimize production costs given the product demand rates during the planning time horizon.

  11. The checkpoint ordering problem

    PubMed Central

    Hungerländer, P.

    2017-01-01

    Abstract We suggest a new variant of a row layout problem: Find an ordering of n departments with given lengths such that the total weighted sum of their distances to a given checkpoint is minimized. The Checkpoint Ordering Problem (COP) is both of theoretical and practical interest. It has several applications and is conceptually related to some well-studied combinatorial optimization problems, namely the Single-Row Facility Layout Problem, the Linear Ordering Problem and a variant of parallel machine scheduling. In this paper we study the complexity of the (COP) and its special cases. The general version of the (COP) with an arbitrary but fixed number of checkpoints is NP-hard in the weak sense. We propose both a dynamic programming algorithm and an integer linear programming approach for the (COP) . Our computational experiments indicate that the (COP) is hard to solve in practice. While the run time of the dynamic programming algorithm strongly depends on the length of the departments, the integer linear programming approach is able to solve instances with up to 25 departments to optimality. PMID:29170574

  12. Scheduling with genetic algorithms

    NASA Technical Reports Server (NTRS)

    Fennel, Theron R.; Underbrink, A. J., Jr.; Williams, George P. W., Jr.

    1994-01-01

    In many domains, scheduling a sequence of jobs is an important function contributing to the overall efficiency of the operation. At Boeing, we develop schedules for many different domains, including assembly of military and commercial aircraft, weapons systems, and space vehicles. Boeing is under contract to develop scheduling systems for the Space Station Payload Planning System (PPS) and Payload Operations and Integration Center (POIC). These applications require that we respect certain sequencing restrictions among the jobs to be scheduled while at the same time assigning resources to the jobs. We call this general problem scheduling and resource allocation. Genetic algorithms (GA's) offer a search method that uses a population of solutions and benefits from intrinsic parallelism to search the problem space rapidly, producing near-optimal solutions. Good intermediate solutions are probabalistically recombined to produce better offspring (based upon some application specific measure of solution fitness, e.g., minimum flowtime, or schedule completeness). Also, at any point in the search, any intermediate solution can be accepted as a final solution; allowing the search to proceed longer usually produces a better solution while terminating the search at virtually any time may yield an acceptable solution. Many processes are constrained by restrictions of sequence among the individual jobs. For a specific job, other jobs must be completed beforehand. While there are obviously many other constraints on processes, it is these on which we focussed for this research: how to allocate crews to jobs while satisfying job precedence requirements and personnel, and tooling and fixture (or, more generally, resource) requirements.

  13. 49 CFR 214.531 - Schedule of repairs; general.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... Hi-Rail Vehicles § 214.531 Schedule of repairs; general. Except as provided in §§ 214.527(c)(5), 214.529, and 214.533, an on-track roadway maintenance machine or hi-rail vehicle that does not meet all... or hi-rail vehicle shall be placed out of on-track service. ...

  14. An innovative artificial bee colony algorithm and its application to a practical intercell scheduling problem

    NASA Astrophysics Data System (ADS)

    Li, Dongni; Guo, Rongtao; Zhan, Rongxin; Yin, Yong

    2018-06-01

    In this article, an innovative artificial bee colony (IABC) algorithm is proposed, which incorporates two mechanisms. On the one hand, to provide the evolutionary process with a higher starting level, genetic programming (GP) is used to generate heuristic rules by exploiting the elements that constitute the problem. On the other hand, to achieve a better balance between exploration and exploitation, a leading mechanism is proposed to attract individuals towards a promising region. To evaluate the performance of IABC in solving practical and complex problems, it is applied to the intercell scheduling problem with limited transportation capacity. It is observed that the GP-generated rules incorporate the elements of the most competing human-designed rules, and they are more effective than the human-designed ones. Regarding the leading mechanism, the strategies of the ageing leader and multiple challengers make the algorithm less likely to be trapped in local optima.

  15. Job Scheduling in a Heterogeneous Grid Environment

    NASA Technical Reports Server (NTRS)

    Shan, Hong-Zhang; Smith, Warren; Oliker, Leonid; Biswas, Rupak

    2004-01-01

    Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. However, a number of major technical hurdles must be overcome before this potential can be realized. One problem that is critical to effective utilization of computational grids is the efficient scheduling of jobs. This work addresses this problem by describing and evaluating a grid scheduling architecture and three job migration algorithms. The architecture is scalable and does not assume control of local site resources. The job migration policies use the availability and performance of computer systems, the network bandwidth available between systems, and the volume of input and output data associated with each job. An extensive performance comparison is presented using real workloads from leading computational centers. The results, based on several key metrics, demonstrate that the performance of our distributed migration algorithms is significantly greater than that of a local scheduling framework and comparable to a non-scalable global scheduling approach.

  16. High performance techniques for space mission scheduling

    NASA Technical Reports Server (NTRS)

    Smith, Stephen F.

    1994-01-01

    In this paper, we summarize current research at Carnegie Mellon University aimed at development of high performance techniques and tools for space mission scheduling. Similar to prior research in opportunistic scheduling, our approach assumes the use of dynamic analysis of problem constraints as a basis for heuristic focusing of problem solving search. This methodology, however, is grounded in representational assumptions more akin to those adopted in recent temporal planning research, and in a problem solving framework which similarly emphasizes constraint posting in an explicitly maintained solution constraint network. These more general representational assumptions are necessitated by the predominance of state-dependent constraints in space mission planning domains, and the consequent need to integrate resource allocation and plan synthesis processes. First, we review the space mission problems we have considered to date and indicate the results obtained in these application domains. Next, we summarize recent work in constraint posting scheduling procedures, which offer the promise of better future solutions to this class of problems.

  17. Shiftwork Scheduling for the 1990s.

    ERIC Educational Resources Information Center

    Coleman, Richard M.

    1989-01-01

    The author discusses the problems of scheduling shift work, touching on such topics as employee desires, health requirements, and business needs. He presents a method for developing shift schedules that addresses these three areas. Implementation hints are also provided. (CH)

  18. Polynomial-time solution of prime factorization and NP-complete problems with digital memcomputing machines

    NASA Astrophysics Data System (ADS)

    Traversa, Fabio L.; Di Ventra, Massimiliano

    2017-02-01

    We introduce a class of digital machines, we name Digital Memcomputing Machines, (DMMs) able to solve a wide range of problems including Non-deterministic Polynomial (NP) ones with polynomial resources (in time, space, and energy). An abstract DMM with this power must satisfy a set of compatible mathematical constraints underlying its practical realization. We prove this by making a connection with the dynamical systems theory. This leads us to a set of physical constraints for poly-resource resolvability. Once the mathematical requirements have been assessed, we propose a practical scheme to solve the above class of problems based on the novel concept of self-organizing logic gates and circuits (SOLCs). These are logic gates and circuits able to accept input signals from any terminal, without distinction between conventional input and output terminals. They can solve boolean problems by self-organizing into their solution. They can be fabricated either with circuit elements with memory (such as memristors) and/or standard MOS technology. Using tools of functional analysis, we prove mathematically the following constraints for the poly-resource resolvability: (i) SOLCs possess a global attractor; (ii) their only equilibrium points are the solutions of the problems to solve; (iii) the system converges exponentially fast to the solutions; (iv) the equilibrium convergence rate scales at most polynomially with input size. We finally provide arguments that periodic orbits and strange attractors cannot coexist with equilibria. As examples, we show how to solve the prime factorization and the search version of the NP-complete subset-sum problem. Since DMMs map integers into integers, they are robust against noise and hence scalable. We finally discuss the implications of the DMM realization through SOLCs to the NP = P question related to constraints of poly-resources resolvability.

  19. Quantum-Enhanced Machine Learning

    NASA Astrophysics Data System (ADS)

    Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J.

    2016-09-01

    The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.

  20. Scheduling algorithms for automatic control systems for technological processes

    NASA Astrophysics Data System (ADS)

    Chernigovskiy, A. S.; Tsarev, R. Yu; Kapulin, D. V.

    2017-01-01

    Wide use of automatic process control systems and the usage of high-performance systems containing a number of computers (processors) give opportunities for creation of high-quality and fast production that increases competitiveness of an enterprise. Exact and fast calculations, control computation, and processing of the big data arrays - all of this requires the high level of productivity and, at the same time, minimum time of data handling and result receiving. In order to reach the best time, it is necessary not only to use computing resources optimally, but also to design and develop the software so that time gain will be maximal. For this purpose task (jobs or operations), scheduling techniques for the multi-machine/multiprocessor systems are applied. Some of basic task scheduling methods for the multi-machine process control systems are considered in this paper, their advantages and disadvantages come to light, and also some usage considerations, in case of the software for automatic process control systems developing, are made.

  1. Solving a mathematical model integrating unequal-area facilities layout and part scheduling in a cellular manufacturing system by a genetic algorithm.

    PubMed

    Ebrahimi, Ahmad; Kia, Reza; Komijan, Alireza Rashidi

    2016-01-01

    In this article, a novel integrated mixed-integer nonlinear programming model is presented for designing a cellular manufacturing system (CMS) considering machine layout and part scheduling problems simultaneously as interrelated decisions. The integrated CMS model is formulated to incorporate several design features including part due date, material handling time, operation sequence, processing time, an intra-cell layout of unequal-area facilities, and part scheduling. The objective function is to minimize makespan, tardiness penalties, and material handling costs of inter-cell and intra-cell movements. Two numerical examples are solved by the Lingo software to illustrate the results obtained by the incorporated features. In order to assess the effects and importance of integration of machine layout and part scheduling in designing a CMS, two approaches, sequentially and concurrent are investigated and the improvement resulted from a concurrent approach is revealed. Also, due to the NP-hardness of the integrated model, an efficient genetic algorithm is designed. As a consequence, computational results of this study indicate that the best solutions found by GA are better than the solutions found by B&B in much less time for both sequential and concurrent approaches. Moreover, the comparisons between the objective function values (OFVs) obtained by sequential and concurrent approaches demonstrate that the OFV improvement is averagely around 17 % by GA and 14 % by B&B.

  2. Cue-Reactive Altered State of Consciousness Mediates the Relationship Between Problem-Gambling Severity and Cue-Reactive Urge in Poker-Machine Gamblers.

    PubMed

    Tricker, Christopher; Rock, Adam J; Clark, Gavin I

    2016-06-01

    In order to enhance our understanding of the nature of poker-machine problem-gambling, a community sample of 37 poker-machine gamblers (M age = 32 years, M PGSI = 5; PGSI = Problem Gambling Severity Index) were assessed for urge to gamble (responses on a visual analogue scale) and altered state of consciousness (assessed by the Altered State of Awareness dimension of the Phenomenology of Consciousness Inventory) at baseline, after a neutral cue, and after a gambling cue. It was found that (a) problem-gambling severity (PGSI score) predicted increase in urge (from neutral cue to gambling cue, controlling for baseline; sr (2) = .19, p = .006) and increase in altered state of consciousness (from neutral cue to gambling cue, controlling for baseline; sr (2) = .57, p < .001), and (b) increase in altered state of consciousness (from neutral cue to gambling cue) mediated the relationship between problem-gambling severity and increase in urge (from neutral cue to gambling cue; κ(2) = .40, 99 % CI [.08, .71]). These findings suggest that cue-reactive altered state of consciousness is an important component of cue-reactive urge in poker-machine problem-gamblers.

  3. Improving Hospital-wide Patient Scheduling Decisions by Clinical Pathway Mining.

    PubMed

    Gartner, Daniel; Arnolds, Ines V; Nickel, Stefan

    2015-01-01

    Recent research has highlighted the need for solving hospital-wide patient scheduling problems. Inpatient scheduling, patient activities have to be scheduled on scarce hospital resources such that temporal relations between activities (e.g. for recovery times) are ensured. Common objectives are, among others, the minimization of the length of stay (LOS). In this paper, we consider a hospital-wide patient scheduling problem with LOS minimization based on uncertain clinical pathways. We approach the problem in three stages: First, we learn most likely clinical pathways using a sequential pattern mining approach. Second, we provide a mathematical model for patient scheduling and finally, we combine the two approaches. In an experimental study carried out using real-world data, we show that our approach outperforms baseline approaches on two metrics.

  4. Binary Trees and Parallel Scheduling Algorithms.

    DTIC Science & Technology

    1980-09-01

    been pro- cessed for p. time units. If a job does not complete by its due time, it is tardy. In a nonpreemptive schedule, job i is scheduled to process...the preemptive schedule obtained by the algorithm of section 2.1.2 also minimizes 5Ti, this problem is easily solved in parallel. When lci is to e...August 1978, pp. 657-661. 14. Horn, W. A., "Some simple scheduling algorithms," Naval Res. Logist . Qur., Vol. 21, pp. 177-185, 1974. i5. Hforowitz, E

  5. Multiresource allocation and scheduling for periodic soft real-time applications

    NASA Astrophysics Data System (ADS)

    Gopalan, Kartik; Chiueh, Tzi-cker

    2001-12-01

    Real-time applications that utilize multiple system resources, such as CPU, disks, and network links, require coordinated scheduling of these resources in order to meet their end-to-end performance requirements. Most state-of-the-art operating systems support independent resource allocation and deadline-driven scheduling but lack coordination among multiple heterogeneous resources. This paper describes the design and implementation of an Integrated Real-time Resource Scheduler (IRS) that performs coordinated allocation and scheduling of multiple heterogeneous resources on the same machine for periodic soft real-time application. The principal feature of IRS is a heuristic multi-resource allocation algorithm that reserves multiple resources for real-time applications in a manner that can maximize the number of applications admitted into the system in the long run. At run-time, a global scheduler dispatches the tasks of the soft real-time application to individual resource schedulers according to the precedence constraints between tasks. The individual resource schedulers, which could be any deadline based schedulers, can make scheduling decisions locally and yet collectively satisfy a real-time application's performance requirements. The tightness of overall timing guarantees is ultimately determined by the properties of individual resource schedulers. However, IRS maximizes overall system resource utilization efficiency by coordinating deadline assignment across multiple tasks in a soft real-time application.

  6. IESIP - AN IMPROVED EXPLORATORY SEARCH TECHNIQUE FOR PURE INTEGER LINEAR PROGRAMMING PROBLEMS

    NASA Technical Reports Server (NTRS)

    Fogle, F. R.

    1994-01-01

    IESIP, an Improved Exploratory Search Technique for Pure Integer Linear Programming Problems, addresses the problem of optimizing an objective function of one or more variables subject to a set of confining functions or constraints by a method called discrete optimization or integer programming. Integer programming is based on a specific form of the general linear programming problem in which all variables in the objective function and all variables in the constraints are integers. While more difficult, integer programming is required for accuracy when modeling systems with small numbers of components such as the distribution of goods, machine scheduling, and production scheduling. IESIP establishes a new methodology for solving pure integer programming problems by utilizing a modified version of the univariate exploratory move developed by Robert Hooke and T.A. Jeeves. IESIP also takes some of its technique from the greedy procedure and the idea of unit neighborhoods. A rounding scheme uses the continuous solution found by traditional methods (simplex or other suitable technique) and creates a feasible integer starting point. The Hook and Jeeves exploratory search is modified to accommodate integers and constraints and is then employed to determine an optimal integer solution from the feasible starting solution. The user-friendly IESIP allows for rapid solution of problems up to 10 variables in size (limited by DOS allocation). Sample problems compare IESIP solutions with the traditional branch-and-bound approach. IESIP is written in Borland's TURBO Pascal for IBM PC series computers and compatibles running DOS. Source code and an executable are provided. The main memory requirement for execution is 25K. This program is available on a 5.25 inch 360K MS DOS format diskette. IESIP was developed in 1990. IBM is a trademark of International Business Machines. TURBO Pascal is registered by Borland International.

  7. Scheduling multirobot operations in manufacturing by truncated Petri nets

    NASA Astrophysics Data System (ADS)

    Chen, Qin; Luh, J. Y.

    1995-08-01

    Scheduling of operational sequences in manufacturing processes is one of the important problems in automation. Methods of applying Petri nets to model and analyze the problem with constraints on precedence relations, multiple resources allocation, etc. have been available in literature. Searching for an optimum schedule can be implemented by combining the branch-and-bound technique with the execution of the timed Petri net. The process usually produces a large Petri net which is practically not manageable. This disadvantage, however, can be handled by a truncation technique which divides the original large Petri net into several smaller size subnets. The complexity involved in the analysis of each subnet individually is greatly reduced. However, when the locally optimum schedules of the resulting subnets are combined together, it may not yield an overall optimum schedule for the original Petri net. To circumvent this problem, algorithms are developed based on the concepts of Petri net execution and modified branch-and-bound process. The developed technique is applied to a multi-robot task scheduling problem of the manufacturing work cell.

  8. Coordinating space telescope operations in an integrated planning and scheduling architecture

    NASA Technical Reports Server (NTRS)

    Muscettola, Nicola; Smith, Stephen F.; Cesta, Amedeo; D'Aloisi, Daniela

    1992-01-01

    The Heuristic Scheduling Testbed System (HSTS), a software architecture for integrated planning and scheduling, is discussed. The architecture has been applied to the problem of generating observation schedules for the Hubble Space Telescope. This problem is representative of the class of problems that can be addressed: their complexity lies in the interaction of resource allocation and auxiliary task expansion. The architecture deals with this interaction by viewing planning and scheduling as two complementary aspects of the more general process of constructing behaviors of a dynamical system. The principal components of the software architecture are described, indicating how to model the structure and dynamics of a system, how to represent schedules at multiple levels of abstraction in the temporal database, and how the problem solving machinery operates. A scheduler for the detailed management of Hubble Space Telescope operations that has been developed within HSTS is described. Experimental performance results are given that indicate the utility and practicality of the approach.

  9. A Comparison of Techniques for Scheduling Fleets of Earth-Observing Satellites

    NASA Technical Reports Server (NTRS)

    Globus, Al; Crawford, James; Lohn, Jason; Pryor, Anna

    2003-01-01

    Earth observing satellite (EOS) scheduling is a complex real-world domain representative of a broad class of over-subscription scheduling problems. Over-subscription problems are those where requests for a facility exceed its capacity. These problems arise in a wide variety of NASA and terrestrial domains and are .XI important class of scheduling problems because such facilities often represent large capital investments. We have run experiments comparing multiple variants of the genetic algorithm, hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on two variants of a realistically-sized model of the EOS scheduling problem. These are implemented as permutation-based methods; methods that search in the space of priority orderings of observation requests and evaluate each permutation by using it to drive a greedy scheduler. Simulated annealing performs best and random mutation operators outperform our squeaky (more intelligent) operator. Furthermore, taking smaller steps towards the end of the search improves performance.

  10. Predit: A temporal predictive framework for scheduling systems

    NASA Technical Reports Server (NTRS)

    Paolucci, E.; Patriarca, E.; Sem, M.; Gini, G.

    1992-01-01

    Scheduling can be formalized as a Constraint Satisfaction Problem (CSP). Within this framework activities belonging to a plan are interconnected via temporal constraints that account for slack among them. Temporal representation must include methods for constraints propagation and provide a logic for symbolic and numerical deductions. In this paper we describe a support framework for opportunistic reasoning in constraint directed scheduling. In order to focus the attention of an incremental scheduler on critical problem aspects, some discrete temporal indexes are presented. They are also useful for the prediction of the degree of resources contention. The predictive method expressed through our indexes can be seen as a Knowledge Source for an opportunistic scheduler with a blackboard architecture.

  11. Scheduling and control strategies for the departure problem in air traffic control

    NASA Astrophysics Data System (ADS)

    Bolender, Michael Alan

    Two problems relating to the departure problem in air traffic control automation are examined. The first problem that is addressed is the scheduling of aircraft for departure. The departure operations at a major US hub airport are analyzed, and a discrete event simulation of the departure operations is constructed. Specifically, the case where there is a single departure runway is considered. The runway is fed by two queues of aircraft. Each queue, in turn, is fed by a single taxiway. Two salient areas regarding scheduling are addressed. The first is the construction of optimal departure sequences for the aircraft that are queued. Several greedy search algorithms are designed to minimize the total time to depart a set of queued aircraft. Each algorithm has a different set of heuristic rules to resolve situations within the search space whenever two branches of the search tree with equal edge costs are encountered. These algorithms are then compared and contrasted with a genetic search algorithm in order to assess the performance of the heuristics. This is done in the context of a static departure problem where the length of the departure queue is fixed. A greedy algorithm which deepens the search whenever two branches of the search tree with non-unique costs are encountered is shown to outperform the other heuristic algorithms. This search strategy is then implemented in the discrete event simulation. A baseline performance level is established, and a sensitivity analysis is performed by implementing changes in traffic mix, routing, and miles-in-trail restrictions for comparison. It is concluded that to minimize the average time spent in the queue for different traffic conditions, a queue assignment algorithm is needed to maintain an even balance of aircraft in the queues. A necessary consideration is to base queue assignment upon traffic management restrictions such as miles-in-trail constraints. The second problem addresses the technical challenges associated

  12. 30 CFR 18.97 - Inspection of machines; minimum requirements.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... all electrical components for materials, workmanship, design, and construction; (2) Examination of all components of the machine which have been approved or certified under Bureau of Mines Schedule 2D, 2E, 2F, or...

  13. 30 CFR 18.97 - Inspection of machines; minimum requirements.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... all electrical components for materials, workmanship, design, and construction; (2) Examination of all components of the machine which have been approved or certified under Bureau of Mines Schedule 2D, 2E, 2F, or...

  14. 30 CFR 18.97 - Inspection of machines; minimum requirements.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... all electrical components for materials, workmanship, design, and construction; (2) Examination of all components of the machine which have been approved or certified under Bureau of Mines Schedule 2D, 2E, 2F, or...

  15. Steps Toward Optimal Competitive Scheduling

    NASA Technical Reports Server (NTRS)

    Frank, Jeremy; Crawford, James; Khatib, Lina; Brafman, Ronen

    2006-01-01

    This paper is concerned with the problem of allocating a unit capacity resource to multiple users within a pre-defined time period. The resource is indivisible, so that at most one user can use it at each time instance. However, different users may use it at different times. The users have independent, se@sh preferences for when and for how long they are allocated this resource. Thus, they value different resource access durations differently, and they value different time slots differently. We seek an optimal allocation schedule for this resource. This problem arises in many institutional settings where, e.g., different departments, agencies, or personal, compete for a single resource. We are particularly motivated by the problem of scheduling NASA's Deep Space Satellite Network (DSN) among different users within NASA. Access to DSN is needed for transmitting data from various space missions to Earth. Each mission has different needs for DSN time, depending on satellite and planetary orbits. Typically, the DSN is over-subscribed, in that not all missions will be allocated as much time as they want. This leads to various inefficiencies - missions spend much time and resource lobbying for their time, often exaggerating their needs. NASA, on the other hand, would like to make optimal use of this resource, ensuring that the good for NASA is maximized. This raises the thorny problem of how to measure the utility to NASA of each allocation. In the typical case, it is difficult for the central agency, NASA in our case, to assess the value of each interval to each user - this is really only known to the users who understand their needs. Thus, our problem is more precisely formulated as follows: find an allocation schedule for the resource that maximizes the sum of users preferences, when the preference values are private information of the users. We bypass this problem by making the assumptions that one can assign money to customers. This assumption is reasonable; a

  16. Development of Watch Schedule Using Rules Approach

    NASA Astrophysics Data System (ADS)

    Jurkevicius, Darius; Vasilecas, Olegas

    The software for schedule creation and optimization solves a difficult, important and practical problem. The proposed solution is an online employee portal where administrator users can create and manage watch schedules and employee requests. Each employee can login with his/her own account and see his/her assignments, manage requests, etc. Employees set as administrators can perform the employee scheduling online, manage requests, etc. This scheduling software allows users not only to see the initial and optimized watch schedule in a simple and understandable form, but also to create special rules and criteria and input their business. The system using rules automatically will generate watch schedule.

  17. Newton Methods for Large Scale Problems in Machine Learning

    ERIC Educational Resources Information Center

    Hansen, Samantha Leigh

    2014-01-01

    The focus of this thesis is on practical ways of designing optimization algorithms for minimizing large-scale nonlinear functions with applications in machine learning. Chapter 1 introduces the overarching ideas in the thesis. Chapters 2 and 3 are geared towards supervised machine learning applications that involve minimizing a sum of loss…

  18. Machine Learning

    DTIC Science & Technology

    1990-04-01

    DTIC i.LE COPY RADC-TR-90-25 Final Technical Report April 1990 MACHINE LEARNING The MITRE Corporation Melissa P. Chase Cs) CTIC ’- CT E 71 IN 2 11990...S. FUNDING NUMBERS MACHINE LEARNING C - F19628-89-C-0001 PE - 62702F PR - MOlE S. AUTHO(S) TA - 79 Melissa P. Chase WUT - 80 S. PERFORMING...341.280.5500 pm I " Aw Sig rill Ia 2110-01 SECTION 1 INTRODUCTION 1.1 BACKGROUND Research in machine learning has taken two directions in the problem of

  19. Fractional Programming for Communication Systems—Part II: Uplink Scheduling via Matching

    NASA Astrophysics Data System (ADS)

    Shen, Kaiming; Yu, Wei

    2018-05-01

    This two-part paper develops novel methodologies for using fractional programming (FP) techniques to design and optimize communication systems. Part I of this paper proposes a new quadratic transform for FP and treats its application for continuous optimization problems. In this Part II of the paper, we study discrete problems, such as those involving user scheduling, which are considerably more difficult to solve. Unlike the continuous problems, discrete or mixed discrete-continuous problems normally cannot be recast as convex problems. In contrast to the common heuristic of relaxing the discrete variables, this work reformulates the original problem in an FP form amenable to distributed combinatorial optimization. The paper illustrates this methodology by tackling the important and challenging problem of uplink coordinated multi-cell user scheduling in wireless cellular systems. Uplink scheduling is more challenging than downlink scheduling, because uplink user scheduling decisions significantly affect the interference pattern in nearby cells. Further, the discrete scheduling variable needs to be optimized jointly with continuous variables such as transmit power levels and beamformers. The main idea of the proposed FP approach is to decouple the interaction among the interfering links, thereby permitting a distributed and joint optimization of the discrete and continuous variables with provable convergence. The paper shows that the well-known weighted minimum mean-square-error (WMMSE) algorithm can also be derived from a particular use of FP; but our proposed FP-based method significantly outperforms WMMSE when discrete user scheduling variables are involved, both in term of run-time efficiency and optimizing results.

  20. Pre-Scheduled and Self Organized Sleep-Scheduling Algorithms for Efficient K-Coverage in Wireless Sensor Networks

    PubMed Central

    Hwang, I-Shyan

    2017-01-01

    The K-coverage configuration that guarantees coverage of each location by at least K sensors is highly popular and is extensively used to monitor diversified applications in wireless sensor networks. Long network lifetime and high detection quality are the essentials of such K-covered sleep-scheduling algorithms. However, the existing sleep-scheduling algorithms either cause high cost or cannot preserve the detection quality effectively. In this paper, the Pre-Scheduling-based K-coverage Group Scheduling (PSKGS) and Self-Organized K-coverage Scheduling (SKS) algorithms are proposed to settle the problems in the existing sleep-scheduling algorithms. Simulation results show that our pre-scheduled-based KGS approach enhances the detection quality and network lifetime, whereas the self-organized-based SKS algorithm minimizes the computation and communication cost of the nodes and thereby is energy efficient. Besides, SKS outperforms PSKGS in terms of network lifetime and detection quality as it is self-organized. PMID:29257078

  1. A System for Automatically Generating Scheduling Heuristics

    NASA Technical Reports Server (NTRS)

    Morris, Robert

    1996-01-01

    The goal of this research is to improve the performance of automated schedulers by designing and implementing an algorithm by automatically generating heuristics by selecting a schedule. The particular application selected by applying this method solves the problem of scheduling telescope observations, and is called the Associate Principal Astronomer. The input to the APA scheduler is a set of observation requests submitted by one or more astronomers. Each observation request specifies an observation program as well as scheduling constraints and preferences associated with the program. The scheduler employs greedy heuristic search to synthesize a schedule that satisfies all hard constraints of the domain and achieves a good score with respect to soft constraints expressed as an objective function established by an astronomer-user.

  2. A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem

    NASA Technical Reports Server (NTRS)

    Montoya, Justin Vincent; Wood, Zachary Paul; Rathinam, Sivakumar; Malik, Waqar Ahmad

    2010-01-01

    Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.

  3. Support vector machine in machine condition monitoring and fault diagnosis

    NASA Astrophysics Data System (ADS)

    Widodo, Achmad; Yang, Bo-Suk

    2007-08-01

    Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

  4. Machine-Learning-Based Prediction of a Missed Scheduled Clinical Appointment by Patients With Diabetes.

    PubMed

    Kurasawa, Hisashi; Hayashi, Katsuyoshi; Fujino, Akinori; Takasugi, Koichi; Haga, Tsuneyuki; Waki, Kayo; Noguchi, Takashi; Ohe, Kazuhiko

    2016-05-01

    About 10% of patients with diabetes discontinue treatment, resulting in the progression of diabetes-related complications and reduced quality of life. The objective was to predict a missed clinical appointment (MA), which can lead to discontinued treatment for diabetes patients. A machine-learning algorithm was used to build a logistic regression model for MA predictions, with L2-norm regularization used to avoid over-fitting and 10-fold cross validation used to evaluate prediction performance. Data associated with patient MAs were extracted from electronic medical records and classified into two groups: one related to patients' clinical condition (X1) and the other related to previous findings (X2). The records used were those of the University of Tokyo Hospital, and they included the history of 16 026 clinical appointments scheduled by 879 patients whose initial clinical visit had been made after January 1, 2004, who had diagnostic codes indicating diabetes, and whose HbA1c had been tested within 3 months after their initial visit. Records between April 1, 2011, and June 30, 2014, were inspected for a history of MAs. The best predictor of MAs proved to be X1 + X2 (AUC = 0.958); precision and recall rates were, respectively, 0.757 and 0.659. Among all the appointment data, the day of the week when an appointment was made was most strongly associated with MA predictions (weight = 2.22). Our findings may provide information to help clinicians make timely interventions to avoid MAs. © 2015 Diabetes Technology Society.

  5. Machine-Learning-Based Prediction of a Missed Scheduled Clinical Appointment by Patients With Diabetes

    PubMed Central

    Kurasawa, Hisashi; Hayashi, Katsuyoshi; Fujino, Akinori; Takasugi, Koichi; Haga, Tsuneyuki; Waki, Kayo; Noguchi, Takashi; Ohe, Kazuhiko

    2015-01-01

    Background: About 10% of patients with diabetes discontinue treatment, resulting in the progression of diabetes-related complications and reduced quality of life. Objective: The objective was to predict a missed clinical appointment (MA), which can lead to discontinued treatment for diabetes patients. Methods: A machine-learning algorithm was used to build a logistic regression model for MA predictions, with L2-norm regularization used to avoid over-fitting and 10-fold cross validation used to evaluate prediction performance. Data associated with patient MAs were extracted from electronic medical records and classified into two groups: one related to patients’ clinical condition (X1) and the other related to previous findings (X2). The records used were those of the University of Tokyo Hospital, and they included the history of 16 026 clinical appointments scheduled by 879 patients whose initial clinical visit had been made after January 1, 2004, who had diagnostic codes indicating diabetes, and whose HbA1c had been tested within 3 months after their initial visit. Records between April 1, 2011, and June 30, 2014, were inspected for a history of MAs. Results: The best predictor of MAs proved to be X1 + X2 (AUC = 0.958); precision and recall rates were, respectively, 0.757 and 0.659. Among all the appointment data, the day of the week when an appointment was made was most strongly associated with MA predictions (weight = 2.22). Conclusions: Our findings may provide information to help clinicians make timely interventions to avoid MAs. PMID:26555782

  6. Three hybridization models based on local search scheme for job shop scheduling problem

    NASA Astrophysics Data System (ADS)

    Balbi Fraga, Tatiana

    2015-05-01

    This work presents three different hybridization models based on the general schema of Local Search Heuristics, named Hybrid Successive Application, Hybrid Neighborhood, and Hybrid Improved Neighborhood. Despite similar approaches might have already been presented in the literature in other contexts, in this work these models are applied to analyzes the solution of the job shop scheduling problem, with the heuristics Taboo Search and Particle Swarm Optimization. Besides, we investigate some aspects that must be considered in order to achieve better solutions than those obtained by the original heuristics. The results demonstrate that the algorithms derived from these three hybrid models are more robust than the original algorithms and able to get better results than those found by the single Taboo Search.

  7. Chandra mission scheduling on-orbit experience

    NASA Astrophysics Data System (ADS)

    Bucher, Sabina; Williams, Brent; Pendexter, Misty; Balke, David

    2008-07-01

    Scheduling observatory time to maximize both day-to-day science target integration time and the lifetime of the observatory is a formidable challenge. Furthermore, it is not a static problem. Of course, every schedule brings a new set of observations, but the boundaries of the problem change as well. As spacecraft ages, its capabilities may degrade. As in-flight experience grows, capabilities may expand. As observing programs are completed, the needs and expectations of the science community may evolve. Changes such as these impact the rules by which a mission scheduled. In eight years on orbit, the Chandra X-Ray Observatory Mission Planning process has adapted to meet the challenge of maximizing day-to-day and mission lifetime science return, despite a consistently evolving set of scheduling constraints. The success of the planning team has been achieved, not through the use of complex algorithms and optimization routines, but through processes and home grown tools that help individuals make smart short term and long term Mission Planning decisions. This paper walks through the processes and tools used to plan and produce mission schedules for the Chandra X-Ray Observatory. Nominal planning and scheduling, target of opportunity response, and recovery from on-board autonomous safing actions are all addressed. Evolution of tools and processes, best practices, and lessons learned are highlighted along the way.

  8. Scheduling periodic jobs using imprecise results

    NASA Technical Reports Server (NTRS)

    Chung, Jen-Yao; Liu, Jane W. S.; Lin, Kwei-Jay

    1987-01-01

    One approach to avoid timing faults in hard, real-time systems is to make available intermediate, imprecise results produced by real-time processes. When a result of the desired quality cannot be produced in time, an imprecise result of acceptable quality produced before the deadline can be used. The problem of scheduling periodic jobs to meet deadlines on a system that provides the necessary programming language primitives and run-time support for processes to return imprecise results is discussed. Since the scheduler may choose to terminate a task before it is completed, causing it to produce an acceptable but imprecise result, the amount of processor time assigned to any task in a valid schedule can be less than the amount of time required to complete the task. A meaningful formulation of the scheduling problem must take into account the overall quality of the results. Depending on the different types of undesirable effects caused by errors, jobs are classified as type N or type C. For type N jobs, the effects of errors in results produced in different periods are not cumulative. A reasonable performance measure is the average error over all jobs. Three heuristic algorithms that lead to feasible schedules with small average errors are described. For type C jobs, the undesirable effects of errors produced in different periods are cumulative. Schedulability criteria of type C jobs are discussed.

  9. Scheduling Projects with Multiskill Learning Effect

    PubMed Central

    2014-01-01

    We investigate the project scheduling problem with multiskill learning effect. A new model is proposed to deal with the problem, where both autonomous and induced learning are considered. In order to obtain the optimal solution, a genetic algorithm with specific encoding and decoding schemes is introduced. A numerical example is used to illustrate the proposed model. The computational results show that the learning effect cannot be neglected in project scheduling. By means of determining the level of induced learning, the project manager can balance the project makespan with total cost. PMID:24683355

  10. Scheduling projects with multiskill learning effect.

    PubMed

    Zha, Hong; Zhang, Lianying

    2014-01-01

    We investigate the project scheduling problem with multiskill learning effect. A new model is proposed to deal with the problem, where both autonomous and induced learning are considered. In order to obtain the optimal solution, a genetic algorithm with specific encoding and decoding schemes is introduced. A numerical example is used to illustrate the proposed model. The computational results show that the learning effect cannot be neglected in project scheduling. By means of determining the level of induced learning, the project manager can balance the project makespan with total cost.

  11. Preliminary Development of Real Time Usage-Phase Monitoring System for CNC Machine Tools with a Case Study on CNC Machine VMC 250

    NASA Astrophysics Data System (ADS)

    Budi Harja, Herman; Prakosa, Tri; Raharno, Sri; Yuwana Martawirya, Yatna; Nurhadi, Indra; Setyo Nogroho, Alamsyah

    2018-03-01

    The production characteristic of job-shop industry at which products have wide variety but small amounts causes every machine tool will be shared to conduct production process with dynamic load. Its dynamic condition operation directly affects machine tools component reliability. Hence, determination of maintenance schedule for every component should be calculated based on actual usage of machine tools component. This paper describes study on development of monitoring system to obtaining information about each CNC machine tool component usage in real time approached by component grouping based on its operation phase. A special device has been developed for monitoring machine tool component usage by utilizing usage phase activity data taken from certain electronics components within CNC machine. The components are adaptor, servo driver and spindle driver, as well as some additional components such as microcontroller and relays. The obtained data are utilized for detecting machine utilization phases such as power on state, machine ready state or spindle running state. Experimental result have shown that the developed CNC machine tool monitoring system is capable of obtaining phase information of machine tool usage as well as its duration and displays the information at the user interface application.

  12. Toward interactive scheduling systems for managing medical resources.

    PubMed

    Oddi, A; Cesta, A

    2000-10-01

    Managers of medico-hospital facilities are facing two general problems when allocating resources to activities: (1) to find an agreement between several and contrasting requirements; (2) to manage dynamic and uncertain situations when constraints suddenly change over time due to medical needs. This paper describes the results of a research aimed at applying constraint-based scheduling techniques to the management of medical resources. A mixed-initiative problem solving approach is adopted in which a user and a decision support system interact to incrementally achieve a satisfactory solution to the problem. A running prototype is described called Interactive Scheduler which offers a set of functionalities for a mixed-initiative interaction to cope with the medical resource management. Interactive Scheduler is endowed with a representation schema used for describing the medical environment, a set of algorithms that address the specific problems of the domain, and an innovative interaction module that offers functionalities for the dialogue between the support system and its user. A particular contribution of this work is the explicit representation of constraint violations, and the definition of scheduling algorithms that aim at minimizing the amount of constraint violations in a solution.

  13. Scheduling Independent Partitions in Integrated Modular Avionics Systems

    PubMed Central

    Du, Chenglie; Han, Pengcheng

    2016-01-01

    Recently the integrated modular avionics (IMA) architecture has been widely adopted by the avionics industry due to its strong partition mechanism. Although the IMA architecture can achieve effective cost reduction and reliability enhancement in the development of avionics systems, it results in a complex allocation and scheduling problem. All partitions in an IMA system should be integrated together according to a proper schedule such that their deadlines will be met even under the worst case situations. In order to help provide a proper scheduling table for all partitions in IMA systems, we study the schedulability of independent partitions on a multiprocessor platform in this paper. We firstly present an exact formulation to calculate the maximum scaling factor and determine whether all partitions are schedulable on a limited number of processors. Then with a Game Theory analogy, we design an approximation algorithm to solve the scheduling problem of partitions, by allowing each partition to optimize its own schedule according to the allocations of the others. Finally, simulation experiments are conducted to show the efficiency and reliability of the approach proposed in terms of time consumption and acceptance ratio. PMID:27942013

  14. Scheduling from the perspective of the application

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Berman, F.; Wolski, R.

    1996-12-31

    Metacomputing is the aggregation of distributed and high-performance resources on coordinated networks. With careful scheduling, resource-intensive applications can be implemented efficiently on metacomputing systems at the sizes of interest to developers and users. In this paper we focus on the problem of scheduling applications on metacomputing systems. We introduce the concept of application-centric scheduling in which everything about the system is evaluated in terms of its impact on the application. Application-centric scheduling is used by virtually all metacomputer programmers to achieve performance on metacomputing systems. We describe two successful metacomputing applications to illustrate this approach, and describe AppLeS scheduling agentsmore » which generalize the application-centric scheduling approach. Finally, we show preliminary results which compare AppLeS-derived schedules with conventional strip and blocked schedules for a two-dimensional Jacobi code.« less

  15. Probability machines: consistent probability estimation using nonparametric learning machines.

    PubMed

    Malley, J D; Kruppa, J; Dasgupta, A; Malley, K G; Ziegler, A

    2012-01-01

    Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.

  16. Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Shah, Rahul H.

    production planning framework are discussed. A modified Particle Swarm Optimization solution technique is adopted to solve the proposed scheduling problem. The algorithm is described in detail and compared to Genetic Algorithm. Case studies are presented to illustrate the benefits of using the proposed model and the effectiveness of the Particle Swarm Optimization approach. Numerical Experiments are implemented and analyzed to test the effectiveness of the proposed model. The proposed scheduling strategy can achieve savings of around 19 to 27 % in cost per part when compared to the baseline scheduling scenarios. By optimizing key production system parameters from the cost per part model, the baseline scenarios can obtain around 20 to 35 % in savings for the cost per part. These savings further increase by 42 to 55 % when system parameter optimization is integrated with the proposed scheduling problem. Using this method, the most influential parameters on the cost per part are the rated power from production, the production rate, and the initial machine reliabilities. The modified Particle Swarm Optimization algorithm adopted allows greater diversity and exploration compared to Genetic Algorithm for the proposed joint model which results in it being more computationally efficient in determining the optimal scheduling. While Genetic Algorithm could achieve a solution quality of 2,279.63 at an expense of 2,300 seconds in computational effort. In comparison, the proposed Particle Swarm Optimization algorithm achieved a solution quality of 2,167.26 in less than half the computation effort which is required by Genetic Algorithm.

  17. Electrical machines with superconducting windings. Part 3: Homopolar dc machines

    NASA Astrophysics Data System (ADS)

    Kullman, D.; Henninger, P.

    1981-01-01

    The losses in rotating liquid metal contacts and the problems in including liquid metals were theoretically and experimentally studied. These machines are shown realiable. For electric ship propulsion, they are a more efficient method of power transmission than mechanical gearboxes. However, weight reduction as compared to mechanical gearboxes can hardly be achieved with machines fully shielded by magnetic iron.

  18. Decomposition of the compound Atwood machine

    NASA Astrophysics Data System (ADS)

    Lopes Coelho, R.

    2017-11-01

    Non-standard solving strategies for the compound Atwood machine problem have been proposed. The present strategy is based on a very simple idea. Taking an Atwood machine and replacing one of its bodies by another Atwood machine, we have a compound machine. As this operation can be repeated, we can construct any compound Atwood machine. This rule of construction is transferred to a mathematical model, whereby the equations of motion are obtained. The only difference between the machine and its model is that instead of pulleys and bodies, we have reference frames that move solidarily with these objects. This model provides us with the accelerations in the non-inertial frames of the bodies, which we will use to obtain the equations of motion. This approach to the problem will be justified by the Lagrange method and exemplified by machines with six and eight bodies.

  19. Quadrilateral Micro-Hole Array Machining on Invar Thin Film: Wet Etching and Electrochemical Fusion Machining

    PubMed Central

    Choi, Woong-Kirl; Kim, Seong-Hyun; Choi, Seung-Geon; Lee, Eun-Sang

    2018-01-01

    Ultra-precision products which contain a micro-hole array have recently shown remarkable demand growth in many fields, especially in the semiconductor and display industries. Photoresist etching and electrochemical machining are widely known as precision methods for machining micro-holes with no residual stress and lower surface roughness on the fabricated products. The Invar shadow masks used for organic light-emitting diodes (OLEDs) contain numerous micro-holes and are currently machined by a photoresist etching method. However, this method has several problems, such as uncontrollable hole machining accuracy, non-etched areas, and overcutting. To solve these problems, a machining method that combines photoresist etching and electrochemical machining can be applied. In this study, negative photoresist with a quadrilateral hole array pattern was dry coated onto 30-µm-thick Invar thin film, and then exposure and development were carried out. After that, photoresist single-side wet etching and a fusion method of wet etching-electrochemical machining were used to machine micro-holes on the Invar. The hole machining geometry, surface quality, and overcutting characteristics of the methods were studied. Wet etching and electrochemical fusion machining can improve the accuracy and surface quality. The overcutting phenomenon can also be controlled by the fusion machining. Experimental results show that the proposed method is promising for the fabrication of Invar film shadow masks. PMID:29351235

  20. Strategic Gang Scheduling for Railroad Maintenance

    DOT National Transportation Integrated Search

    2012-08-14

    We address the railway track maintenance scheduling problem. The problem stems from the : significant percentage of the annual budget invested by the railway industry for maintaining its railway : tracks. The process requires consideration of human r...

  1. Production scheduling and rescheduling with genetic algorithms.

    PubMed

    Bierwirth, C; Mattfeld, D C

    1999-01-01

    A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs.

  2. Littoral Combat Ship Crew Scheduling

    DTIC Science & Technology

    2015-03-01

    events and schedules. The selection of u for each sub-problem also has the same tradeoff considerations of balancing solve time and overly myopic ...extending them beyond four months in a phase. Results are compared based on solve time and penalty value. The MIP solution has the best quality...benefits to crew alignment for longer-range schedules. The planner must balance solve time and solution quality when determining the approach to

  3. The effect of embedded bonus rounds on slot machine preference.

    PubMed

    Belisle, Jordan; Owens, Kelti; Dixon, Mark R; Malkin, Albert; Jordan, Sam D

    2017-04-01

    Twenty-three university students completed a simulated slot machine task involving the concurrent presentation of two slot machines that were varied both in win density and the inclusion of a bonus round feature to evaluate the effect of embedded bonus rounds on participant response allocation. The results suggest that participants allocated a greater percentage of responses to machines with embedded bonus rounds across both dense (Bonus: M = 68.4, SD = 19.2; No Bonus: M = 51.2; 9.6) and lean (Bonus: M = 48.8, SD = 9.6; No Bonus: M = 31.6, SD = 19.2) reinforcement schedules, in which the overall reinforcement rate across all machines was held constant. © 2016 Society for the Experimental Analysis of Behavior.

  4. Schedule-Aware Workflow Management Systems

    NASA Astrophysics Data System (ADS)

    Mans, Ronny S.; Russell, Nick C.; van der Aalst, Wil M. P.; Moleman, Arnold J.; Bakker, Piet J. M.

    Contemporary workflow management systems offer work-items to users through specific work-lists. Users select the work-items they will perform without having a specific schedule in mind. However, in many environments work needs to be scheduled and performed at particular times. For example, in hospitals many work-items are linked to appointments, e.g., a doctor cannot perform surgery without reserving an operating theater and making sure that the patient is present. One of the problems when applying workflow technology in such domains is the lack of calendar-based scheduling support. In this paper, we present an approach that supports the seamless integration of unscheduled (flow) and scheduled (schedule) tasks. Using CPN Tools we have developed a specification and simulation model for schedule-aware workflow management systems. Based on this a system has been realized that uses YAWL, Microsoft Exchange Server 2007, Outlook, and a dedicated scheduling service. The approach is illustrated using a real-life case study at the AMC hospital in the Netherlands. In addition, we elaborate on the experiences obtained when developing and implementing a system of this scale using formal techniques.

  5. Optimization Models for Scheduling of Jobs

    PubMed Central

    Indika, S. H. Sathish; Shier, Douglas R.

    2006-01-01

    This work is motivated by a particular scheduling problem that is faced by logistics centers that perform aircraft maintenance and modification. Here we concentrate on a single facility (hangar) which is equipped with several work stations (bays). Specifically, a number of jobs have already been scheduled for processing at the facility; the starting times, durations, and work station assignments for these jobs are assumed to be known. We are interested in how best to schedule a number of new jobs that the facility will be processing in the near future. We first develop a mixed integer quadratic programming model (MIQP) for this problem. Since the exact solution of this MIQP formulation is time consuming, we develop a heuristic procedure, based on existing bin packing techniques. This heuristic is further enhanced by application of certain local optimality conditions. PMID:27274921

  6. The role of artificial intelligence techniques in scheduling systems

    NASA Technical Reports Server (NTRS)

    Geoffroy, Amy L.; Britt, Daniel L.; Gohring, John R.

    1990-01-01

    Artificial Intelligence (AI) techniques provide good solutions for many of the problems which are characteristic of scheduling applications. However, scheduling is a large, complex heterogeneous problem. Different applications will require different solutions. Any individual application will require the use of a variety of techniques, including both AI and conventional software methods. The operational context of the scheduling system will also play a large role in design considerations. The key is to identify those places where a specific AI technique is in fact the preferable solution, and to integrate that technique into the overall architecture.

  7. Education and Social Equity: With a Special Focus on Scheduled Castes and Scheduled Tribes in Elementary Education. CREATE Pathways to Access. Research Monograph No. 19

    ERIC Educational Resources Information Center

    Sedwal, Mona; Kamat, Sangeeta

    2008-01-01

    The Scheduled Castes (SCs, also known as Dalits) and Scheduled Tribes (STs, also known as Adivasis) are among the most socially and educationally disadvantaged groups in India. This paper examines issues concerning school access and equity for Scheduled Caste and Scheduled Tribe communities and also highlights their unique problems, which may…

  8. Scheduling real-time, periodic jobs using imprecise results

    NASA Technical Reports Server (NTRS)

    Liu, Jane W. S.; Lin, Kwei-Jay; Natarajan, Swaminathan

    1987-01-01

    A process is called a monotone process if the accuracy of its intermediate results is non-decreasing as more time is spent to obtain the result. The result produced by a monotone process upon its normal termination is the desired result; the error in this result is zero. External events such as timeouts or crashes may cause the process to terminate prematurely. If the intermediate result produced by the process upon its premature termination is saved and made available, the application may still find the result unusable and, hence, acceptable; such a result is said to be an imprecise one. The error in an imprecise result is nonzero. The problem of scheduling periodic jobs to meet deadlines on a system that provides the necessary programming language primitives and run-time support for processes to return imprecise results is discussed. This problem differs from the traditional scheduling problems since the scheduler may choose to terminate a task before it is completed, causing it to produce an acceptable but imprecise result. Consequently, the amounts of processor time assigned to tasks in a valid schedule can be less than the amounts of time required to complete the tasks. A meaningful formulation of this problem taking into account the quality of the overall result is discussed. Three algorithms for scheduling jobs for which the effects of errors in results produced in different periods are not cumulative are described, and their relative merits are evaluated.

  9. A user interface for a knowledge-based planning and scheduling system

    NASA Technical Reports Server (NTRS)

    Mulvehill, Alice M.

    1988-01-01

    The objective of EMPRESS (Expert Mission Planning and Replanning Scheduling System) is to support the planning and scheduling required to prepare science and application payloads for flight aboard the US Space Shuttle. EMPRESS was designed and implemented in Zetalisp on a 3600 series Symbolics Lisp machine. Initially, EMPRESS was built as a concept demonstration system. The system has since been modified and expanded to ensure that the data have integrity. Issues underlying the design and development of the EMPRESS-I interface, results from a system usability assessment, and consequent modifications are described.

  10. Scheduling Non-Preemptible Jobs to Minimize Peak Demand

    DOE PAGES

    Yaw, Sean; Mumey, Brendan

    2017-10-28

    Our paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We then focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the peak demand minimization problem, and has been previously shown tomore » be NP-hard. These results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show that these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.« less

  11. Scheduling Non-Preemptible Jobs to Minimize Peak Demand

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Yaw, Sean; Mumey, Brendan

    Our paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We then focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the peak demand minimization problem, and has been previously shown tomore » be NP-hard. These results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show that these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.« less

  12. Quantum Machine Learning

    NASA Technical Reports Server (NTRS)

    Biswas, Rupak

    2018-01-01

    Quantum computing promises an unprecedented ability to solve intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. The Quantum Artificial Intelligence Laboratory (QuAIL) at NASA Ames Research Center is the space agency's primary facility for conducting research and development in quantum information sciences. QuAIL conducts fundamental research in quantum physics but also explores how best to exploit and apply this disruptive technology to enable NASA missions in aeronautics, Earth and space sciences, and space exploration. At the same time, machine learning has become a major focus in computer science and captured the imagination of the public as a panacea to myriad big data problems. In this talk, we will discuss how classical machine learning can take advantage of quantum computing to significantly improve its effectiveness. Although we illustrate this concept on a quantum annealer, other quantum platforms could be used as well. If explored fully and implemented efficiently, quantum machine learning could greatly accelerate a wide range of tasks leading to new technologies and discoveries that will significantly change the way we solve real-world problems.

  13. The methodic of calculation for the need of basic construction machines on construction site when developing organizational and technological documentation

    NASA Astrophysics Data System (ADS)

    Zhadanovsky, Boris; Sinenko, Sergey

    2018-03-01

    Economic indicators of construction work, particularly in high-rise construction, are directly related to the choice of optimal number of machines. The shortage of machinery makes it impossible to complete the construction & installation work on scheduled time. Rates of performance of construction & installation works and labor productivity during high-rise construction largely depend on the degree of provision of construction project with machines (level of work mechanization). During calculation of the need for machines in construction projects, it is necessary to ensure that work is completed on scheduled time, increased level of complex mechanization, increased productivity and reduction of manual work, and improved usage and maintenance of machine fleet. The selection of machines and determination of their numbers should be carried out by using formulas presented in this work.

  14. Scheduling Software for Complex Scenarios

    NASA Technical Reports Server (NTRS)

    2006-01-01

    Preparing a vehicle and its payload for a single launch is a complex process that involves thousands of operations. Because the equipment and facilities required to carry out these operations are extremely expensive and limited in number, optimal assignment and efficient use are critically important. Overlapping missions that compete for the same resources, ground rules, safety requirements, and the unique needs of processing vehicles and payloads destined for space impose numerous constraints that, when combined, require advanced scheduling. Traditional scheduling systems use simple algorithms and criteria when selecting activities and assigning resources and times to each activity. Schedules generated by these simple decision rules are, however, frequently far from optimal. To resolve mission-critical scheduling issues and predict possible problem areas, NASA historically relied upon expert human schedulers who used their judgment and experience to determine where things should happen, whether they will happen on time, and whether the requested resources are truly necessary.

  15. Manipulating Slot Machine Preference in Problem Gamblers through Contextual Control

    ERIC Educational Resources Information Center

    Nastally, Becky L.; Dixon, Mark R.; Jackson, James W.

    2010-01-01

    Pathological and nonpathological gamblers completed a task that assessed preference among 2 concurrently available slot machines. Subsequent assessments of choice were conducted after various attempts to transfer contextual functions associated with irrelevant characteristics of the slot machines. Results indicated that the nonproblem gambling…

  16. Multi-objective group scheduling optimization integrated with preventive maintenance

    NASA Astrophysics Data System (ADS)

    Liao, Wenzhu; Zhang, Xiufang; Jiang, Min

    2017-11-01

    This article proposes a single-machine-based integration model to meet the requirements of production scheduling and preventive maintenance in group production. To describe the production for identical/similar and different jobs, this integrated model considers the learning and forgetting effects. Based on machine degradation, the deterioration effect is also considered. Moreover, perfect maintenance and minimal repair are adopted in this integrated model. The multi-objective of minimizing total completion time and maintenance cost is taken to meet the dual requirements of delivery date and cost. Finally, a genetic algorithm is developed to solve this optimization model, and the computation results demonstrate that this integrated model is effective and reliable.

  17. Conception of Self-Construction Production Scheduling System

    NASA Astrophysics Data System (ADS)

    Xue, Hai; Zhang, Xuerui; Shimizu, Yasuhiro; Fujimura, Shigeru

    With the high speed innovation of information technology, many production scheduling systems have been developed. However, a lot of customization according to individual production environment is required, and then a large investment for development and maintenance is indispensable. Therefore now the direction to construct scheduling systems should be changed. The final objective of this research aims at developing a system which is built by it extracting the scheduling technique automatically through the daily production scheduling work, so that an investment will be reduced. This extraction mechanism should be applied for various production processes for the interoperability. Using the master information extracted by the system, production scheduling operators can be supported to accelerate the production scheduling work easily and accurately without any restriction of scheduling operations. By installing this extraction mechanism, it is easy to introduce scheduling system without a lot of expense for customization. In this paper, at first a model for expressing a scheduling problem is proposed. Then the guideline to extract the scheduling information and use the extracted information is shown and some applied functions are also proposed based on it.

  18. Design tool for multiprocessor scheduling and evaluation of iterative dataflow algorithms

    NASA Technical Reports Server (NTRS)

    Jones, Robert L., III

    1995-01-01

    A graph-theoretic design process and software tool is defined for selecting a multiprocessing scheduling solution for a class of computational problems. The problems of interest are those that can be described with a dataflow graph and are intended to be executed repetitively on a set of identical processors. Typical applications include signal processing and control law problems. Graph-search algorithms and analysis techniques are introduced and shown to effectively determine performance bounds, scheduling constraints, and resource requirements. The software tool applies the design process to a given problem and includes performance optimization through the inclusion of additional precedence constraints among the schedulable tasks.

  19. A scheduling algorithm for Spacelab telescope observations

    NASA Technical Reports Server (NTRS)

    Grone, B.

    1982-01-01

    An algorithm is developed for sequencing and scheduling of observations of stellar targets by equipment on Spacelab. The method is a general one. The scheduling problem is defined and examined. The method developed for its solution is documented. Suggestions for further development and implementation of this method are made.

  20. An improved robust buffer allocation method for the project scheduling problem

    NASA Astrophysics Data System (ADS)

    Ghoddousi, Parviz; Ansari, Ramin; Makui, Ahmad

    2017-04-01

    Unpredictable uncertainties cause delays and additional costs for projects. Often, when using traditional approaches, the optimizing procedure of the baseline project plan fails and leads to delays. In this study, a two-stage multi-objective buffer allocation approach is applied for robust project scheduling. In the first stage, some decisions are made on buffer sizes and allocation to the project activities. A set of Pareto-optimal robust schedules is designed using the meta-heuristic non-dominated sorting genetic algorithm (NSGA-II) based on the decisions made in the buffer allocation step. In the second stage, the Pareto solutions are evaluated in terms of the deviation from the initial start time and due dates. The proposed approach was implemented on a real dam construction project. The outcomes indicated that the obtained buffered schedule reduces the cost of disruptions by 17.7% compared with the baseline plan, with an increase of about 0.3% in the project completion time.

  1. Space power system scheduling using an expert system

    NASA Technical Reports Server (NTRS)

    Bahrami, K. A.; Biefeld, E.; Costello, L.; Klein, J. W.

    1986-01-01

    A most pressing problem in space exploration is timely spacecraft power system sequence generation, which requires the scheduling of a set of loads given a set of resource constraints. This is particularly important after an anomaly or failure. This paper discusses the power scheduling problem and how the software program, Plan-It, can be used as a consultant for scheduling power system activities. Modeling of power activities, human interface, and two of the many strategies used by Plan-It are discussed. Preliminary results showing the development of a conflict-free sequence from an initial sequence with conflicts is presented. It shows that a 4-day schedule can be generated in a matter of a few minutes, which provides sufficient time in many cases to aid the crew in the replanning of loads and generation use following a failure or anomaly.

  2. Scheduling Real-Time Mixed-Criticality Jobs

    NASA Astrophysics Data System (ADS)

    Baruah, Sanjoy K.; Bonifaci, Vincenzo; D'Angelo, Gianlorenzo; Li, Haohan; Marchetti-Spaccamela, Alberto; Megow, Nicole; Stougie, Leen

    Many safety-critical embedded systems are subject to certification requirements; some systems may be required to meet multiple sets of certification requirements, from different certification authorities. Certification requirements in such "mixed-criticality" systems give rise to interesting scheduling problems, that cannot be satisfactorily addressed using techniques from conventional scheduling theory. In this paper, we study a formal model for representing such mixed-criticality workloads. We demonstrate first the intractability of determining whether a system specified in this model can be scheduled to meet all its certification requirements, even for systems subject to two sets of certification requirements. Then we quantify, via the metric of processor speedup factor, the effectiveness of two techniques, reservation-based scheduling and priority-based scheduling, that are widely used in scheduling such mixed-criticality systems, showing that the latter of the two is superior to the former. We also show that the speedup factors are tight for these two techniques.

  3. A space station onboard scheduling assistant

    NASA Technical Reports Server (NTRS)

    Brindle, A. F.; Anderson, B. H.

    1988-01-01

    One of the goals for the Space Station is to achieve greater autonomy, and have less reliance on ground commanding than previous space missions. This means that the crew will have to take an active role in scheduling and rescheduling their activities onboard, perhaps working from preliminary schedules generated on the ground. Scheduling is a time intensive task, whether performed manually or automatically, so the best approach to solving onboard scheduling problems may involve crew members working with an interactive software scheduling package. A project is described which investigates a system that uses knowledge based techniques for the rescheduling of experiments within the Materials Technology Laboratory of the Space Station. Particular attention is paid to: (1) methods for rapid response rescheduling to accommodate unplanned changes in resource availability, (2) the nature of the interface to the crew, (3) the representation of the many types of data within the knowledge base, and (4) the possibility of applying rule-based and constraint-based reasoning methods to onboard activity scheduling.

  4. Electric power scheduling - A distributed problem-solving approach

    NASA Technical Reports Server (NTRS)

    Mellor, Pamela A.; Dolce, James L.; Krupp, Joseph C.

    1990-01-01

    Space Station Freedom's power system, along with the spacecraft's other subsystems, needs to carefully conserve its resources and yet strive to maximize overall Station productivity. Due to Freedom's distributed design, each subsystem must work cooperatively within the Station community. There is a need for a scheduling tool which will preserve this distributed structure, allow each subsystem the latitude to satisfy its own constraints, and preserve individual value systems while maintaining Station-wide integrity.

  5. Minimizing makespan in a two-stage flow shop with parallel batch-processing machines and re-entrant jobs

    NASA Astrophysics Data System (ADS)

    Huang, J. D.; Liu, J. J.; Chen, Q. X.; Mao, N.

    2017-06-01

    Against a background of heat-treatment operations in mould manufacturing, a two-stage flow-shop scheduling problem is described for minimizing makespan with parallel batch-processing machines and re-entrant jobs. The weights and release dates of jobs are non-identical, but job processing times are equal. A mixed-integer linear programming model is developed and tested with small-scale scenarios. Given that the problem is NP hard, three heuristic construction methods with polynomial complexity are proposed. The worst case of the new constructive heuristic is analysed in detail. A method for computing lower bounds is proposed to test heuristic performance. Heuristic efficiency is tested with sets of scenarios. Compared with the two improved heuristics, the performance of the new constructive heuristic is superior.

  6. Scheduling in the Face of Uncertain Resource Consumption and Utility

    NASA Technical Reports Server (NTRS)

    Frank, Jeremy; Dearden, Richard

    2003-01-01

    We discuss the problem of scheduling tasks that consume uncertain amounts of a resource with known capacity and where the tasks have uncertain utility. In these circumstances, we would like to find schedules that exceed a lower bound on the expected utility when executed. We show that the problems are NP- complete, and present some results that characterize the behavior of some simple heuristics over a variety of problem classes.

  7. Machine learning in updating predictive models of planning and scheduling transportation projects

    DOT National Transportation Integrated Search

    1997-01-01

    A method combining machine learning and regression analysis to automatically and intelligently update predictive models used in the Kansas Department of Transportations (KDOTs) internal management system is presented. The predictive models used...

  8. Source localization in an ocean waveguide using supervised machine learning.

    PubMed

    Niu, Haiqiang; Reeves, Emma; Gerstoft, Peter

    2017-09-01

    Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization.

  9. Temporal planning for transportation planning and scheduling

    NASA Technical Reports Server (NTRS)

    Frederking, Robert E.; Muscettola, Nicola

    1992-01-01

    In this paper we describe preliminary work done in the CORTES project, applying the Heuristic Scheduling Testbed System (HSTS) to a transportation planning and scheduling domain. First, we describe in more detail the transportation problems that we are addressing. We then describe the fundamental characteristics of HSTS and we concentrate on the representation of multiple capacity resources. We continue with a more detailed description of the transportation planning problem that we have initially addressed in HSTS and of its solution. Finally we describe future directions for our research.

  10. Planning as a Precursor to Scheduling for Space Station Payload Operations

    NASA Technical Reports Server (NTRS)

    Howell, Eric; Maxwell, Theresa

    1995-01-01

    Contemporary schedulers attempt to solve the problem of best fitting a set of activities into an available timeframe while still satisfying the necessary constraints. This approach produces results which are optimized for the region of time the scheduler is able to process, satisfying the near term goals of the operation. In general the scheduler is not able to reason about the activities which precede or follow the window into which it is inputs to scheduling so that the intermediate placing activities. This creates a problem for operations which are composed of many activities spanning long durations (which exceed the scheduler's reasoning horizon) such as the continuous operations environment for payload operations on the Space Station. Not only must the near term scheduling objectives be met, but somehow the results of near term scheduling must be made to support the attainment of long term goals.

  11. An extended abstract: A heuristic repair method for constraint-satisfaction and scheduling problems

    NASA Technical Reports Server (NTRS)

    Minton, Steven; Johnston, Mark D.; Philips, Andrew B.; Laird, Philip

    1992-01-01

    The work described in this paper was inspired by a surprisingly effective neural network developed for scheduling astronomical observations on the Hubble Space Telescope. Our heuristic constraint satisfaction problem (CSP) method was distilled from an analysis of the network. In the process of carrying out the analysis, we discovered that the effectiveness of the network has little to do with its connectionist implementation. Furthermore, the ideas employed in the network can be implemented very efficiently within a symbolic CSP framework. The symbolic implementation is extremely simple. It also has the advantage that several different search strategies can be employed, although we have found that hill-climbing methods are particularly well-suited for the applications that we have investigated. We begin the paper with a brief review of the neural network. Following this, we describe our symbolic method for heuristic repair.

  12. CABINS: Case-based interactive scheduler

    NASA Technical Reports Server (NTRS)

    Miyashita, Kazuo; Sycara, Katia

    1992-01-01

    In this paper we discuss the need for interactive factory schedule repair and improvement, and we identify case-based reasoning (CBR) as an appropriate methodology. Case-based reasoning is the problem solving paradigm that relies on a memory for past problem solving experiences (cases) to guide current problem solving. Cases similar to the current case are retrieved from the case memory, and similarities and differences of the current case to past cases are identified. Then a best case is selected, and its repair plan is adapted to fit the current problem description. If a repair solution fails, an explanation for the failure is stored along with the case in memory, so that the user can avoid repeating similar failures in the future. So far we have identified a number of repair strategies and tactics for factory scheduling and have implemented a part of our approach in a prototype system, called CABINS. As a future work, we are going to scale up CABINS to evaluate its usefulness in a real manufacturing environment.

  13. Optimal radiotherapy dose schedules under parametric uncertainty

    NASA Astrophysics Data System (ADS)

    Badri, Hamidreza; Watanabe, Yoichi; Leder, Kevin

    2016-01-01

    We consider the effects of parameter uncertainty on the optimal radiation schedule in the context of the linear-quadratic model. Our interest arises from the observation that if inter-patient variability in normal and tumor tissue radiosensitivity or sparing factor of the organs-at-risk (OAR) are not accounted for during radiation scheduling, the performance of the therapy may be strongly degraded or the OAR may receive a substantially larger dose than the allowable threshold. This paper proposes a stochastic radiation scheduling concept to incorporate inter-patient variability into the scheduling optimization problem. Our method is based on a probabilistic approach, where the model parameters are given by a set of random variables. Our probabilistic formulation ensures that our constraints are satisfied with a given probability, and that our objective function achieves a desired level with a stated probability. We used a variable transformation to reduce the resulting optimization problem to two dimensions. We showed that the optimal solution lies on the boundary of the feasible region and we implemented a branch and bound algorithm to find the global optimal solution. We demonstrated how the configuration of optimal schedules in the presence of uncertainty compares to optimal schedules in the absence of uncertainty (conventional schedule). We observed that in order to protect against the possibility of the model parameters falling into a region where the conventional schedule is no longer feasible, it is required to avoid extremal solutions, i.e. a single large dose or very large total dose delivered over a long period. Finally, we performed numerical experiments in the setting of head and neck tumors including several normal tissues to reveal the effect of parameter uncertainty on optimal schedules and to evaluate the sensitivity of the solutions to the choice of key model parameters.

  14. Advancing Air Force Scheduling through Modeling Problem Topologies

    DTIC Science & Technology

    2006-08-03

    Merrill on August 23, 2005 and corresponded with Major David Van Veldhuizen in Fall 2005 about obtaining data. 3.4.3 Transitions Analytical Graphics and...observation satellite orbit. Technical Report CRT-2003-27, Centre de recherche sur les transports, July 2003. [5] Van -Dat Cung. ROADEF 2003: Results of the...collaborateurs/etd/default.htm. January, 2004. [15] P.J.M van Laarhoven, E.H.L. Aarts, and J.K. Lenstra. Job shop scheduling by simulated annealing

  15. Developing optimal nurses work schedule using integer programming

    NASA Astrophysics Data System (ADS)

    Shahidin, Ainon Mardhiyah; Said, Mohd Syazwan Md; Said, Noor Hizwan Mohamad; Sazali, Noor Izatie Amaliena

    2017-08-01

    Time management is the art of arranging, organizing and scheduling one's time for the purpose of generating more effective work and productivity. Scheduling is the process of deciding how to commit resources between varieties of possible tasks. Thus, it is crucial for every organization to have a good work schedule for their staffs. The job of Ward nurses at hospitals runs for 24 hours every day. Therefore, nurses will be working using shift scheduling. This study is aimed to solve the nurse scheduling problem at an emergency ward of a private hospital. A 7-day work schedule for 7 consecutive weeks satisfying all the constraints set by the hospital will be developed using Integer Programming. The work schedule for the nurses obtained gives an optimal solution where all the constraints are being satisfied successfully.

  16. Scheduling revisited workstations in integrated-circuit fabrication

    NASA Technical Reports Server (NTRS)

    Kline, Paul J.

    1992-01-01

    The cost of building new semiconductor wafer fabrication factories has grown rapidly, and a state-of-the-art fab may cost 250 million dollars or more. Obtaining an acceptable return on this investment requires high productivity from the fabrication facilities. This paper describes the Photo Dispatcher system which was developed to make machine-loading recommendations on a set of key fab machines. Dispatching policies that generally perform well in job shops (e.g., Shortest Remaining Processing Time) perform poorly for workstations such as photolithography which are visited several times by the same lot of silicon wafers. The Photo Dispatcher evaluates the history of workloads throughout the fab and identifies bottleneck areas. The scheduler then assigns priorities to lots depending on where they are headed after photolithography. These priorities are designed to avoid starving bottleneck workstations and to give preference to lots that are headed to areas where they can be processed with minimal waiting. Other factors considered by the scheduler to establish priorities are the nearness of a lot to the end of its process flow and the time that the lot has already been waiting in queue. Simulations that model the equipment and products in one of Texas Instrument's wafer fabs show the Photo Dispatcher can produce a 10 percent improvement in the time required to fabricate integrated circuits.

  17. An Integer Batch Scheduling Model for a Single Machine with Simultaneous Learning and Deterioration Effects to Minimize Total Actual Flow Time

    NASA Astrophysics Data System (ADS)

    Yusriski, R.; Sukoyo; Samadhi, T. M. A. A.; Halim, A. H.

    2016-02-01

    In the manufacturing industry, several identical parts can be processed in batches, and setup time is needed between two consecutive batches. Since the processing times of batches are not always fixed during a scheduling period due to learning and deterioration effects, this research deals with batch scheduling problems with simultaneous learning and deterioration effects. The objective is to minimize total actual flow time, defined as a time interval between the arrival of all parts at the shop and their common due date. The decision variables are the number of batches, integer batch sizes, and the sequence of the resulting batches. This research proposes a heuristic algorithm based on the Lagrange Relaxation. The effectiveness of the proposed algorithm is determined by comparing the resulting solutions of the algorithm to the respective optimal solution obtained from the enumeration method. Numerical experience results show that the average of difference among the solutions is 0.05%.

  18. Design Control Systems of Human Machine Interface in the NTVS-2894 Seat Grinder Machine to Increase the Productivity

    NASA Astrophysics Data System (ADS)

    Ardi, S.; Ardyansyah, D.

    2018-02-01

    In the Manufacturing of automotive spare parts, increased sales of vehicles is resulted in increased demand for production of engine valve of the customer. To meet customer demand, we carry out improvement and overhaul of the NTVS-2894 seat grinder machine on a machining line. NTVS-2894 seat grinder machine has been decreased machine productivity, the amount of trouble, and the amount of downtime. To overcome these problems on overhaul the NTVS-2984 seat grinder machine include mechanical and programs, is to do the design and manufacture of HMI (Human Machine Interface) GP-4501T program. Because of the time prior to the overhaul, NTVS-2894 seat grinder machine does not have a backup HMI (Human Machine Interface) program. The goal of the design and manufacture in this program is to improve the achievement of production, and allows an operator to operate beside it easier to troubleshoot the NTVS-2894 seat grinder machine thereby reducing downtime on the NTVS-2894 seat grinder machine. The results after the design are HMI program successfully made it back, machine productivity increased by 34.8%, the amount of trouble, and downtime decreased 40% decrease from 3,160 minutes to 1,700 minutes. The implication of our design, it could facilitate the operator in operating machine and the technician easer to maintain and do the troubleshooting the machine problems.

  19. Observation Scheduling System

    NASA Technical Reports Server (NTRS)

    Chien, Steve A.; Tran, Daniel Q.; Rabideau, Gregg R.; Schaffer, Steven R.

    2011-01-01

    Software has been designed to schedule remote sensing with the Earth Observing One spacecraft. The software attempts to satisfy as many observation requests as possible considering each against spacecraft operation constraints such as data volume, thermal, pointing maneuvers, and others. More complex constraints such as temperature are approximated to enable efficient reasoning while keeping the spacecraft within safe limits. Other constraints are checked using an external software library. For example, an attitude control library is used to determine the feasibility of maneuvering between pairs of observations. This innovation can deal with a wide range of spacecraft constraints and solve large scale scheduling problems like hundreds of observations and thousands of combinations of observation sequences.

  20. Online stochastic optimization of radiotherapy patient scheduling.

    PubMed

    Legrain, Antoine; Fortin, Marie-Andrée; Lahrichi, Nadia; Rousseau, Louis-Martin

    2015-06-01

    The effective management of a cancer treatment facility for radiation therapy depends mainly on optimizing the use of the linear accelerators. In this project, we schedule patients on these machines taking into account their priority for treatment, the maximum waiting time before the first treatment, and the treatment duration. We collaborate with the Centre Intégré de Cancérologie de Laval to determine the best scheduling policy. Furthermore, we integrate the uncertainty related to the arrival of patients at the center. We develop a hybrid method combining stochastic optimization and online optimization to better meet the needs of central planning. We use information on the future arrivals of patients to provide an accurate picture of the expected utilization of resources. Results based on real data show that our method outperforms the policies typically used in treatment centers.

  1. An investigation of the use of temporal decomposition in space mission scheduling

    NASA Technical Reports Server (NTRS)

    Bullington, Stanley E.; Narayanan, Venkat

    1994-01-01

    This research involves an examination of techniques for solving scheduling problems in long-duration space missions. The mission timeline is broken up into several time segments, which are then scheduled incrementally. Three methods are presented for identifying the activities that are to be attempted within these segments. The first method is a mathematical model, which is presented primarily to illustrate the structure of the temporal decomposition problem. Since the mathematical model is bound to be computationally prohibitive for realistic problems, two heuristic assignment procedures are also presented. The first heuristic method is based on dispatching rules for activity selection, and the second heuristic assigns performances of a model evenly over timeline segments. These heuristics are tested using a sample Space Station mission and a Spacelab mission. The results are compared with those obtained by scheduling the missions without any problem decomposition. The applicability of this approach to large-scale mission scheduling problems is also discussed.

  2. Scheduling IT staff at a bank: a mathematical programming approach.

    PubMed

    Labidi, M; Mrad, M; Gharbi, A; Louly, M A

    2014-01-01

    We address a real-world optimization problem: the scheduling of a Bank Information Technologies (IT) staff. This problem can be defined as the process of constructing optimized work schedules for staff. In a general sense, it requires the allocation of suitably qualified staff to specific shifts to meet the demands for services of an organization while observing workplace regulations and attempting to satisfy individual work preferences. A monthly shift schedule is prepared to determine the shift duties of each staff considering shift coverage requirements, seniority-based workload rules, and staff work preferences. Due to the large number of conflicting constraints, a multiobjective programming model has been proposed to automate the schedule generation process. The suggested mathematical model has been implemented using Lingo software. The results indicate that high quality solutions can be obtained within a few seconds compared to the manually prepared schedules.

  3. Language Acquisition and Machine Learning.

    DTIC Science & Technology

    1986-02-01

    machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, the authors propose four component tasks involved in learning from experience-aggregation, clustering, characterization, and storage. They then consider four common problems studied by machine learning researchers-learning from examples, heuristics learning, conceptual clustering, and learning macro-operators-describing each in terms of our framework. After this, they turn to the problem of grammar

  4. Electronic gaming machines: are they the 'crack-cocaine' of gambling?

    PubMed

    Dowling, Nicki; Smith, David; Thomas, Trang

    2005-01-01

    There is a general view that electronic gaming is the most 'addictive' form of gambling, in that it contributes more to causing problem gambling than any other gambling activity. As such, electronic gaming machines have been referred to as the 'crack-cocaine' of gambling. While this analogy has popular appeal, it is only recently that the scientific community has begun to investigate its validity. In line with the belief that electronic gambling has a higher 'addictive' potential than other forms of gambling, research has also begun to focus on identifying the characteristics of gaming machines that may be associated with problem gambling behaviour. This paper will review the different types of modern electronic gaming machines, and will use the introduction of gaming machines to Australia to examine the association between electronic gaming and problem gambling, with particular reference to the characteristics of modern electronic gaming machines. Despite overwhelming acceptance that gaming machines are associated with the highest level of problem gambling, the empirical literature provides inconclusive evidence to support the analogy linking electronic gaming to 'crack-cocaine'. Rigorous and systematic evaluation is required to establish definitively the absolute 'addictive' potential of gaming machines and the degree to which machine characteristics influence the development and maintenance of problem gambling behaviour.

  5. A Comparison of Earned Value Management and Earned Schedule as Schedule Predictors on DoD ACAT I Programs

    DTIC Science & Technology

    2013-03-01

    33 Mario Vanhoucke and Stephan Vandevoorde – “Measuring the Accuracy of Earned Value/Earned Schedule Forecasting Predictors” (2007...technical problem to the present day ‘ super projects’” (Clark and Lorenzoni, 1997: 2). Cost engineering has “application regardless of industry...large construction projects, but also the acceptance of earned schedule principles on an international scale. Mario Vanhoucke and Stephan Vandevoorde

  6. Further Evaluation of the Use of Multiple Schedules for Behavior Maintained by Negative Reinforcement.

    PubMed

    Campos, Claudia; Leon, Yanerys; Sleiman, Andressa; Urcuyo, Beatriz

    2017-03-01

    One potential limitation of functional communication training (FCT) is that after the functional communication response (FCR) is taught, the response may be emitted at high rates or inappropriate times. Thus, schedule thinning is often necessary. Previous research has demonstrated that multiple schedules can facilitate schedule thinning by establishing discriminative control of the communication response while maintaining low rates of problem behavior. To date, most applied research evaluating the clinical utility of multiple schedules has done so in the context of behavior maintained by positive reinforcement (e.g., attention or tangible items). This study examined the use of a multiple schedule with alternating Fixed Ratio (FR 1)/extinction (EXT) components for two individuals with developmental disabilities who emitted escape-maintained problem behavior. Although problem behavior remained low during all FCT and multiple schedule phases, the use of the multiple schedule alone did not result in discriminated manding.

  7. Systemic Sustainability in RtI Using Intervention-Based Scheduling Methodologies

    ERIC Educational Resources Information Center

    Dallas, William P.

    2017-01-01

    This study evaluated a scheduling methodology referred to as intervention-based scheduling to address the problem of practice regarding the fidelity of implementing Response to Intervention (RtI) in an existing school schedule design. Employing panel data, this study used fixed-effects regressions and first differences ordinary least squares (OLS)…

  8. Using the principles of circadian physiology enhances shift schedule design

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Connolly, J.J.; Moore-Ede, M.C.

    1987-01-01

    Nuclear power plants must operate 24 h, 7 days a week. For the most part, shift schedules currently in use at nuclear power plants have been designed to meet operational needs without considering the biological clocks of the human operators. The development of schedules that also take circadian principles into account is a positive step that can be taken to improve plant safety by optimizing operator alertness. These schedules reduce the probability of human errors especially during backshifts. In addition, training programs that teach round-the-clock workers how to deal with the problems of shiftwork can help to optimize performance andmore » alertness. These programs teach shiftworkers the underlying causes of the sleep problems associated with shiftwork and also provide coping strategies for improving sleep and dealing with the transition between shifts. When these training programs are coupled with an improved schedule, the problems associated with working round-the-clock can be significantly reduced.« less

  9. Dynamic scheduling and planning parallel observations on large Radio Telescope Arrays with the Square Kilometre Array in mind

    NASA Astrophysics Data System (ADS)

    Buchner, Johannes

    2011-12-01

    Scheduling, the task of producing a time table for resources and tasks, is well-known to be a difficult problem the more resources are involved (a NP-hard problem). This is about to become an issue in Radio astronomy as observatories consisting of hundreds to thousands of telescopes are planned and operated. The Square Kilometre Array (SKA), which Australia and New Zealand bid to host, is aiming for scales where current approaches -- in construction, operation but also scheduling -- are insufficent. Although manual scheduling is common today, the problem is becoming complicated by the demand for (1) independent sub-arrays doing simultaneous observations, which requires the scheduler to plan parallel observations and (2) dynamic re-scheduling on changed conditions. Both of these requirements apply to the SKA, especially in the construction phase. We review the scheduling approaches taken in the astronomy literature, as well as investigate techniques from human schedulers and today's observatories. The scheduling problem is specified in general for scientific observations and in particular on radio telescope arrays. Also taken into account is the fact that the observatory may be oversubscribed, requiring the scheduling problem to be integrated with a planning process. We solve this long-term scheduling problem using a time-based encoding that works in the very general case of observation scheduling. This research then compares algorithms from various approaches, including fast heuristics from CPU scheduling, Linear Integer Programming and Genetic algorithms, Branch-and-Bound enumeration schemes. Measures include not only goodness of the solution, but also scalability and re-scheduling capabilities. In conclusion, we have identified a fast and good scheduling approach that allows (re-)scheduling difficult and changing problems by combining heuristics with a Genetic algorithm using block-wise mutation operations. We are able to explain and eradicate two problems in the

  10. Technology for planning and scheduling under complex constraints

    NASA Astrophysics Data System (ADS)

    Alguire, Karen M.; Pedro Gomes, Carla O.

    1997-02-01

    Within the context of law enforcement, several problems fall into the category of planning and scheduling under constraints. Examples include resource and personnel scheduling, and court scheduling. In the case of court scheduling, a schedule must be generated considering available resources, e.g., court rooms and personnel. Additionally, there are constraints on individual court cases, e.g., temporal and spatial, and between different cases, e.g., precedence. Finally, there are overall objectives that the schedule should satisfy such as timely processing of cases and optimal use of court facilities. Manually generating a schedule that satisfies all of the constraints is a very time consuming task. As the number of court cases and constraints increases, this becomes increasingly harder to handle without the assistance of automatic scheduling techniques. This paper describes artificial intelligence (AI) technology that has been used to develop several high performance scheduling applications including a military transportation scheduler, a military in-theater airlift scheduler, and a nuclear power plant outage scheduler. We discuss possible law enforcement applications where we feel the same technology could provide long-term benefits to law enforcement agencies and their operations personnel.

  11. Electric power scheduling: A distributed problem-solving approach

    NASA Technical Reports Server (NTRS)

    Mellor, Pamela A.; Dolce, James L.; Krupp, Joseph C.

    1990-01-01

    Space Station Freedom's power system, along with the spacecraft's other subsystems, needs to carefully conserve its resources and yet strive to maximize overall Station productivity. Due to Freedom's distributed design, each subsystem must work cooperatively within the Station community. There is a need for a scheduling tool which will preserve this distributed structure, allow each subsystem the latitude to satisfy its own constraints, and preserve individual value systems while maintaining Station-wide integrity. The value-driven free-market economic model is such a tool.

  12. Autonomous planning and scheduling on the TechSat 21 mission

    NASA Technical Reports Server (NTRS)

    Sherwood, R.; Chien, S.; Castano, R.; Rabideau, G.

    2002-01-01

    The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting.

  13. Intercell scheduling: A negotiation approach using multi-agent coalitions

    NASA Astrophysics Data System (ADS)

    Tian, Yunna; Li, Dongni; Zheng, Dan; Jia, Yunde

    2016-10-01

    Intercell scheduling problems arise as a result of intercell transfers in cellular manufacturing systems. Flexible intercell routes are considered in this article, and a coalition-based scheduling (CBS) approach using distributed multi-agent negotiation is developed. Taking advantage of the extended vision of the coalition agents, the global optimization is improved and the communication cost is reduced. The objective of the addressed problem is to minimize mean tardiness. Computational results show that, compared with the widely used combinatorial rules, CBS provides better performance not only in minimizing the objective, i.e. mean tardiness, but also in minimizing auxiliary measures such as maximum completion time, mean flow time and the ratio of tardy parts. Moreover, CBS is better than the existing intercell scheduling approach for the same problem with respect to the solution quality and computational costs.

  14. A new distributed systems scheduling algorithm: a swarm intelligence approach

    NASA Astrophysics Data System (ADS)

    Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi

    2011-12-01

    The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.

  15. Naval Postgraduate School Scheduling Support System (NPS4)

    DTIC Science & Technology

    1992-03-01

    NPSS ...... .................. 156 2. Final Exam Scheduler .. .......... 159 F. PRESENTATION SYSTEM ... ............. . 160 G. USER INTERFACE... NPSS ...... .................. 185 2. Final Exam Model ... ............ 186 3. The Class Schedulers .. .......... 186 4. Assessment of Problem Model...Information Distribution ....... 150 4.13 NPSS Optimization Process .... ............ . 157 4.14 NPSS Performance ..... ................ . 159 4.15 Department

  16. Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times.

    PubMed

    Yang, Xin; Zeng, Zhenxiang; Wang, Ruidong; Sun, Xueshan

    2016-01-01

    This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems.

  17. Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times

    PubMed Central

    Zeng, Zhenxiang; Wang, Ruidong; Sun, Xueshan

    2016-01-01

    This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems. PMID:27907163

  18. Scheduling IT Staff at a Bank: A Mathematical Programming Approach

    PubMed Central

    Labidi, M.; Mrad, M.; Gharbi, A.; Louly, M. A.

    2014-01-01

    We address a real-world optimization problem: the scheduling of a Bank Information Technologies (IT) staff. This problem can be defined as the process of constructing optimized work schedules for staff. In a general sense, it requires the allocation of suitably qualified staff to specific shifts to meet the demands for services of an organization while observing workplace regulations and attempting to satisfy individual work preferences. A monthly shift schedule is prepared to determine the shift duties of each staff considering shift coverage requirements, seniority-based workload rules, and staff work preferences. Due to the large number of conflicting constraints, a multiobjective programming model has been proposed to automate the schedule generation process. The suggested mathematical model has been implemented using Lingo software. The results indicate that high quality solutions can be obtained within a few seconds compared to the manually prepared schedules. PMID:24772032

  19. Scheduling for energy and reliability management on multiprocessor real-time systems

    NASA Astrophysics Data System (ADS)

    Qi, Xuan

    Scheduling algorithms for multiprocessor real-time systems have been studied for years with many well-recognized algorithms proposed. However, it is still an evolving research area and many problems remain open due to their intrinsic complexities. With the emergence of multicore processors, it is necessary to re-investigate the scheduling problems and design/develop efficient algorithms for better system utilization, low scheduling overhead, high energy efficiency, and better system reliability. Focusing cluster schedulings with optimal global schedulers, we study the utilization bound and scheduling overhead for a class of cluster-optimal schedulers. Then, taking energy/power consumption into consideration, we developed energy-efficient scheduling algorithms for real-time systems, especially for the proliferating embedded systems with limited energy budget. As the commonly deployed energy-saving technique (e.g. dynamic voltage frequency scaling (DVFS)) will significantly affect system reliability, we study schedulers that have intelligent mechanisms to recuperate system reliability to satisfy the quality assurance requirements. Extensive simulation is conducted to evaluate the performance of the proposed algorithms on reduction of scheduling overhead, energy saving, and reliability improvement. The simulation results show that the proposed reliability-aware power management schemes could preserve the system reliability while still achieving substantial energy saving.

  20. Anaesthesia machine: checklist, hazards, scavenging.

    PubMed

    Goneppanavar, Umesh; Prabhu, Manjunath

    2013-09-01

    From a simple pneumatic device of the early 20(th) century, the anaesthesia machine has evolved to incorporate various mechanical, electrical and electronic components to be more appropriately called anaesthesia workstation. Modern machines have overcome many drawbacks associated with the older machines. However, addition of several mechanical, electronic and electric components has contributed to recurrence of some of the older problems such as leak or obstruction attributable to newer gadgets and development of newer problems. No single checklist can satisfactorily test the integrity and safety of all existing anaesthesia machines due to their complex nature as well as variations in design among manufacturers. Human factors have contributed to greater complications than machine faults. Therefore, better understanding of the basics of anaesthesia machine and checking each component of the machine for proper functioning prior to use is essential to minimise these hazards. Clear documentation of regular and appropriate servicing of the anaesthesia machine, its components and their satisfactory functioning following servicing and repair is also equally important. Trace anaesthetic gases polluting the theatre atmosphere can have several adverse effects on the health of theatre personnel. Therefore, safe disposal of these gases away from the workplace with efficiently functioning scavenging system is necessary. Other ways of minimising atmospheric pollution such as gas delivery equipment with negligible leaks, low flow anaesthesia, minimal leak around the airway equipment (facemask, tracheal tube, laryngeal mask airway, etc.) more than 15 air changes/hour and total intravenous anaesthesia should also be considered.

  1. Anaesthesia Machine: Checklist, Hazards, Scavenging

    PubMed Central

    Goneppanavar, Umesh; Prabhu, Manjunath

    2013-01-01

    From a simple pneumatic device of the early 20th century, the anaesthesia machine has evolved to incorporate various mechanical, electrical and electronic components to be more appropriately called anaesthesia workstation. Modern machines have overcome many drawbacks associated with the older machines. However, addition of several mechanical, electronic and electric components has contributed to recurrence of some of the older problems such as leak or obstruction attributable to newer gadgets and development of newer problems. No single checklist can satisfactorily test the integrity and safety of all existing anaesthesia machines due to their complex nature as well as variations in design among manufacturers. Human factors have contributed to greater complications than machine faults. Therefore, better understanding of the basics of anaesthesia machine and checking each component of the machine for proper functioning prior to use is essential to minimise these hazards. Clear documentation of regular and appropriate servicing of the anaesthesia machine, its components and their satisfactory functioning following servicing and repair is also equally important. Trace anaesthetic gases polluting the theatre atmosphere can have several adverse effects on the health of theatre personnel. Therefore, safe disposal of these gases away from the workplace with efficiently functioning scavenging system is necessary. Other ways of minimising atmospheric pollution such as gas delivery equipment with negligible leaks, low flow anaesthesia, minimal leak around the airway equipment (facemask, tracheal tube, laryngeal mask airway, etc.) more than 15 air changes/hour and total intravenous anaesthesia should also be considered. PMID:24249887

  2. Scheduling in the Face of Uncertain Resource Consumption and Utility

    NASA Technical Reports Server (NTRS)

    Koga, Dennis (Technical Monitor); Frank, Jeremy; Dearden, Richard

    2003-01-01

    We discuss the problem of scheduling tasks that consume a resource with known capacity and where the tasks have varying utility. We consider problems in which the resource consumption and utility of each activity is described by probability distributions. In these circumstances, we would like to find schedules that exceed a lower bound on the expected utility when executed. We first show that while some of these problems are NP-complete, others are only NP-Hard. We then describe various heuristic search algorithms to solve these problems and their drawbacks. Finally, we present empirical results that characterize the behavior of these heuristics over a variety of problem classes.

  3. Solution and reasoning reuse in space planning and scheduling applications

    NASA Technical Reports Server (NTRS)

    Verfaillie, Gerard; Schiex, Thomas

    1994-01-01

    In the space domain, as in other domains, the CSP (Constraint Satisfaction Problems) techniques are increasingly used to represent and solve planning and scheduling problems. But these techniques have been developed to solve CSP's which are composed of fixed sets of variables and constraints, whereas many planning and scheduling problems are dynamic. It is therefore important to develop methods which allow a new solution to be rapidly found, as close as possible to the previous one, when some variables or constraints are added or removed. After presenting some existing approaches, this paper proposes a simple and efficient method, which has been developed on the basis of the dynamic backtracking algorithm. This method allows previous solution and reasoning to be reused in the framework of a CSP which is close to the previous one. Some experimental results on general random CSPs and on operation scheduling problems for remote sensing satellites are given.

  4. Scheduling of an aircraft fleet

    NASA Technical Reports Server (NTRS)

    Paltrinieri, Massimo; Momigliano, Alberto; Torquati, Franco

    1992-01-01

    Scheduling is the task of assigning resources to operations. When the resources are mobile vehicles, they describe routes through the served stations. To emphasize such aspect, this problem is usually referred to as the routing problem. In particular, if vehicles are aircraft and stations are airports, the problem is known as aircraft routing. This paper describes the solution to such a problem developed in OMAR (Operative Management of Aircraft Routing), a system implemented by Bull HN for Alitalia. In our approach, aircraft routing is viewed as a Constraint Satisfaction Problem. The solving strategy combines network consistency and tree search techniques.

  5. A multi-group and preemptable scheduling of cloud resource based on HTCondor

    NASA Astrophysics Data System (ADS)

    Jiang, Xiaowei; Zou, Jiaheng; Cheng, Yaodong; Shi, Jingyan

    2017-10-01

    Due to the features of virtual machine-flexibility, easy controlling and various system environments, more and more fields utilize the virtualization technology to construct the distributed system with the virtual resources, also including high energy physics. This paper introduce a method used in high energy physics that supports multiple resource group and preemptable cloud resource scheduling, combining virtual machine with HTCondor (a batch system). It makes resource controlling more flexible and more efficient and makes resource scheduling independent of job scheduling. Firstly, the resources belong to different experiment-groups, and the type of user-groups mapping to resource-groups(same as experiment-group) is one-to-one or many-to-one. In order to make the confused group simply to be managed, we designed the permission controlling component to ensure that the different resource-groups can get the suitable jobs. Secondly, for the purpose of elastically allocating resources for suitable resource-group, it is necessary to schedule resources like scheduling jobs. So this paper designs the cloud resource scheduling to maintain a resource queue and allocate an appropriate amount of virtual resources to the request resource-group. Thirdly, in some kind of situations, because of the resource occupied for a long time, resources need to be preempted. This paper adds the preemption function for the resource scheduling that implement resource preemption based on the group priority. Additionally, the way to preempting is soft that when virtual resources are preempted, jobs will not be killed but also be held and rematched later. It is implemented with the help of HTCondor, storing the held job information in scheduler, releasing the job to idle status and doing second matcher. In IHEP (institute of high energy physics), we have built a batch system based on HTCondor with a virtual resources pool based on Openstack. And this paper will show some cases of experiment JUNO

  6. Scheduling language and algorithm development study. Volume 1, phase 2: Design considerations for a scheduling and resource allocation system

    NASA Technical Reports Server (NTRS)

    Morrell, R. A.; Odoherty, R. J.; Ramsey, H. R.; Reynolds, C. C.; Willoughby, J. K.; Working, R. D.

    1975-01-01

    Data and analyses related to a variety of algorithms for solving typical large-scale scheduling and resource allocation problems are presented. The capabilities and deficiencies of various alternative problem solving strategies are discussed from the viewpoint of computer system design.

  7. New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

    PubMed Central

    Li, Xiguang; Zhao, Liang; Gong, Changqing; Liu, Xiaojing

    2017-01-01

    Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA), is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM) for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent. PMID:29085425

  8. Nurse Scheduling by Cooperative GA with Effective Mutation Operator

    NASA Astrophysics Data System (ADS)

    Ohki, Makoto

    In this paper, we propose an effective mutation operators for Cooperative Genetic Algorithm (CGA) to be applied to a practical Nurse Scheduling Problem (NSP). The nurse scheduling is a very difficult task, because NSP is a complex combinatorial optimizing problem for which many requirements must be considered. In real hospitals, the schedule changes frequently. The changes of the shift schedule yields various problems, for example, a fall in the nursing level. We describe a technique of the reoptimization of the nurse schedule in response to a change. The conventional CGA is superior in ability for local search by means of its crossover operator, but often stagnates at the unfavorable situation because it is inferior to ability for global search. When the optimization stagnates for long generation cycle, a searching point, population in this case, would be caught in a wide local minimum area. To escape such local minimum area, small change in a population should be required. Based on such consideration, we propose a mutation operator activated depending on the optimization speed. When the optimization stagnates, in other words, when the optimization speed decreases, the mutation yields small changes in the population. Then the population is able to escape from a local minimum area by means of the mutation. However, this mutation operator requires two well-defined parameters. This means that user have to consider the value of these parameters carefully. To solve this problem, we propose a periodic mutation operator which has only one parameter to define itself. This simplified mutation operator is effective over a wide range of the parameter value.

  9. Optimizing an F-16 Squadron Weekly Pilot Schedule for the Turkish Air Force

    DTIC Science & Technology

    2010-03-01

    disrupted schedules are rescheduled , minimizing the total number of changes with respect to the previous schedule’s objective function. Output...producing rosters for a nursing staff in a large general hospital (Dowsland, 1998) and afterwards Aickelin and Dowsland use an Indirect Genetic...algorithm to improve the solutions of the nurse scheduling problem which is similar to the fighter squadron pilot scheduling problem (Aickelin and

  10. Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction

    NASA Astrophysics Data System (ADS)

    Baniamerian, Ali; Bashiri, Mahdi; Zabihi, Fahime

    2018-03-01

    Cross-docking is a new warehousing policy in logistics which is widely used all over the world and attracts many researchers attention to study about in last decade. In the literature, economic aspects has been often studied, while one of the most significant factors for being successful in the competitive global market is improving quality of customer servicing and focusing on customer satisfaction. In this paper, we introduce a vehicle routing and scheduling problem with cross-docking and time windows in a three-echelon supply chain that considers customer satisfaction. A set of homogeneous vehicles collect products from suppliers and after consolidation process in the cross-dock, immediately deliver them to customers. A mixed integer linear programming model is presented for this problem to minimize transportation cost and early/tardy deliveries with scheduling of inbound and outbound vehicles to increase customer satisfaction. A two phase genetic algorithm (GA) is developed for the problem. For investigating the performance of the algorithm, it was compared with exact and lower bound solutions in small and large-size instances, respectively. Results show that there are at least 86.6% customer satisfaction by the proposed method, whereas customer satisfaction in the classical model is at most 33.3%. Numerical examples results show that the proposed two phase algorithm could achieve optimal solutions in small-size instances. Also in large-size instances, the proposed two phase algorithm could achieve better solutions with less gap from the lower bound in less computational time in comparison with the classic GA.

  11. Multi-Stage Convex Relaxation Methods for Machine Learning

    DTIC Science & Technology

    2013-03-01

    Many problems in machine learning can be naturally formulated as non-convex optimization problems. However, such direct nonconvex formulations have...original nonconvex formulation. We will develop theoretical properties of this method and algorithmic consequences. Related convex and nonconvex machine learning methods will also be investigated.

  12. Advance Resource Provisioning in Bulk Data Scheduling

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Balman, Mehmet

    2012-10-01

    Today?s scientific and business applications generate mas- sive data sets that need to be transferred to remote sites for sharing, processing, and long term storage. Because of increasing data volumes and enhancement in current net- work technology that provide on-demand high-speed data access between collaborating institutions, data handling and scheduling problems have reached a new scale. In this paper, we present a new data scheduling model with ad- vance resource provisioning, in which data movement operations are defined with earliest start and latest comple- tion times. We analyze time-dependent resource assign- ment problem, and propose a new methodology to improvemore » the current systems by allowing researchers and higher-level meta-schedulers to use data-placement as-a-service, so they can plan ahead and submit transfer requests in advance. In general, scheduling with time and resource conflicts is NP-hard. We introduce an efficient algorithm to organize multiple requests on the fly, while satisfying users? time and resource constraints. We successfully tested our algorithm in a simple benchmark simulator that we have developed, and demonstrated its performance with initial test results.« less

  13. Model-based machine learning.

    PubMed

    Bishop, Christopher M

    2013-02-13

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

  14. Model-based machine learning

    PubMed Central

    Bishop, Christopher M.

    2013-01-01

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

  15. Automated Long - Term Scheduling for the SOFIA Airborne Observatory

    NASA Technical Reports Server (NTRS)

    Civeit, Thomas

    2013-01-01

    The NASA Stratospheric Observatory for Infrared Astronomy (SOFIA) is a joint US/German project to develop and operate a gyro-stabilized 2.5-meter telescope in a Boeing 747SP. SOFIA's first science observations were made in December 2010. During 2011, SOFIA accomplished 30 flights in the "Early Science" program as well as a deployment to Germany. The new observing period, known as Cycle 1, is scheduled to begin in 2012. It includes 46 science flights grouped in four multi-week observing campaigns spread through a 13-month span. Automation of the flight scheduling process offers a major challenge to the SOFIA mission operations. First because it is needed to mitigate its relatively high cost per unit observing time compared to space-borne missions. Second because automated scheduling techniques available for ground-based and space-based telescopes are inappropriate for an airborne observatory. Although serious attempts have been made in the past to solve part of the problem, until recently mission operations staff was still manually scheduling flights. We present in this paper a new automated solution for generating SOFIA long-term schedules that will be used in operations from the Cycle 1 observing period. We describe the constraints that should be satisfied to solve the SOFIA scheduling problem in the context of real operations. We establish key formulas required to efficiently calculate the aircraft course over ground when evaluating flight schedules. We describe the foundations of the SOFIA long-term scheduler, the constraint representation, and the random search based algorithm that generates observation and instrument schedules. Finally, we report on how the new long-term scheduler has been used in operations to date.

  16. Spike: AI scheduling for Hubble Space Telescope after 18 months of orbital operations

    NASA Technical Reports Server (NTRS)

    Johnston, Mark D.

    1992-01-01

    This paper is a progress report on the Spike scheduling system, developed by the Space Telescope Science Institute for long-term scheduling of Hubble Space Telescope (HST) observations. Spike is an activity-based scheduler which exploits artificial intelligence (AI) techniques for constraint representation and for scheduling search. The system has been in operational use since shortly after HST launch in April 1990. Spike was adopted for several other satellite scheduling problems; of particular interest was the demonstration that the Spike framework is sufficiently flexible to handle both long-term and short-term scheduling, on timescales of years down to minutes or less. We describe the recent progress made in scheduling search techniques, the lessons learned from early HST operations, and the application of Spike to other problem domains. We also describe plans for the future evolution of the system.

  17. Scheduling Operations for Massive Heterogeneous Clusters

    NASA Technical Reports Server (NTRS)

    Humphrey, John; Spagnoli, Kyle

    2013-01-01

    High-performance computing (HPC) programming has become increasingly difficult with the advent of hybrid supercomputers consisting of multicore CPUs and accelerator boards such as the GPU. Manual tuning of software to achieve high performance on this type of machine has been performed by programmers. This is needlessly difficult and prone to being invalidated by new hardware, new software, or changes in the underlying code. A system was developed for task-based representation of programs, which when coupled with a scheduler and runtime system, allows for many benefits, including higher performance and utilization of computational resources, easier programming and porting, and adaptations of code during runtime. The system consists of a method of representing computer algorithms as a series of data-dependent tasks. The series forms a graph, which can be scheduled for execution on many nodes of a supercomputer efficiently by a computer algorithm. The schedule is executed by a dispatch component, which is tailored to understand all of the hardware types that may be available within the system. The scheduler is informed by a cluster mapping tool, which generates a topology of available resources and their strengths and communication costs. Software is decoupled from its hardware, which aids in porting to future architectures. A computer algorithm schedules all operations, which for systems of high complexity (i.e., most NASA codes), cannot be performed optimally by a human. The system aids in reducing repetitive code, such as communication code, and aids in the reduction of redundant code across projects. It adds new features to code automatically, such as recovering from a lost node or the ability to modify the code while running. In this project, the innovators at the time of this reporting intend to develop two distinct technologies that build upon each other and both of which serve as building blocks for more efficient HPC usage. First is the scheduling and dynamic

  18. EUROPA2: Plan Database Services for Planning and Scheduling Applications

    NASA Technical Reports Server (NTRS)

    Bedrax-Weiss, Tania; Frank, Jeremy; Jonsson, Ari; McGann, Conor

    2004-01-01

    NASA missions require solving a wide variety of planning and scheduling problems with temporal constraints; simple resources such as robotic arms, communications antennae and cameras; complex replenishable resources such as memory, power and fuel; and complex constraints on geometry, heat and lighting angles. Planners and schedulers that solve these problems are used in ground tools as well as onboard systems. The diversity of planning problems and applications of planners and schedulers precludes a one-size fits all solution. However, many of the underlying technologies are common across planning domains and applications. We describe CAPR, a formalism for planning that is general enough to cover a wide variety of planning and scheduling domains of interest to NASA. We then describe EUROPA(sub 2), a software framework implementing CAPR. EUROPA(sub 2) provides efficient, customizable Plan Database Services that enable the integration of CAPR into a wide variety of applications. We describe the design of EUROPA(sub 2) from the perspective of both modeling, customization and application integration to different classes of NASA missions.

  19. Electricity usage scheduling in smart building environments using smart devices.

    PubMed

    Lee, Eunji; Bahn, Hyokyung

    2013-01-01

    With the recent advances in smart grid technologies as well as the increasing dissemination of smart meters, the electricity usage of every moment can be detected in modern smart building environments. Thus, the utility company adopts different price of electricity at each time slot considering the peak time. This paper presents a new electricity usage scheduling algorithm for smart buildings that adopts real-time pricing of electricity. The proposed algorithm detects the change of electricity prices by making use of a smart device and changes the power mode of each electric device dynamically. Specifically, we formulate the electricity usage scheduling problem as a real-time task scheduling problem and show that it is a complex search problem that has an exponential time complexity. An efficient heuristic based on genetic algorithms is performed on a smart device to cut down the huge searching space and find a reasonable schedule within a feasible time budget. Experimental results with various building conditions show that the proposed algorithm reduces the electricity charge of a smart building by 25.6% on average and up to 33.4%.

  20. A hybrid online scheduling mechanism with revision and progressive techniques for autonomous Earth observation satellite

    NASA Astrophysics Data System (ADS)

    Li, Guoliang; Xing, Lining; Chen, Yingwu

    2017-11-01

    The autonomicity of self-scheduling on Earth observation satellite and the increasing scale of satellite network attract much attention from researchers in the last decades. In reality, the limited onboard computational resource presents challenge for the online scheduling algorithm. This study considered online scheduling problem for a single autonomous Earth observation satellite within satellite network environment. It especially addressed that the urgent tasks arrive stochastically during the scheduling horizon. We described the problem and proposed a hybrid online scheduling mechanism with revision and progressive techniques to solve this problem. The mechanism includes two decision policies, a when-to-schedule policy combining periodic scheduling and critical cumulative number-based event-driven rescheduling, and a how-to-schedule policy combining progressive and revision approaches to accommodate two categories of task: normal tasks and urgent tasks. Thus, we developed two heuristic (re)scheduling algorithms and compared them with other generally used techniques. Computational experiments indicated that the into-scheduling percentage of urgent tasks in the proposed mechanism is much higher than that in periodic scheduling mechanism, and the specific performance is highly dependent on some mechanism-relevant and task-relevant factors. For the online scheduling, the modified weighted shortest imaging time first and dynamic profit system benefit heuristics outperformed the others on total profit and the percentage of successfully scheduled urgent tasks.

  1. Machine vision and appearance based learning

    NASA Astrophysics Data System (ADS)

    Bernstein, Alexander

    2017-03-01

    Smart algorithms are used in Machine vision to organize or extract high-level information from the available data. The resulted high-level understanding the content of images received from certain visual sensing system and belonged to an appearance space can be only a key first step in solving various specific tasks such as mobile robot navigation in uncertain environments, road detection in autonomous driving systems, etc. Appearance-based learning has become very popular in the field of machine vision. In general, the appearance of a scene is a function of the scene content, the lighting conditions, and the camera position. Mobile robots localization problem in machine learning framework via appearance space analysis is considered. This problem is reduced to certain regression on an appearance manifold problem, and newly regression on manifolds methods are used for its solution.

  2. Uncertainty management by relaxation of conflicting constraints in production process scheduling

    NASA Technical Reports Server (NTRS)

    Dorn, Juergen; Slany, Wolfgang; Stary, Christian

    1992-01-01

    Mathematical-analytical methods as used in Operations Research approaches are often insufficient for scheduling problems. This is due to three reasons: the combinatorial complexity of the search space, conflicting objectives for production optimization, and the uncertainty in the production process. Knowledge-based techniques, especially approximate reasoning and constraint relaxation, are promising ways to overcome these problems. A case study from an industrial CIM environment, namely high-grade steel production, is presented to demonstrate how knowledge-based scheduling with the desired capabilities could work. By using fuzzy set theory, the applied knowledge representation technique covers the uncertainty inherent in the problem domain. Based on this knowledge representation, a classification of jobs according to their importance is defined which is then used for the straightforward generation of a schedule. A control strategy which comprises organizational, spatial, temporal, and chemical constraints is introduced. The strategy supports the dynamic relaxation of conflicting constraints in order to improve tentative schedules.

  3. Real-time Scheduling for GPUS with Applications in Advanced Automotive Systems

    DTIC Science & Technology

    2015-01-01

    129 3.7 Architecture of GPU tasklet scheduling infrastructure ...throughput. This disparity is even greater when we consider mobile CPUs, such as those designed by ARM. For instance, the ARM Cortex-A15 series processor as...stub library that replaces the GPGPU runtime within each virtual machine. The stub library communicates API calls to a GPGPU backend user-space daemon

  4. SARDA Surface Schedulers

    NASA Technical Reports Server (NTRS)

    Malik, Waqar

    2016-01-01

    Provide an overview of algorithms used in SARDA (Spot and Runway Departure Advisor) HITL (Human-in-the-Loop) simulation for Dallas Fort-Worth International Airport and Charlotte Douglas International airport. Outline a multi-objective dynamic programming (DP) based algorithm that finds the exact solution to the single runway scheduling (SRS) problem, and discuss heuristics to restrict the search space for the DP based algorithm and provide improvements.

  5. CPAP Machines: Tips for Avoiding 10 Common Problems

    MedlinePlus

    ... closed and reduce the air leak if you wear a nasal mask. A full-face-mask-style device that covers your mouth and nose also may work well for you. A CPAP-heated humidifier that attaches to the air pressure machine also may help. It's normal to sometimes wake ...

  6. Utilizing AI in Temporal, Spatial, and Resource Scheduling

    NASA Technical Reports Server (NTRS)

    Stottler, Richard; Kalton, Annaka; Bell, Aaron

    2006-01-01

    Aurora is a software system enabling the rapid, easy solution of complex scheduling problems involving spatial and temporal constraints among operations and scarce resources (such as equipment, workspace, and human experts). Although developed for use in the International Space Station Processing Facility, Aurora is flexible enough that it can be easily customized for application to other scheduling domains and adapted as the requirements change or become more precisely known over time. Aurora s scheduling module utilizes artificial-intelligence (AI) techniques to make scheduling decisions on the basis of domain knowledge, including knowledge of constraints and their relative importance, interdependencies among operations, and possibly frequent changes in governing schedule requirements. Unlike many other scheduling software systems, Aurora focuses on resource requirements and temporal scheduling in combination. For example, Aurora can accommodate a domain requirement to schedule two subsequent operations to locations adjacent to a shared resource. The graphical interface allows the user to quickly visualize the schedule and perform changes reflecting additional knowledge or alterations in the situation. For example, the user might drag the activity corresponding to the start of operations to reflect a late delivery.

  7. Hypertext-based design of a user interface for scheduling

    NASA Technical Reports Server (NTRS)

    Woerner, Irene W.; Biefeld, Eric

    1993-01-01

    Operations Mission Planner (OMP) is an ongoing research project at JPL that utilizes AI techniques to create an intelligent, automated planning and scheduling system. The information space reflects the complexity and diversity of tasks necessary in most real-world scheduling problems. Thus the problem of the user interface is to present as much information as possible at a given moment and allow the user to quickly navigate through the various types of displays. This paper describes a design which applies the hypertext model to solve these user interface problems. The general paradigm is to provide maps and search queries to allow the user to quickly find an interesting conflict or problem, and then allow the user to navigate through the displays in a hypertext fashion.

  8. Scheduling the resident 80-hour work week: an operations research algorithm.

    PubMed

    Day, T Eugene; Napoli, Joseph T; Kuo, Paul C

    2006-01-01

    The resident 80-hour work week requires that programs now schedule duty hours. Typically, scheduling is performed in an empirical "trial-and-error" fashion. However, this is a classic "scheduling" problem from the field of operations research (OR). It is similar to scheduling issues that airlines must face with pilots and planes routing through various airports at various times. The authors hypothesized that an OR approach using iterative computer algorithms could provide a rational scheduling solution. Institution-specific constraints of the residency problem were formulated. A total of 56 residents are rotating through 4 hospitals. Additional constraints were dictated by the Residency Review Committee (RRC) rules or the specific surgical service. For example, at Hospital 1, during the weekday hours between 6 am and 6 pm, there will be a PGY4 or PGY5 and a PGY2 or PGY3 on-duty to cover Service "A." A series of equations and logic statements was generated to satisfy all constraints and requirements. These were restated in the Optimization Programming Language used by the ILOG software suite for solving mixed integer programming problems. An integer programming solution was generated to this resource-constrained assignment problem. A total of 30,900 variables and 12,443 constraints were required. A total of man-hours of programming were used; computer run-time was 25.9 hours. A weekly schedule was generated for each resident that satisfied the RRC regulations while fulfilling all stated surgical service requirements. Each required between 64 and 80 weekly resident duty hours. The authors conclude that OR is a viable approach to schedule resident work hours. This technique is sufficiently robust to accommodate changes in resident numbers, service requirements, and service and hospital rotations.

  9. Service-Oriented Node Scheduling Scheme for Wireless Sensor Networks Using Markov Random Field Model

    PubMed Central

    Cheng, Hongju; Su, Zhihuang; Lloret, Jaime; Chen, Guolong

    2014-01-01

    Future wireless sensor networks are expected to provide various sensing services and energy efficiency is one of the most important criterions. The node scheduling strategy aims to increase network lifetime by selecting a set of sensor nodes to provide the required sensing services in a periodic manner. In this paper, we are concerned with the service-oriented node scheduling problem to provide multiple sensing services while maximizing the network lifetime. We firstly introduce how to model the data correlation for different services by using Markov Random Field (MRF) model. Secondly, we formulate the service-oriented node scheduling issue into three different problems, namely, the multi-service data denoising problem which aims at minimizing the noise level of sensed data, the representative node selection problem concerning with selecting a number of active nodes while determining the services they provide, and the multi-service node scheduling problem which aims at maximizing the network lifetime. Thirdly, we propose a Multi-service Data Denoising (MDD) algorithm, a novel multi-service Representative node Selection and service Determination (RSD) algorithm, and a novel MRF-based Multi-service Node Scheduling (MMNS) scheme to solve the above three problems respectively. Finally, extensive experiments demonstrate that the proposed scheme efficiently extends the network lifetime. PMID:25384005

  10. APGEN Scheduling: 15 Years of Experience in Planning Automation

    NASA Technical Reports Server (NTRS)

    Maldague, Pierre F.; Wissler, Steve; Lenda, Matthew; Finnerty, Daniel

    2014-01-01

    In this paper, we discuss the scheduling capability of APGEN (Activity Plan Generator), a multi-mission planning application that is part of the NASA AMMOS (Advanced Multi- Mission Operations System), and how APGEN scheduling evolved over its applications to specific Space Missions. Our analysis identifies two major reasons for the successful application of APGEN scheduling to real problems: an expressive DSL (Domain-Specific Language) for formulating scheduling algorithms, and a well-defined process for enlisting the help of auxiliary modeling tools in providing high-fidelity, system-level simulations of the combined spacecraft and ground support system.

  11. A Combined Adaptive Tabu Search and Set Partitioning Approach for the Crew Scheduling Problem with an Air Tanker Crew Application

    DTIC Science & Technology

    2002-08-15

    Agency Name(s) and Address(es) Maj Juan Vasquez AFOSR/NM 801 N. Randolph St., Rm 732 Arlington, VA 22203-1977 Sponsor/Monitor’s Acronym(s) Sponsor... Gelman , E., Patty, B., and R. Tanga. 1991. Recent Advances in Crew-Pairing Optimization at American Airlines, Interfaces, 21(1):62-74. Baker, E.K...Operations Research, 25(11):887-894. Chu, H.D., Gelman , E., and E.L. Johnson. 1997. Solving Large Scale Crew Scheduling Problems, European

  12. Efficient operation scheduling for adsorption chillers using predictive optimization-based control methods

    NASA Astrophysics Data System (ADS)

    Bürger, Adrian; Sawant, Parantapa; Bohlayer, Markus; Altmann-Dieses, Angelika; Braun, Marco; Diehl, Moritz

    2017-10-01

    Within this work, the benefits of using predictive control methods for the operation of Adsorption Cooling Machines (ACMs) are shown on a simulation study. Since the internal control decisions of series-manufactured ACMs often cannot be influenced, the work focuses on optimized scheduling of an ACM considering its internal functioning as well as forecasts for load and driving energy occurrence. For illustration, an assumed solar thermal climate system is introduced and a system model suitable for use within gradient-based optimization methods is developed. The results of a system simulation using a conventional scheme for ACM scheduling are compared to the results of a predictive, optimization-based scheduling approach for the same exemplary scenario of load and driving energy occurrence. The benefits of the latter approach are shown and future actions for application of these methods for system control are addressed.

  13. Simulation-driven machine learning: Bearing fault classification

    NASA Astrophysics Data System (ADS)

    Sobie, Cameron; Freitas, Carina; Nicolai, Mike

    2018-01-01

    Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.

  14. Communication Studies of DMP and SMP Machines

    NASA Technical Reports Server (NTRS)

    Sohn, Andrew; Biswas, Rupak; Chancellor, Marisa K. (Technical Monitor)

    1997-01-01

    Understanding the interplay between machines and problems is key to obtaining high performance on parallel machines. This paper investigates the interplay between programming paradigms and communication capabilities of parallel machines. In particular, we explicate the communication capabilities of the IBM SP-2 distributed-memory multiprocessor and the SGI PowerCHALLENGEarray symmetric multiprocessor. Two benchmark problems of bitonic sorting and Fast Fourier Transform are selected for experiments. Communication-efficient algorithms are developed to exploit the overlapping capabilities of the machines. Programs are written in Message-Passing Interface for portability and identical codes are used for both machines. Various data sizes and message sizes are used to test the machines' communication capabilities. Experimental results indicate that the communication performance of the multiprocessors are consistent with the size of messages. The SP-2 is sensitive to message size but yields a much higher communication overlapping because of the communication co-processor. The PowerCHALLENGEarray is not highly sensitive to message size and yields a low communication overlapping. Bitonic sorting yields lower performance compared to FFT due to a smaller computation-to-communication ratio.

  15. Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

    PubMed Central

    Kadima, Hubert; Granado, Bertrand

    2013-01-01

    We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach. PMID:24319361

  16. Multi-objective approach for energy-aware workflow scheduling in cloud computing environments.

    PubMed

    Yassa, Sonia; Chelouah, Rachid; Kadima, Hubert; Granado, Bertrand

    2013-01-01

    We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

  17. Workflow as a Service in the Cloud: Architecture and Scheduling Algorithms

    PubMed Central

    Wang, Jianwu; Korambath, Prakashan; Altintas, Ilkay; Davis, Jim; Crawl, Daniel

    2017-01-01

    With more and more workflow systems adopting cloud as their execution environment, it becomes increasingly challenging on how to efficiently manage various workflows, virtual machines (VMs) and workflow execution on VM instances. To make the system scalable and easy-to-extend, we design a Workflow as a Service (WFaaS) architecture with independent services. A core part of the architecture is how to efficiently respond continuous workflow requests from users and schedule their executions in the cloud. Based on different targets, we propose four heuristic workflow scheduling algorithms for the WFaaS architecture, and analyze the differences and best usages of the algorithms in terms of performance, cost and the price/performance ratio via experimental studies. PMID:29399237

  18. Workflow as a Service in the Cloud: Architecture and Scheduling Algorithms.

    PubMed

    Wang, Jianwu; Korambath, Prakashan; Altintas, Ilkay; Davis, Jim; Crawl, Daniel

    2014-01-01

    With more and more workflow systems adopting cloud as their execution environment, it becomes increasingly challenging on how to efficiently manage various workflows, virtual machines (VMs) and workflow execution on VM instances. To make the system scalable and easy-to-extend, we design a Workflow as a Service (WFaaS) architecture with independent services. A core part of the architecture is how to efficiently respond continuous workflow requests from users and schedule their executions in the cloud. Based on different targets, we propose four heuristic workflow scheduling algorithms for the WFaaS architecture, and analyze the differences and best usages of the algorithms in terms of performance, cost and the price/performance ratio via experimental studies.

  19. Enter the machine

    NASA Astrophysics Data System (ADS)

    Palittapongarnpim, Pantita; Sanders, Barry C.

    2018-05-01

    Quantum tomography infers quantum states from measurement data, but it becomes infeasible for large systems. Machine learning enables tomography of highly entangled many-body states and suggests a new powerful approach to this problem.

  20. Workshop on Fielded Applications of Machine Learning

    DTIC Science & Technology

    1994-05-11

    This report summaries the talks presented at the Workshop on Fielded Applications of Machine Learning , and draws some initial conclusions about the state of machine learning and its potential for solving real-world problems.

  1. Debugging Fortran on a shared memory machine

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Allen, T.R.; Padua, D.A.

    1987-01-01

    Debugging on a parallel processor is more difficult than debugging on a serial machine because errors in a parallel program may introduce nondeterminism. The approach to parallel debugging presented here attempts to reduce the problem of debugging on a parallel machine to that of debugging on a serial machine by automatically detecting nondeterminism. 20 refs., 6 figs.

  2. Optimisation of assembly scheduling in VCIM systems using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Dao, Son Duy; Abhary, Kazem; Marian, Romeo

    2017-09-01

    Assembly plays an important role in any production system as it constitutes a significant portion of the lead time and cost of a product. Virtual computer-integrated manufacturing (VCIM) system is a modern production system being conceptually developed to extend the application of traditional computer-integrated manufacturing (CIM) system to global level. Assembly scheduling in VCIM systems is quite different from one in traditional production systems because of the difference in the working principles of the two systems. In this article, the assembly scheduling problem in VCIM systems is modeled and then an integrated approach based on genetic algorithm (GA) is proposed to search for a global optimised solution to the problem. Because of dynamic nature of the scheduling problem, a novel GA with unique chromosome representation and modified genetic operations is developed herein. Robustness of the proposed approach is verified by a numerical example.

  3. Using Multiple Schedules During Functional Communication Training to Promote Rapid Transfer of Treatment Effects

    PubMed Central

    Fisher, Wayne W.; Greer, Brian D.; Fuhrman, Ashley M.; Querim, Angie C.

    2016-01-01

    Multiple schedules with signaled periods of reinforcement and extinction have been used to thin reinforcement schedules during functional communication training (FCT) to make the intervention more practical for parents and teachers. We evaluated whether these signals would also facilitate rapid transfer of treatment effects from one setting to the next and from one therapist to the next. With two children, we conducted FCT in the context of mixed (baseline) and multiple (treatment) schedules introduced across settings or therapists using a multiple baseline design. Results indicated that when the multiple schedules were introduced, the functional communication response came under rapid discriminative control, and problem behavior remained at near-zero rates. We extended these findings with another individual by using a more traditional baseline in which problem behavior produced reinforcement. Results replicated those of the previous participants and showed rapid reductions in problem behavior when multiple schedules were implemented across settings. PMID:26384141

  4. Using multiple schedules during functional communication training to promote rapid transfer of treatment effects.

    PubMed

    Fisher, Wayne W; Greer, Brian D; Fuhrman, Ashley M; Querim, Angie C

    2015-12-01

    Multiple schedules with signaled periods of reinforcement and extinction have been used to thin reinforcement schedules during functional communication training (FCT) to make the intervention more practical for parents and teachers. We evaluated whether these signals would also facilitate rapid transfer of treatment effects across settings and therapists. With 2 children, we conducted FCT in the context of mixed (baseline) and multiple (treatment) schedules introduced across settings or therapists using a multiple baseline design. Results indicated that when the multiple schedules were introduced, the functional communication response came under rapid discriminative control, and problem behavior remained at near-zero rates. We extended these findings with another individual by using a more traditional baseline in which problem behavior produced reinforcement. Results replicated those of the previous participants and showed rapid reductions in problem behavior when multiple schedules were implemented across settings. © Society for the Experimental Analysis of Behavior.

  5. Electricity Usage Scheduling in Smart Building Environments Using Smart Devices

    PubMed Central

    Lee, Eunji; Bahn, Hyokyung

    2013-01-01

    With the recent advances in smart grid technologies as well as the increasing dissemination of smart meters, the electricity usage of every moment can be detected in modern smart building environments. Thus, the utility company adopts different price of electricity at each time slot considering the peak time. This paper presents a new electricity usage scheduling algorithm for smart buildings that adopts real-time pricing of electricity. The proposed algorithm detects the change of electricity prices by making use of a smart device and changes the power mode of each electric device dynamically. Specifically, we formulate the electricity usage scheduling problem as a real-time task scheduling problem and show that it is a complex search problem that has an exponential time complexity. An efficient heuristic based on genetic algorithms is performed on a smart device to cut down the huge searching space and find a reasonable schedule within a feasible time budget. Experimental results with various building conditions show that the proposed algorithm reduces the electricity charge of a smart building by 25.6% on average and up to 33.4%. PMID:24453860

  6. Dynamic Appliances Scheduling in Collaborative MicroGrids System

    PubMed Central

    Bilil, Hasnae; Aniba, Ghassane; Gharavi, Hamid

    2017-01-01

    In this paper a new approach which is based on a collaborative system of MicroGrids (MG’s), is proposed to enable household appliance scheduling. To achieve this, appliances are categorized into flexible and non-flexible Deferrable Loads (DL’s), according to their electrical components. We propose a dynamic scheduling algorithm where users can systematically manage the operation of their electric appliances. The main challenge is to develop a flattening function calculus (reshaping) for both flexible and non-flexible DL’s. In addition, implementation of the proposed algorithm would require dynamically analyzing two successive multi-objective optimization (MOO) problems. The first targets the activation schedule of non-flexible DL’s and the second deals with the power profiles of flexible DL’s. The MOO problems are resolved by using a fast and elitist multi-objective genetic algorithm (NSGA-II). Finally, in order to show the efficiency of the proposed approach, a case study of a collaborative system that consists of 40 MG’s registered in the load curve for the flattening program has been developed. The results verify that the load curve can indeed become very flat by applying the proposed scheduling approach. PMID:28824226

  7. Machine Learning and Radiology

    PubMed Central

    Wang, Shijun; Summers, Ronald M.

    2012-01-01

    In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

  8. Joint optimization of green vehicle scheduling and routing problem with time-varying speeds.

    PubMed

    Zhang, Dezhi; Wang, Xin; Li, Shuangyan; Ni, Nan; Zhang, Zhuo

    2018-01-01

    Based on an analysis of the congestion effect and changes in the speed of vehicle flow during morning and evening peaks in a large- or medium-sized city, the piecewise function is used to capture the rules of the time-varying speed of vehicles, which are very important in modelling their fuel consumption and CO2 emission. A joint optimization model of the green vehicle scheduling and routing problem with time-varying speeds is presented in this study. Extra wages during nonworking periods and soft time-window constraints are considered. A heuristic algorithm based on the adaptive large neighborhood search algorithm is also presented. Finally, a numerical simulation example is provided to illustrate the optimization model and its algorithm. Results show that, (1) the shortest route is not necessarily the route that consumes the least energy, (2) the departure time influences the vehicle fuel consumption and CO2 emissions and the optimal departure time saves on fuel consumption and reduces CO2 emissions by up to 5.4%, and (3) extra driver wages have significant effects on routing and departure time slot decisions.

  9. Alternative work schedules for female pharmacists.

    PubMed

    Mason, N A; Perry, W R; Ryan, M L

    1991-01-01

    The impact of the increased proportion of women in pharmacy is discussed, and two leadership positions for which part-time work schedules were implemented are described. Issues associated with the increased representation of women include pharmacist shortages, loss of future leaders, decreased staff productivity related to inadequate day-care services, and a reduced earning potential of pharmacists. Many of these problems can be addressed by altering benefit packages and work schedules to enable employees to raise children while continuing to work. Specific strategies include legislation, day-care programs, flex time and flex scheduling, telecommuting, and the creation of alternative work schedules or permanent part-time positions. At the University of Michigan, a part-time position that combines faculty and clinical responsibilities has been in place since 1988. At The Washington Hospital Center, one of the three assistant director of pharmacy positions is part-time. The women in both positions have met or exceeded job performance requirements while raising a family. Issues raised by the increasing number of female pharmacists must be addressed by the profession. Part-time work schedules are one strategy for enabling female pharmacists to meet both their family and career responsibilities.

  10. A Mechanized Decision Support System for Academic Scheduling.

    DTIC Science & Technology

    1986-03-01

    an operational system called software. The first step in the development phase is Design . Designers destribute software control by factoring the Data...SUBJECT TERMS (Continue on reverse if necessary and identify by block number) ELD GROUP SUB-GROUP Scheduling, Decision Support System , Software Design ...scheduling system . It will also examine software - design techniques to identify the most appropriate method- ology for this problem. " - Chapter 3 will

  11. Uplink Packet-Data Scheduling in DS-CDMA Systems

    NASA Astrophysics Data System (ADS)

    Choi, Young Woo; Kim, Seong-Lyun

    In this letter, we consider the uplink packet scheduling for non-real-time data users in a DS-CDMA system. As an effort to jointly optimize throughput and fairness, we formulate a time-span minimization problem incorporating the time-multiplexing of different simultaneous transmission schemes. Based on simple rules, we propose efficient scheduling algorithms and compare them with the optimal solution obtained by linear programming.

  12. Intelligent Planning and Scheduling for Controlled Life Support Systems

    NASA Technical Reports Server (NTRS)

    Leon, V. Jorge

    1996-01-01

    Planning in Controlled Ecological Life Support Systems (CELSS) requires special look ahead capabilities due to the complex and long-term dynamic behavior of biological systems. This project characterizes the behavior of CELSS, identifies the requirements of intelligent planning systems for CELSS, proposes the decomposition of the planning task into short-term and long-term planning, and studies the crop scheduling problem as an initial approach to long-term planning. CELSS is studied in the realm of Chaos. The amount of biomass in the system is modeled using a bounded quadratic iterator. The results suggests that closed ecological systems can exhibit periodic behavior when imposed external or artificial control. The main characteristics of CELSS from the planning and scheduling perspective are discussed and requirements for planning systems are given. Crop scheduling problem is identified as an important component of the required long-term lookahead capabilities of a CELSS planner. The main characteristics of crop scheduling are described and a model is proposed to represent the problem. A surrogate measure of the probability of survival is developed. The measure reflects the absolute deviation of the vital reservoir levels from their nominal values. The solution space is generated using a probability distribution which captures both knowledge about the system and the current state of affairs at each decision epoch. This probability distribution is used in the context of an evolution paradigm. The concepts developed serve as the basis for the development of a simple crop scheduling tool which is used to demonstrate its usefulness in the design and operation of CELSS.

  13. Frequency of gambling problems among parents of pathological, versus nonpathological, casino gamblers using slot machines.

    PubMed

    Versini, Audrey; LeGauffre, Cindy; Romo, Lucia; Adès, Jean; Gorwood, Philip

    2012-01-01

    Familial and twin studies suggest the implication of a genetic factor in pathological gambling, but mainly assess probands through treatment settings or advertisements. The question raised here is: are parents of casino pathological gamblers using slot machines more affected with pathological gambling than nonpathological gamblers, all interviewed on site at the same casino? Three hundred and fifty-five casino gamblers on slot machines, which included 96 pathological gamblers, 116 problem gamblers, and 143 nonproblem gamblers, were recruited in situ at the largest casino in the Paris suburbs. We evaluated pathological gambling and two addictive disorders (alcohol dependence and tobacco consumption) in the gamblers and their 690 parents (through the proband). Familial aggregation of pathological gambling was confirmed, with a risk of 3.3 for being a pathological gambler when at least one of the parents has problematic gambling. No familial co-aggregation of pathological gambling with alcohol or tobacco dependence was observed. Pathological gambling is found in excess in the parents of pathological casino gamblers, in accordance with previous aggregation studies devoted to other types of gambling, and with studies recruiting gamblers in different settings.  Copyright © American Academy of Addiction Psychiatry.

  14. Distributed Sleep Scheduling in Wireless Sensor Networks via Fractional Domatic Partitioning

    NASA Astrophysics Data System (ADS)

    Schumacher, André; Haanpää, Harri

    We consider setting up sleep scheduling in sensor networks. We formulate the problem as an instance of the fractional domatic partition problem and obtain a distributed approximation algorithm by applying linear programming approximation techniques. Our algorithm is an application of the Garg-Könemann (GK) scheme that requires solving an instance of the minimum weight dominating set (MWDS) problem as a subroutine. Our two main contributions are a distributed implementation of the GK scheme for the sleep-scheduling problem and a novel asynchronous distributed algorithm for approximating MWDS based on a primal-dual analysis of Chvátal's set-cover algorithm. We evaluate our algorithm with ns2 simulations.

  15. Intelligible machine learning with malibu.

    PubMed

    Langlois, Robert E; Lu, Hui

    2008-01-01

    malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

  16. An Extended Deterministic Dendritic Cell Algorithm for Dynamic Job Shop Scheduling

    NASA Astrophysics Data System (ADS)

    Qiu, X. N.; Lau, H. Y. K.

    The problem of job shop scheduling in a dynamic environment where random perturbation exists in the system is studied. In this paper, an extended deterministic Dendritic Cell Algorithm (dDCA) is proposed to solve such a dynamic Job Shop Scheduling Problem (JSSP) where unexpected events occurred randomly. This algorithm is designed based on dDCA and makes improvements by considering all types of signals and the magnitude of the output values. To evaluate this algorithm, ten benchmark problems are chosen and different kinds of disturbances are injected randomly. The results show that the algorithm performs competitively as it is capable of triggering the rescheduling process optimally with much less run time for deciding the rescheduling action. As such, the proposed algorithm is able to minimize the rescheduling times under the defined objective and to keep the scheduling process stable and efficient.

  17. Estimation of distribution algorithm with path relinking for the blocking flow-shop scheduling problem

    NASA Astrophysics Data System (ADS)

    Shao, Zhongshi; Pi, Dechang; Shao, Weishi

    2018-05-01

    This article presents an effective estimation of distribution algorithm, named P-EDA, to solve the blocking flow-shop scheduling problem (BFSP) with the makespan criterion. In the P-EDA, a Nawaz-Enscore-Ham (NEH)-based heuristic and the random method are combined to generate the initial population. Based on several superior individuals provided by a modified linear rank selection, a probabilistic model is constructed to describe the probabilistic distribution of the promising solution space. The path relinking technique is incorporated into EDA to avoid blindness of the search and improve the convergence property. A modified referenced local search is designed to enhance the local exploitation. Moreover, a diversity-maintaining scheme is introduced into EDA to avoid deterioration of the population. Finally, the parameters of the proposed P-EDA are calibrated using a design of experiments approach. Simulation results and comparisons with some well-performing algorithms demonstrate the effectiveness of the P-EDA for solving BFSP.

  18. An extended continuous estimation of distribution algorithm for solving the permutation flow-shop scheduling problem

    NASA Astrophysics Data System (ADS)

    Shao, Zhongshi; Pi, Dechang; Shao, Weishi

    2017-11-01

    This article proposes an extended continuous estimation of distribution algorithm (ECEDA) to solve the permutation flow-shop scheduling problem (PFSP). In ECEDA, to make a continuous estimation of distribution algorithm (EDA) suitable for the PFSP, the largest order value rule is applied to convert continuous vectors to discrete job permutations. A probabilistic model based on a mixed Gaussian and Cauchy distribution is built to maintain the exploration ability of the EDA. Two effective local search methods, i.e. revolver-based variable neighbourhood search and Hénon chaotic-based local search, are designed and incorporated into the EDA to enhance the local exploitation. The parameters of the proposed ECEDA are calibrated by means of a design of experiments approach. Simulation results and comparisons based on some benchmark instances show the efficiency of the proposed algorithm for solving the PFSP.

  19. An Integrated Constraint Programming Approach to Scheduling Sports Leagues with Divisional and Round-robin Tournaments

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Carlsson, Mats; Johansson, Mikael; Larson, Jeffrey

    Previous approaches for scheduling a league with round-robin and divisional tournaments involved decomposing the problem into easier subproblems. This approach, used to schedule the top Swedish handball league Elitserien, reduces the problem complexity but can result in suboptimal schedules. This paper presents an integrated constraint programming model that allows to perform the scheduling in a single step. Particular attention is given to identifying implied and symmetry-breaking constraints that reduce the computational complexity significantly. The experimental evaluation of the integrated approach takes considerably less computational effort than the previous approach.

  20. Minimizing metastatic risk in radiotherapy fractionation schedules

    NASA Astrophysics Data System (ADS)

    Badri, Hamidreza; Ramakrishnan, Jagdish; Leder, Kevin

    2015-11-01

    Metastasis is the process by which cells from a primary tumor disperse and form new tumors at distant anatomical locations. The treatment and prevention of metastatic cancer remains an extremely challenging problem. This work introduces a novel biologically motivated objective function to the radiation optimization community that takes into account metastatic risk instead of the status of the primary tumor. In this work, we consider the problem of developing fractionated irradiation schedules that minimize production of metastatic cancer cells while keeping normal tissue damage below an acceptable level. A dynamic programming framework is utilized to determine the optimal fractionation scheme. We evaluated our approach on a breast cancer case using the heart and the lung as organs-at-risk (OAR). For small tumor α /β values, hypo-fractionated schedules were optimal, which is consistent with standard models. However, for relatively larger α /β values, we found the type of schedule depended on various parameters such as the time when metastatic risk was evaluated, the α /β values of the OARs, and the normal tissue sparing factors. Interestingly, in contrast to standard models, hypo-fractionated and semi-hypo-fractionated schedules (large initial doses with doses tapering off with time) were suggested even with large tumor α/β values. Numerical results indicate the potential for significant reduction in metastatic risk.

  1. Learning Search Control Knowledge for Deep Space Network Scheduling

    NASA Technical Reports Server (NTRS)

    Gratch, Jonathan; Chien, Steve; DeJong, Gerald

    1993-01-01

    While the general class of most scheduling problems is NP-hard in worst-case complexity, in practice, for specific distributions of problems and constraints, domain-specific solutions have been shown to perform in much better than exponential time.

  2. Diverse task scheduling for individualized requirements in cloud manufacturing

    NASA Astrophysics Data System (ADS)

    Zhou, Longfei; Zhang, Lin; Zhao, Chun; Laili, Yuanjun; Xu, Lida

    2018-03-01

    Cloud manufacturing (CMfg) has emerged as a new manufacturing paradigm that provides ubiquitous, on-demand manufacturing services to customers through network and CMfg platforms. In CMfg system, task scheduling as an important means of finding suitable services for specific manufacturing tasks plays a key role in enhancing the system performance. Customers' requirements in CMfg are highly individualized, which leads to diverse manufacturing tasks in terms of execution flows and users' preferences. We focus on diverse manufacturing tasks and aim to address their scheduling issue in CMfg. First of all, a mathematical model of task scheduling is built based on analysis of the scheduling process in CMfg. To solve this scheduling problem, we propose a scheduling method aiming for diverse tasks, which enables each service demander to obtain desired manufacturing services. The candidate service sets are generated according to subtask directed graphs. An improved genetic algorithm is applied to searching for optimal task scheduling solutions. The effectiveness of the scheduling method proposed is verified by a case study with individualized customers' requirements. The results indicate that the proposed task scheduling method is able to achieve better performance than some usual algorithms such as simulated annealing and pattern search.

  3. An assessment of the connection machine

    NASA Technical Reports Server (NTRS)

    Schreiber, Robert

    1990-01-01

    The CM-2 is an example of a connection machine. The strengths and problems of this implementation are considered as well as important issues in the architecture and programming environment of connection machines in general. These are contrasted to the same issues in Multiple Instruction/Multiple Data (MIMD) microprocessors and multicomputers.

  4. An Efficient Downlink Scheduling Strategy Using Normal Graphs for Multiuser MIMO Wireless Systems

    NASA Astrophysics Data System (ADS)

    Chen, Jung-Chieh; Wu, Cheng-Hsuan; Lee, Yao-Nan; Wen, Chao-Kai

    Inspired by the success of the low-density parity-check (LDPC) codes in the field of error-control coding, in this paper we propose transforming the downlink multiuser multiple-input multiple-output scheduling problem into an LDPC-like problem using the normal graph. Based on the normal graph framework, soft information, which indicates the probability that each user will be scheduled to transmit packets at the access point through a specified angle-frequency sub-channel, is exchanged among the local processors to iteratively optimize the multiuser transmission schedule. Computer simulations show that the proposed algorithm can efficiently schedule simultaneous multiuser transmission which then increases the overall channel utilization and reduces the average packet delay.

  5. Integration of Optimal Scheduling with Case-Based Planning.

    DTIC Science & Technology

    1995-08-01

    integrates Case-Based Reasoning (CBR) and Rule-Based Reasoning (RBR) systems. ’ Tachyon : A Constraint-Based Temporal Reasoning Model and Its...Implementation’ provides an overview of the Tachyon temporal’s reasoning system and discusses its possible applications. ’Dual-Use Applications of Tachyon : From...Force Structure Modeling to Manufacturing Scheduling’ discusses the application of Tachyon to real world problems, specifically military force deployment and manufacturing scheduling.

  6. An investigation of chatter and tool wear when machining titanium

    NASA Technical Reports Server (NTRS)

    Sutherland, I. A.

    1974-01-01

    The low thermal conductivity of titanium, together with the low contact area between chip and tool and the unusually high chip velocities, gives rise to high tool tip temperatures and accelerated tool wear. Machining speeds have to be considerably reduced to avoid these high temperatures with a consequential loss of productivity. Restoring this lost productivity involves increasing other machining variables, such as feed and depth-of-cut, and can lead to another machining problem commonly known as chatter. This work is to acquaint users with these problems, to examine the variables that may be encountered when machining a material like titanium, and to advise the machine tool user on how to maximize the output from the machines and tooling available to him. Recommendations are made on ways of improving tolerances, reducing machine tool instability or chatter, and improving productivity. New tool materials, tool coatings, and coolants are reviewed and their relevance examined when machining titanium.

  7. Metalworking and machining fluids

    DOEpatents

    Erdemir, Ali; Sykora, Frank; Dorbeck, Mark

    2010-10-12

    Improved boron-based metal working and machining fluids. Boric acid and boron-based additives that, when mixed with certain carrier fluids, such as water, cellulose and/or cellulose derivatives, polyhydric alcohol, polyalkylene glycol, polyvinyl alcohol, starch, dextrin, in solid and/or solvated forms result in improved metalworking and machining of metallic work pieces. Fluids manufactured with boric acid or boron-based additives effectively reduce friction, prevent galling and severe wear problems on cutting and forming tools.

  8. Machine vision systems using machine learning for industrial product inspection

    NASA Astrophysics Data System (ADS)

    Lu, Yi; Chen, Tie Q.; Chen, Jie; Zhang, Jian; Tisler, Anthony

    2002-02-01

    Machine vision inspection requires efficient processing time and accurate results. In this paper, we present a machine vision inspection architecture, SMV (Smart Machine Vision). SMV decomposes a machine vision inspection problem into two stages, Learning Inspection Features (LIF), and On-Line Inspection (OLI). The LIF is designed to learn visual inspection features from design data and/or from inspection products. During the OLI stage, the inspection system uses the knowledge learnt by the LIF component to inspect the visual features of products. In this paper we will present two machine vision inspection systems developed under the SMV architecture for two different types of products, Printed Circuit Board (PCB) and Vacuum Florescent Displaying (VFD) boards. In the VFD board inspection system, the LIF component learns inspection features from a VFD board and its displaying patterns. In the PCB board inspection system, the LIF learns the inspection features from the CAD file of a PCB board. In both systems, the LIF component also incorporates interactive learning to make the inspection system more powerful and efficient. The VFD system has been deployed successfully in three different manufacturing companies and the PCB inspection system is the process of being deployed in a manufacturing plant.

  9. 30 CFR 18.95 - Approval of machines constructed of components approved, accepted or certified under Bureau of...

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. 18.95 Section 18.95..., accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field approval... 2D, 2E, 2F, or 2G, shall be approved following a determination by the electrical representative that...

  10. 30 CFR 18.95 - Approval of machines constructed of components approved, accepted or certified under Bureau of...

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. 18.95 Section 18.95..., accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field approval... 2D, 2E, 2F, or 2G, shall be approved following a determination by the electrical representative that...

  11. 30 CFR 18.95 - Approval of machines constructed of components approved, accepted or certified under Bureau of...

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. 18.95 Section 18.95..., accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field approval... 2D, 2E, 2F, or 2G, shall be approved following a determination by the electrical representative that...

  12. 30 CFR 18.95 - Approval of machines constructed of components approved, accepted or certified under Bureau of...

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. 18.95 Section 18.95..., accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field approval... 2D, 2E, 2F, or 2G, shall be approved following a determination by the electrical representative that...

  13. 30 CFR 18.95 - Approval of machines constructed of components approved, accepted or certified under Bureau of...

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. 18.95 Section 18.95..., accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field approval... 2D, 2E, 2F, or 2G, shall be approved following a determination by the electrical representative that...

  14. Machine learning and radiology.

    PubMed

    Wang, Shijun; Summers, Ronald M

    2012-07-01

    In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

  15. Solving a supply chain scheduling problem with non-identical job sizes and release times by applying a novel effective heuristic algorithm

    NASA Astrophysics Data System (ADS)

    Pei, Jun; Liu, Xinbao; Pardalos, Panos M.; Fan, Wenjuan; Wang, Ling; Yang, Shanlin

    2016-03-01

    Motivated by applications in manufacturing industry, we consider a supply chain scheduling problem, where each job is characterised by non-identical sizes, different release times and unequal processing times. The objective is to minimise the makespan by making batching and sequencing decisions. The problem is formalised as a mixed integer programming model and proved to be strongly NP-hard. Some structural properties are presented for both the general case and a special case. Based on these properties, a lower bound is derived, and a novel two-phase heuristic (TP-H) is developed to solve the problem, which guarantees to obtain a worst case performance ratio of ?. Computational experiments with a set of different sizes of random instances are conducted to evaluate the proposed approach TP-H, which is superior to another two heuristics proposed in the literature. Furthermore, the experimental results indicate that TP-H can effectively and efficiently solve large-size problems in a reasonable time.

  16. Automatic generation of efficient orderings of events for scheduling applications

    NASA Technical Reports Server (NTRS)

    Morris, Robert A.

    1994-01-01

    In scheduling a set of tasks, it is often not known with certainty how long a given event will take. We call this duration uncertainty. Duration uncertainty is a primary obstacle to the successful completion of a schedule. If a duration of one task is longer than expected, the remaining tasks are delayed. The delay may result in the abandonment of the schedule itself, a phenomenon known as schedule breakage. One response to schedule breakage is on-line, dynamic rescheduling. A more recent alternative is called proactive rescheduling. This method uses statistical data about the durations of events in order to anticipate the locations in the schedule where breakage is likely prior to the execution of the schedule. It generates alternative schedules at such sensitive points, which can be then applied by the scheduler at execution time, without the delay incurred by dynamic rescheduling. This paper proposes a technique for making proactive error management more effective. The technique is based on applying a similarity-based method of clustering to the problem of identifying similar events in a set of events.

  17. Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

    NASA Technical Reports Server (NTRS)

    Drummond, Mark; Fox, Mark; Tate, Austin; Zweben, Monte

    1992-01-01

    The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques.

  18. Mission Operations Planning and Scheduling System (MOPSS)

    NASA Technical Reports Server (NTRS)

    Wood, Terri; Hempel, Paul

    2011-01-01

    MOPSS is a generic framework that can be configured on the fly to support a wide range of planning and scheduling applications. It is currently used to support seven missions at Goddard Space Flight Center (GSFC) in roles that include science planning, mission planning, and real-time control. Prior to MOPSS, each spacecraft project built its own planning and scheduling capability to plan satellite activities and communications and to create the commands to be uplinked to the spacecraft. This approach required creating a data repository for storing planning and scheduling information, building user interfaces to display data, generating needed scheduling algorithms, and implementing customized external interfaces. Complex scheduling problems that involved reacting to multiple variable situations were analyzed manually. Operators then used the results to add commands to the schedule. Each architecture was unique to specific satellite requirements. MOPSS is an expert system that automates mission operations and frees the flight operations team to concentrate on critical activities. It is easily reconfigured by the flight operations team as the mission evolves. The heart of the system is a custom object-oriented data layer mapped onto an Oracle relational database. The combination of these two technologies allows a user or system engineer to capture any type of scheduling or planning data in the system's generic data storage via a GUI.

  19. Abstract quantum computing machines and quantum computational logics

    NASA Astrophysics Data System (ADS)

    Chiara, Maria Luisa Dalla; Giuntini, Roberto; Sergioli, Giuseppe; Leporini, Roberto

    2016-06-01

    Classical and quantum parallelism are deeply different, although it is sometimes claimed that quantum Turing machines are nothing but special examples of classical probabilistic machines. We introduce the concepts of deterministic state machine, classical probabilistic state machine and quantum state machine. On this basis, we discuss the question: To what extent can quantum state machines be simulated by classical probabilistic state machines? Each state machine is devoted to a single task determined by its program. Real computers, however, behave differently, being able to solve different kinds of problems. This capacity can be modeled, in the quantum case, by the mathematical notion of abstract quantum computing machine, whose different programs determine different quantum state machines. The computations of abstract quantum computing machines can be linguistically described by the formulas of a particular form of quantum logic, termed quantum computational logic.

  20. A meta-regression analysis of 41 Australian problem gambling prevalence estimates and their relationship to total spending on electronic gaming machines.

    PubMed

    Markham, Francis; Young, Martin; Doran, Bruce; Sugden, Mark

    2017-05-23

    Many jurisdictions regularly conduct surveys to estimate the prevalence of problem gambling in their adult populations. However, the comparison of such estimates is problematic due to methodological variations between studies. Total consumption theory suggests that an association between mean electronic gaming machine (EGM) and casino gambling losses and problem gambling prevalence estimates may exist. If this is the case, then changes in EGM losses may be used as a proxy indicator for changes in problem gambling prevalence. To test for this association this study examines the relationship between aggregated losses on electronic gaming machines (EGMs) and problem gambling prevalence estimates for Australian states and territories between 1994 and 2016. A Bayesian meta-regression analysis of 41 cross-sectional problem gambling prevalence estimates was undertaken using EGM gambling losses, year of survey and methodological variations as predictor variables. General population studies of adults in Australian states and territory published before 1 July 2016 were considered in scope. 41 studies were identified, with a total of 267,367 participants. Problem gambling prevalence, moderate-risk problem gambling prevalence, problem gambling screen, administration mode and frequency threshold were extracted from surveys. Administrative data on EGM and casino gambling loss data were extracted from government reports and expressed as the proportion of household disposable income lost. Money lost on EGMs is correlated with problem gambling prevalence. An increase of 1% of household disposable income lost on EGMs and in casinos was associated with problem gambling prevalence estimates that were 1.33 times higher [95% credible interval 1.04, 1.71]. There was no clear association between EGM losses and moderate-risk problem gambling prevalence estimates. Moderate-risk problem gambling prevalence estimates were not explained by the models (I 2  ≥ 0.97; R 2  ≤ 0.01). The