Multiprocessor scheduling problem with machine constraints
NASA Astrophysics Data System (ADS)
He, Yong; Tan, Zhiyi
2001-09-01
This paper investigates multiprocessor scheduling with machine constraints, which has many applications in the flexible manufacturing systems and in VLSI chip design. Machines have different starting times and each machine can schedule at most k jobs in a period. The objective is to minimizing the makespan. For this strogly NP-hard problem, it is important to design near-optimal approximation algorithms. It is known that Modified LPT algorithm has a worst-case ratio of 3/2-1/(2m) for kequals2 where m is the number of machines. For k>2, no good algorithm has been got in the literature. In this paper, we prove the worst-case ratio of Modified LPT is less than 2. We further present an approximation algorithm Matching and show it has a worst-case ratio 2-1/m for every k>2. By introducing parameters, we get two better worst-case ratios which show the Matching algorithm is near optimal for two special cases.
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.
JIT single machine scheduling problem with periodic preventive maintenance
NASA Astrophysics Data System (ADS)
Shahriari, Mohammadreza; Shoja, Naghi; Zade, Amir Ebrahimi; Barak, Sasan; Sharifi, Mani
2016-03-01
This article investigates a JIT single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, multi-objective particle swarm optimization (MOPSO) algorithm is implemented. Also, as well as MOPSO, two other optimization algorithms are used for comparing the results. Eventually, Taguchi method with metrics analysis is presented to tune the algorithms' parameters and a multiple criterion decision making technique based on the technique for order of preference by similarity to ideal solution is applied to choose the best algorithm. Comparison results confirmed the supremacy of MOPSO to the other algorithms.
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.
Hybrid metaheuristics for solving a fuzzy single batch-processing machine scheduling problem.
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.
Gang scheduling a parallel machine
Gorda, B.C.; Brooks, E.D. III.
1991-03-01
Program development on parallel machines can be a nightmare of scheduling headaches. We have developed a portable time sharing mechanism to handle the problem of scheduling gangs of processors. User program and their gangs of processors are put to sleep and awakened by the gang scheduler to provide a time sharing environment. Time quantums are adjusted according to priority queues and a system of fair share accounting. The initial platform for this software is the 128 processor BBN TC2000 in use in the Massively Parallel Computing Initiative at the Lawrence Livermore National Laboratory. 2 refs., 1 fig.
Xu, Zhenzhen; Zou, Yongxing; Kong, Xiangjie
2015-01-01
To our knowledge, this paper investigates the first application of meta-heuristic algorithms to tackle the parallel machines scheduling problem with weighted late work criterion and common due date ([Formula: see text]). Late work criterion is one of the performance measures of scheduling problems which considers the length of late parts of particular jobs when evaluating the quality of scheduling. Since this problem is known to be NP-hard, three meta-heuristic algorithms, namely ant colony system, genetic algorithm, and simulated annealing are designed and implemented, respectively. We also propose a novel algorithm named LDF (largest density first) which is improved from LPT (longest processing time first). The computational experiments compared these meta-heuristic algorithms with LDF, LPT and LS (list scheduling), and the experimental results show that SA performs the best in most cases. However, LDF is better than SA in some conditions, moreover, the running time of LDF is much shorter than SA. PMID:26702371
NASA Astrophysics Data System (ADS)
Xu, Ye; Wang, Ling; Wang, Shengyao; Liu, Min
2013-12-01
In this article, an effective shuffled frog-leaping algorithm (SFLA) is proposed to solve the hybrid flow-shop scheduling problem with identical parallel machines (HFSP-IPM). First, some novel heuristic decoding rules for both job order decision and machine assignment are proposed. Then, three hybrid decoding schemes are designed to decode job order sequences to schedules. A special bi-level crossover and multiple local search operators are incorporated in the searching framework of the SFLA to enrich the memetic searching behaviour and to balance the exploration and exploitation capabilities. Meanwhile, some theoretical analysis for the local search operators is provided for guiding the local search. The parameter setting of the algorithm is also investigated based on the Taguchi method of design of experiments. Finally, numerical testing based on well-known benchmarks and comparisons with some existing algorithms are carried out to demonstrate the effectiveness of the proposed algorithm.
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.
NASA Astrophysics Data System (ADS)
Hasani, Keramat; Kravchenko, Svetlana A.; Werner, Frank
2016-01-01
This article considers the problem of scheduling a given set of n jobs on two identical parallel machines with a single server. Each job must be processed on one of the machines. Before processing, the server has to set up the relevant machine. The objective is to minimize the makespan. For this unary NP-hard problem, two fast constructive algorithms with a complexity of O(n2) are presented. The performance of these algorithms is evaluated for instances with up to 10,000 jobs. Computational results indicate that the algorithms have an excellent performance for very large instances so that the obtained objective function values are very close to a lower bound, and in many cases even an optimal solution is achieved. Superiority over all existing algorithms is obtained by sequencing the jobs on the two machines so that the machine idle time and the server waiting time are minimized. In doing so, the characteristics of an optimal solution resulting from its relevant lower bound are taken into account.
Gang scheduling a parallel machine. Revision 1
Gorda, B.C.; Brooks, E.D. III
1991-12-01
Program development on parallel machines can be a nightmare of scheduling headaches. We have developed a portable time sharing mechanism to handle the problem of scheduling gangs of processes. User programs and their gangs of processes are put to sleep and awakened by the gang scheduler to provide a time sharing environment. Time quantum are adjusted according to priority queues and a system of fair share accounting. The initial platform for this software is the 128 processor BBN TC2000 in use in the Massively Parallel Computing Initiative at the Lawrence Livermore National Laboratory.
NASA Astrophysics Data System (ADS)
Nabeshima, I.
1986-09-01
The purpose of this paper is to present sufficiently optimal job-base conditions for a specified sequence in the NP-complete three-machine permutation flow-shop makespan scheduling problem with separated sequence-independent setup times, contrary to the machine-base conditions proposed in the previous papers. Those conditions give us a chance to identify an optimal sequence in O (n sub 2) operations especially in the case where no existing machine-base condition can identify an optimal sequence. As a special case, we have similar known results associated with the usual flow-shop case with setup times included.
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. PMID:27652166
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.
A Study on Machine Maintenance Scheduling Using Distributed Cooperative Approach
NASA Astrophysics Data System (ADS)
Tsujibe, Akihisa; Kaihara, Toshiya; Fujii, Nobutada; Nonaka, Youichi
In this study, we propose a distributed cooperative scheduling method, and apply the method into a machine maintenance scheduling problem in re-entrant production systems. As one of the distributed cooperative scheduling methods, we focus on Lagrangian decomposition and coordination (LDC) method, and formulate the machine maintenance scheduling problem with LDC so as to improve computational efficiency by decomposing an original scheduling problem into several sub-problems. The derived solutions by solving the decomposed dual problem are converted into feasible solutions with a heuristic procedure applied in this study. The proposed approach regards maintenance as job with starting and finishing time constraints, so that product and maintenance schedule can realize proper maintenance operations without losing productivity. We show the effectiveness of the proposed method in several simulation experiments.
Integrated network design and scheduling problems :
Nurre, Sarah G.; Carlson, Jeffrey J.
2014-01-01
We consider the class of integrated network design and scheduling problems. These problems focus on selecting and scheduling operations that will change the characteristics of a network, while being speci cally concerned with the performance of the network over time. Motivating applications of INDS problems include infrastructure restoration after extreme events and building humanitarian distribution supply chains. While similar models have been proposed, no one has performed an extensive review of INDS problems from their complexity, network and scheduling characteristics, information, and solution methods. We examine INDS problems under a parallel identical machine scheduling environment where the performance of the network is evaluated by solving classic network optimization problems. We classify that all considered INDS problems as NP-Hard and propose a novel heuristic dispatching rule algorithm that selects and schedules sets of arcs based on their interactions in the network. We present computational analysis based on realistic data sets representing the infrastructures of coastal New Hanover County, North Carolina, lower Manhattan, New York, and a realistic arti cial community CLARC County. These tests demonstrate the importance of a dispatching rule to arrive at near-optimal solutions during real-time decision making activities. We extend INDS problems to incorporate release dates which represent the earliest an operation can be performed and exible release dates through the introduction of specialized machine(s) that can perform work to move the release date earlier in time. An online optimization setting is explored where the release date of a component is not known.
A simple neural network scheduler for real-time machine task scheduling
Gritzo, R.E.
1991-01-01
The recent development of a new generation of automated radionuclide assay equipment in our facility requires embedded software at each machine for the scheduling of tasks. The execution time requirements of real-time embedded software limit the complexity of the scheduler design. By representing the scheduling problem properly, a simple backpropagation neural network performs the scheduling function within the imposed requirements. Operational tests have demonstrated that the neural network scheduler has met all development goals and is superior to the previous approaches. 3 refs., 1 tab.
Flexible Job-Shop Scheduling Problem by Genetic Algorithm
NASA Astrophysics Data System (ADS)
Ida, Kenichi; Oka, Kensaku
Flexible Job-shop Scheduling Problem is expansion of the traditional Job-shop Scheduling Problem that an operation can be processed one or more machines. The purpose of this problem is to look for the smallest makespan. For that purpose, it is necessary to decide optimal assignment of machines to operations and order of operations on machines. In this paper, we focus on maximum of workloads for all machines and propose new suvival selection, creation method of initial solution, mutation, and escape method to Genetic Algorithm for this problem. The efficacy of our method is demonstrated by comparing its numerical experiment results with another methods.
The Microchp Scheduling Problem
NASA Astrophysics Data System (ADS)
Bosman, M. G. C.; Bakker, V.; Molderink, A.; Hurink, J. L.; Smit, G. J. M.
2009-08-01
The increasing penetration of renewable energy sources, the demand for more energy efficient electricity production and the increase in distributed electricity generation causes a shift in the way electricity is produced and consumed. The downside of these changes in the electricity grid is that network stability and controllability becomes more difficult compared to the old situation. The new network has to accommodate various means of production, consumption and buffering and needs to offer control over the energy flows between these three elements. In order to offer such a control mechanism we need to know more about the individual aspects. In this paper we focus on the modelling of distributed production. Especially we look at the use of microCHP (Combined Heat and Power) appliances in a group of houses. The problem of planning the production runs of the microCHP is modelled via an ILP formulation both for a single house and for a group of houses.
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.
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.
EOTAS dynamic scheduling method based on wearable man-machine synergy
NASA Astrophysics Data System (ADS)
Liu, Zhijun; Wang, Dongmei; Yang, Yukun; Zhao, Jie
2011-12-01
By analyzing the dynamic scheduling needs of its inherent nature, made wearable computing based on human-computer natural interaction forms the basis of EOTAS dynamic scheduling methods, and the targeted building, a new concept of wearable man-machine cooperative forms, turn around its concrete implementation and application, a color based on extended fuzzy Petri net EOTAS dynamic scheduling method for the preliminary settlement of the business operating environment EOTAS field applications of the fast scheduling problem.
EOTAS dynamic scheduling method based on wearable man-machine synergy
NASA Astrophysics Data System (ADS)
Liu, ZhiJun; Wang, DongMei; Yang, YuKun; Zhao, Jie
2012-01-01
By analyzing the dynamic scheduling needs of its inherent nature, made wearable computing based on human-computer natural interaction forms the basis of EOTAS dynamic scheduling methods, and the targeted building, a new concept of wearable man-machine cooperative forms, turn around its concrete implementation and application, a color based on extended fuzzy Petri net EOTAS dynamic scheduling method for the preliminary settlement of the business operating environment EOTAS field applications of the fast scheduling problem.
Scheduling Jobs with Variable Job Processing Times on Unrelated Parallel Machines
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
NASA Astrophysics Data System (ADS)
Afzalirad, Mojtaba; Rezaeian, Javad
2016-04-01
This study involves an unrelated parallel machine scheduling problem in which sequence-dependent set-up times, different release dates, machine eligibility and precedence constraints are considered to minimize total late works. A new mixed-integer programming model is presented and two efficient hybrid meta-heuristics, genetic algorithm and ant colony optimization, combined with the acceptance strategy of the simulated annealing algorithm (Metropolis acceptance rule), are proposed to solve this problem. Manifestly, the precedence constraints greatly increase the complexity of the scheduling problem to generate feasible solutions, especially in a parallel machine environment. In this research, a new corrective algorithm is proposed to obtain the feasibility in all stages of the algorithms. The performance of the proposed algorithms is evaluated in numerical examples. The results indicate that the suggested hybrid ant colony optimization statistically outperformed the proposed hybrid genetic algorithm in solving large-size test problems.
Group scheduling problems in directional sensor networks
NASA Astrophysics Data System (ADS)
Singh, Alok; Rossi, André
2015-12-01
This article addresses the problem of scheduling a set of groups of directional sensors arising as a result of applying an exact or a heuristic approach for solving a problem involving directional sensors. The problem seeks a schedule for these groups that minimizes the total energy consumed in switching from one group to the next group in the schedule. In practice, when switching from a group to the next one, active sensors in the new group have to rotate in order to face their working direction. These rotations consume energy, and the problem is to schedule the groups so as to minimize the total amount of energy consumed by all the sensor rotations, knowing the initial angular positions of all the sensors. In this article, it is assumed that energy consumption is proportional to the angular movement for all the sensors. Another problem version is also investigated that seeks to minimize the total time during which the sensor network cannot cover all the targets because active sensors are rotating. Both problems are proved to be ?-hard, and a lower bound for the first problem is presented. A greedy heuristic and a genetic algorithm are also proposed for addressing the problem of minimizing total rotation in the general case. Finally, a local search is also proposed to improve the solutions obtained through a genetic algorithm.
Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds
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
Prediction based proactive thermal virtual machine scheduling in green clouds.
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.
Prediction based proactive thermal virtual machine scheduling in green clouds.
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
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.
A canned food scheduling problem with batch due date
NASA Astrophysics Data System (ADS)
Chung, Tsui-Ping; Liao, Ching-Jong; Smith, Milton
2014-09-01
This article considers a canned food scheduling problem where jobs are grouped into several batches. Jobs can be sent to the next operation only when all the jobs in the same batch have finished their processing, i.e. jobs in a batch, have a common due date. This batch due date problem is quite common in canned food factories, but there is no efficient heuristic to solve the problem. The problem can be formulated as an identical parallel machine problem with batch due date to minimize the total tardiness. Since the problem is NP hard, two heuristics are proposed to find the near-optimal solution. Computational results comparing the effectiveness and efficiency of the two proposed heuristics with an existing heuristic are reported and discussed.
Optimum Production Control and Workforce Scheduling of Machining Project
NASA Astrophysics Data System (ADS)
Lan, Tian-Syung; Lo, Chih-Yao; Hou, Cheng-I.
Through the proposed model in this study, the production control with the consideration of workforce scheduling for advanced manufacturing systems becomes realistically and concretely solvable. This study not only meditates the concept of balancing machine productivity and human ability into the objective, but also implements Calculus of Variations to optimize the profit for a deterministic production quantity. In addition, the optimum solutions of dynamic productivity control and workforce scheduling are comprehensively provided. Moreover, the decision criteria for selecting the optimum solution and the sensitivity analysis of the critical variables are fully discussed. This study definitely contributes the applicable strategy to control the productivity and workforce in manufacturing and provides the valuable tool to conclusively optimize the profit of a machining project for operations research in today`s manufacturing industry with profound insight.
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.
Efficient Genetic Algorithm for Flexible Job-Shop Scheduling Problem Using Minimise Makespan
NASA Astrophysics Data System (ADS)
Farashahi, Hamid Ghaani; Baharudin, B. T. H. T.; Shojaeipour, Shahed; Jaberi, Mohammad
The aim of this paper is to minimise the makespan. The flexible job-shop scheduling is very common in practice and parallel machine is used in job-shop environment as flexibility. These flexibilities could be used for increasing the throughput rate, avoiding the production stop, removing bottleneck problems and finally achieving competitive advantages in economical environments. In opposition to classic job-shop where there is one machine in each stage, in this problem production system consists of multistage which in each stage there are one or several parallel machines with different speeds. The flexible job-shop scheduling with parallel machines problem consists of two sub-problems for solving: assigning and sequencing sub-problem.
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.
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.
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
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
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.
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.
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. PMID:24977204
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
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.
Machine Learning Based Online Performance Prediction for Runtime Parallelization and Task Scheduling
Li, J; Ma, X; Singh, K; Schulz, M; de Supinski, B R; McKee, S A
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 generate 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.
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.
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.
Analysis of the integration of the physician rostering problem and the surgery scheduling problem.
Van Huele, Christophe; Vanhoucke, Mario
2014-06-01
In this paper, we present the Integrated Physician and Surgery Scheduling Problem (IPSSP) as a new approach for solving operating room scheduling problems where staff rosters for the physicians are integrated in the optimization. A mixed integer linear programming formulation is created based on the most frequently observed objective and restrictions of the surgery scheduling and the physician rostering problem in the literature. We analyze schedules by relaxing both surgery and physician related constraints. We then measure the implications of setting these physician preferences on the surgery schedule. Our experiments show two main interesting insights for physician roster schedulers as well as operating theatre scheduling managers.
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...
Code of Federal Regulations, 2013 CFR
2013-07-01
... 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...
Code of Federal Regulations, 2014 CFR
2014-07-01
... 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...
Code of Federal Regulations, 2010 CFR
2010-07-01
... 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...
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).
Handling Deafness Problem of Scheduled Multi-Channel Polling MACs
NASA Astrophysics Data System (ADS)
Jiang, Fulong; Liu, Hao; Shi, Longxing
Combining scheduled channel polling with channel diversity is a promising way for a MAC protocol to achieve high energy efficiency and performance under both light and heavy traffic conditions. However, the deafness problem may cancel out the benefit of channel diversity. In this paper, we first investigate the deafness problem of scheduled multi-channel polling MACs with experiments. Then we propose and evaluate two schemes to handle the deafness problem. Our experiment shows that deafness is a significant reason for performance degradation in scheduled multi-channel polling MACs. A proper scheme should be chosen depending on the traffic pattern and the design objective.
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
Analysis of Feeder Bus Network Design and Scheduling Problems
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
Code of Federal Regulations, 2010 CFR
2010-07-01
... machine cards available from Federal Supply Schedule contracts. 101-26.509-1 Section 101-26.509-1 Public....509-1 Requisitioning tabulating machine cards available from Federal Supply Schedule contracts... electrical and mechanical contact tabulating machines, including aperture cards and copy cards....
Code of Federal Regulations, 2012 CFR
2012-07-01
... tabulating machine cards not available from Federal Supply Schedule contracts. 101-26.509-2 Section 101-26... Procurement 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...
Code of Federal Regulations, 2012 CFR
2012-07-01
... tabulating machine cards available from Federal Supply Schedule contracts. 101-26.509-1 Section 101-26.509-1... Programs § 101-26.509-1 Requisitioning tabulating machine cards available from Federal Supply Schedule... applicable to electrical and mechanical contact tabulating machines, including aperture cards and copy...
Code of Federal Regulations, 2011 CFR
2011-07-01
... machine cards available from Federal Supply Schedule contracts. 101-26.509-1 Section 101-26.509-1 Public....509-1 Requisitioning tabulating machine cards available from Federal Supply Schedule contracts... electrical and mechanical contact tabulating machines, including aperture cards and copy cards....
Code of Federal Regulations, 2014 CFR
2014-07-01
... machine cards available from Federal Supply Schedule contracts. 101-26.509-1 Section 101-26.509-1 Public....509-1 Requisitioning tabulating machine cards available from Federal Supply Schedule contracts... electrical and mechanical contact tabulating machines, including aperture cards and copy cards....
Code of Federal Regulations, 2013 CFR
2013-07-01
... machine cards available from Federal Supply Schedule contracts. 101-26.509-1 Section 101-26.509-1 Public....509-1 Requisitioning tabulating machine cards available from Federal Supply Schedule contracts... electrical and mechanical contact tabulating machines, including aperture cards and copy cards....
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.
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.
NASA Astrophysics Data System (ADS)
Guo, Peng; Cheng, Wenming; Wang, Yi
2015-11-01
This article considers the parallel machine scheduling problem with step-deteriorating jobs and sequence-dependent setup times. The objective is to minimize the total tardiness by determining the allocation and sequence of jobs on identical parallel machines. In this problem, the processing time of each job is a step function dependent upon its starting time. An individual extended time is penalized when the starting time of a job is later than a specific deterioration date. The possibility of deterioration of a job makes the parallel machine scheduling problem more challenging than ordinary ones. A mixed integer programming model for the optimal solution is derived. Due to its NP-hard nature, a hybrid discrete cuckoo search algorithm is proposed to solve this problem. In order to generate a good initial swarm, a modified Biskup-Hermann-Gupta (BHG) heuristic called MBHG is incorporated into the population initialization. Several discrete operators are proposed in the random walk of Lévy flights and the crossover search. Moreover, a local search procedure based on variable neighbourhood descent is integrated into the algorithm as a hybrid strategy in order to improve the quality of elite solutions. Computational experiments are executed on two sets of randomly generated test instances. The results show that the proposed hybrid algorithm can yield better solutions in comparison with the commercial solver CPLEX® with a one hour time limit, the discrete cuckoo search algorithm and the existing variable neighbourhood search algorithm.
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.
NASA Astrophysics Data System (ADS)
Sodan, Angela C.
Virtual machines have become an important approach to provide performance isolation and performance guarantees (QoS) on cluster servers and on many-core SMP servers. Many-core CPUs are a current trend in CPU design and require jobs to be parallel for exploitation of the performance potential. Very promising for batch job scheduling with virtual machines on both cluster servers and many-core SMP servers is adaptive scheduling which can adjust sizes of parallel jobs to consider different load situations and different resource availability. Then, the resource allocation and resource partitioning can be determined at virtual-machine level and be propagated down to the job sizes. The paper investigates job re-sizing and virtual-machine resizing, and the effects which the efficiency curve of the jobs has on the resulting performance. Additionally, the paper presents a simple, yet effective queuing-model approach for predicting performance under different resource allocation.
Some problems in sequencing and scheduling utilizing branch and bound algorithms
Gim, B.
1988-01-01
This dissertation deals with branch and bound algorithms which are applied to the two-machine flow-shop problem with sparse precedence constraints and the optimal sequencing and scheduling of multiple feedstocks in a batch-type digester problem. The problem studied here is to find a schedule which minimizes the maximum flow time with the requirement that the schedule does not violate a set of sparse precedence constraints. This research provides a branch and bound algorithm which employs a lower bounding rule and is based on an adjustment of the sequence obtained by applying Johnson's algorithm. It is demonstrated that this lower bounding procedure in conjunction with Kurisu's branching rule is effective for the sparse precedence constraints problem case. Biomass to methane production systems have the potential of supplying 25% of the national gas demand. The optimal operation of a batch digester system requires the sequencing and scheduling of all batches from multiple feedstocks during a fixed time horizon. A significant characteristic of these systems is that the feedstock decays in storage before use in the digester system. The operational problem is to determine the time to allocate to each batch of several feedstocks and then sequence the individual batches so as to maximize biogas production for a single batch type digester over a fixed planning horizon. This research provides a branch and bound algorithm for sequencing and a two-step hierarchical dynamic programming procedure for time allocation scheduling. An efficient heuristic algorithm is developed for large problems and demonstrated to yield excellent results.
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.
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.
Semi-online scheduling on two machines with GoS levels and partial information of processing time.
Luo, Taibo; Xu, Yinfeng
2014-01-01
This paper investigates semi-online scheduling problems on two parallel machines under a grade of service (GoS) provision subject to minimize the makespan. We consider three different semi-online versions with knowing the total processing time of the jobs with higher GoS level, knowing the total processing time of the jobs with lower GoS level, or knowing both in advance. Respectively, for the three semi-online versions, we develop algorithms with competitive ratios of 3/2, 20/13, and 4/3 which are shown to be optimal.
Code of Federal Regulations, 2011 CFR
2011-10-01
... 49 Transportation 4 2011-10-01 2011-10-01 false On-track roadway maintenance machines; inspection... RAILROAD WORKPLACE SAFETY On-Track Roadway Maintenance Machines and Hi-Rail Vehicles § 214.527 On-track roadway maintenance machines; inspection for compliance and schedule for repairs. (a) The operator of...
Code of Federal Regulations, 2014 CFR
2014-10-01
... 49 Transportation 4 2014-10-01 2014-10-01 false On-track roadway maintenance machines; inspection... RAILROAD WORKPLACE SAFETY On-Track Roadway Maintenance Machines and Hi-Rail Vehicles § 214.527 On-track roadway maintenance machines; inspection for compliance and schedule for repairs. (a) The operator of...
Code of Federal Regulations, 2012 CFR
2012-10-01
... 49 Transportation 4 2012-10-01 2012-10-01 false On-track roadway maintenance machines; inspection... RAILROAD WORKPLACE SAFETY On-Track Roadway Maintenance Machines and Hi-Rail Vehicles § 214.527 On-track roadway maintenance machines; inspection for compliance and schedule for repairs. (a) The operator of...
Code of Federal Regulations, 2013 CFR
2013-10-01
... 49 Transportation 4 2013-10-01 2013-10-01 false On-track roadway maintenance machines; inspection... RAILROAD WORKPLACE SAFETY On-Track Roadway Maintenance Machines and Hi-Rail Vehicles § 214.527 On-track roadway maintenance machines; inspection for compliance and schedule for repairs. (a) The operator of...
Code of Federal Regulations, 2010 CFR
2010-10-01
... 49 Transportation 4 2010-10-01 2010-10-01 false On-track roadway maintenance machines; inspection... RAILROAD WORKPLACE SAFETY On-Track Roadway Maintenance Machines and Hi-Rail Vehicles § 214.527 On-track roadway maintenance machines; inspection for compliance and schedule for repairs. (a) The operator of...
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.
Efficiently Scheduling Multi-core Guest Virtual Machines on Multi-core Hosts in Network Simulation
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 our 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.
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.
Problem of technological inheritance in machine engineering
NASA Astrophysics Data System (ADS)
Blumenstein, Valery; Rakhimyanov, Kharis; Heifetz, Mikhail; Kleptzov, Alexander
2016-01-01
This article demonstrates the importance of the research study with regard to the technological inheritance of the properties, which characterize the surface layer, at different stages of a part's life cycle. It looks back at the major achievements and gives the findings relating to the technological inheritance of the parameters of the surface layer strength and quality as well as to how they affect the performance properties of machine parts. It demonstrates that high rates of machine engineering development, occurrence of new materials and more complicated machine operation environment require a shorter period for design-to-manufacture facility by reducing experiments and increasing design work. That, in its turn, generates the necessity in more complex but also more accurate models of metal behavior under stressing. It is especially critical for strengthening treatment. Among them are the models developed within the mechanics of technological inheritance. It is assumed that at the stages of a part's life cycle deformation accumulates on a continuous basis and the plasticity reserve of the metal, which the surface layer is made of, depletes. The research study of technological inheritance and the discovery of physical patterns of the evolution and degradation of the structures in a thin surface layer, which occur during machining and operational stressing of parts made from existing and unique including nanopatterned metals, is a crucial scientific challenge. This leads to the acquisition of new knowledge in the plasticity of state-of-the-art metals in the conditions of complex non monotonous stressing and to the development of efficient integrated and combined methods of technological impact.
Petri Net Modeling and Decomposition Method for Solving Production Scheduling Problems
NASA Astrophysics Data System (ADS)
Nishi, Tatsushi; Maeno, Ryota
Considering the need to develop general scheduling problem solver, the recent integration of Petri Nets as modeling tools into effective optimization methods for scheduling problems is very promising. The paper addresses a Petri Net modeling and decomposition method for solving a wide variety of scheduling problems. The scheduling problems are represented as the optimal transition firing sequence problems for timed Petri Nets. The Petri Net is decomposed into several subnets in which each subproblem can be easily solved by Dijkstra' algorithm. The approach is applied to a flowshop scheduling problem. The performance of the proposed algorithm is compared with that of a simulated annealing method.
Optimized Hypervisor Scheduler for Parallel Discrete Event Simulations on Virtual Machine Platforms
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 from 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.
An Improved Differential Evolution Solution for Software Project Scheduling Problem
Biju, A. C.; Victoire, T. Aruldoss Albert; Mohanasundaram, Kumaresan
2015-01-01
This paper proposes a differential evolution (DE) method for the software project scheduling problem (SPSP). The interest on finding a more efficient solution technique for SPSP is always a topic of interest due to the fact of ever growing challenges faced by the software industry. The curse of dimensionality is introduced in the scheduling problem by ever increasing software assignments and the number of staff who handles it. Thus the SPSP is a class of NP-hard problem, which requires a rigorous solution procedure which guarantees a reasonably better solution. Differential evolution is a direct search stochastic optimization technique that is fairly fast and reasonably robust. It is also capable of handling nondifferentiable, nonlinear, and multimodal objective functions like SPSP. This paper proposes a refined DE where a new mutation mechanism is introduced. The superiority of the proposed method is experimented and demonstrated by solving the SPSP on 50 random instances and the results are compared with some of the techniques in the literature. PMID:26495419
Applications of dynamic scheduling technique to space related problems: Some case studies
NASA Technical Reports Server (NTRS)
Nakasuka, Shinichi; Ninomiya, Tetsujiro
1994-01-01
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.
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.
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.
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.
Nurse Scheduling System based on Dynamic Weighted Maximal Constraint Satisfaction Problem
NASA Astrophysics Data System (ADS)
Hattori, Hiromitsu; Isomura, Atsushi; Ito, Takayuki; Ozono, Tadachika; Shintani, Toramatsu
Scheduling has been an important research field in Artificial Intelligence. Because typical scheduling problems could be modeled as a Constraint Satisfaction Problem(CSP), several constraint satisfaction techniques have been proposed. In order to handle the different levels of importance of the constraints, solving a problem as a Weighted Maximal Constraint Satisfaction Problem(W-MaxCSP) is an promising approach. However, there exists the case where unexpected events are added and some sudden changes are required, i.e., the case with dynamic changes in scheduling problems. In this paper, we describe such dynamic scheduling problem as a Dynamic Weighted Maximal Constraint Satisfaction Problem(DW-MaxCSP) in which constraints would changes dynamically. Generally, it is undesirable to determine vastly modified schedule even if re-scheduling is needed. A new schedule should be close to the current one as much as possible. In order to obtain stable solutions, we propose the methodology to maintain portions of the current schedule using the provisional soft constraints, which explicitly penalize the changes from the current schedule. We have experimentally confirmed the efficacy of re-scheduling based on our method with provisional constraints. In this paper, we construct the nurse scheduling system for applying the proposed scheduling method.
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.
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. PMID:27652008
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.
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.
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.
Exploiting the ALICE HLT for PROOF by scheduling of Virtual Machines
NASA Astrophysics Data System (ADS)
Meoni, Marco; Boettger, Stefan; Zelnicek, Pierre; Lindenstruth, Volker; Kebschull, Udo
2011-12-01
The HLT (High-Level Trigger) group of the ALICE experiment at the LHC has prepared a virtual Parallel ROOT Facility (PROOF) enabled cluster (HAF - HLT Analysis Facility) for fast physics analysis, detector calibration and reconstruction of data samples. The HLT-Cluster currently consists of 2860 CPU cores and 175TB of storage. Its purpose is the online filtering of the relevant part of data produced by the particle detector. However, data taking is not running continuously and exploiting unused cluster resources for other applications is highly desirable and improves the usage-cost ratio of the HLT cluster. As such, unused computing resources are dedicated to a PROOF-enabled virtual cluster available to the entire collaboration. This setup is especially aimed at the prototyping phase of analyses that need a high number of development iterations and a short response time, e.g. tuning of analysis cuts, calibration and alignment. HAF machines are enabled and disabled upon user request to start or complete analysis tasks. This is achieved by a virtual machine scheduling framework which dynamically assigns and migrates virtual machines running PROOF workers to unused physical resources. Using this approach we extend the HLT usage scheme to running both online and offline computing, thereby optimizing the resource usage.
NASA Astrophysics Data System (ADS)
Li, Wenjie; Li, Shisheng
2015-03-01
We study the online batch scheduling of equal-length jobs on two identical batch machines. Each batch machine can process up to b jobs simultaneously as a batch (where b is called the capacity of the machines). The goal is to determine a schedule that maximises the (weighted) number of early jobs. For the non-preemptive model, we first present an upper bound that depends on the machine capacity b, and then we provide a greedy online algorithm with a competitive ratio of 1/(b + 1). For the preemption-restart model with b = ∞, we first show that no online algorithm has a competitive ratio greater than 0.595, and then we design an online algorithm with a competitive ratio of ?.
Discrete Bat Algorithm for Optimal Problem of Permutation Flow Shop Scheduling
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
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.
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.
The problem of scheduling for the linear section of a single-track railway
NASA Astrophysics Data System (ADS)
Akimova, Elena N.; Gainanov, Damir N.; Golubev, Oleg A.; Kolmogortsev, Ilya D.; Konygin, Anton V.
2016-06-01
The paper is devoted to the problem of scheduling for the linear section of a single-track railway: how to organize the flow in both directions in the most efficient way. In this paper, the authors propose an algorithm for scheduling, examine the properties of this algorithm and perform the computational experiments.
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.
Mapping unstructured grid problems to the connection machine
NASA Technical Reports Server (NTRS)
Hammond, Steven W.; Schreiber, Robert
1990-01-01
We present a highly parallel graph mapping technique that enables one to solve unstructured grid problems on massively parallel computers. Many implicit and explicit methods for solving discretizated partial differential equations require each point in the discretization to exchange data with its neighboring points every time step or iteration. The time spent communicating can limit the high performance promised by massively parallel computing. To eliminate this bottleneck, we map the graph of the irregular problem to the graph representing the interconnection topology of the computer such that the sum of the distances that the messages travel is minimized. We show that, in comparison to a naive assignment of processors, our heuristic mapping algorithm significantly reduces the communication time on the Connection Machine, CM-2.
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.
Solving a real-world problem using an evolving heuristically driven schedule builder.
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. PMID:10021741
Solving a real-world problem using an evolving heuristically driven schedule builder.
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.
Solving scheduling tournament problems using a new version of CLONALG
NASA Astrophysics Data System (ADS)
Pérez-Cáceres, Leslie; Riff, María Cristina
2015-01-01
The travelling tournament problem (TTP) is an important and well-known problem within the collective sports research community. The problem is NP-hard which makes difficult finding quality solution in short amount of time. Recently a new kind of TTP has been proposed 'The Relaxed Travelling Tournament Problem'. This version of the problem allows teams to have some days off during the tournament. In this paper, we propose an immune algorithm that is able to solve both problem versions. The algorithm uses moves which are based on the team home/away patterns. One of these moves has been specially designed for the relaxed travel tournament instances. We have tested the algorithm using well-known problem benchmarks and the results obtained are very encouraging.
Three-index Model for Westenberger-Kallrath Benchmark Scheduling Problem
NASA Astrophysics Data System (ADS)
Vooradi, Ramsagar; Shaik, Munawar A.; Gupta, Nikhil M.
2010-10-01
Short-term scheduling of batch operations has become an important research area in the last two decades. Recently Shaik and Floudas (2009) proposed a novel unified model for short-term scheduling using unit-specific event based continuous time representation employing three-index binary and continuous variables. In this work, we extend this three index model to solve a challenging benchmark problem from the scheduling literature that covers most of the features contributing to the complexity of batch process scheduling in industry. In order to implement the problem, new sets of constraints and modifications are incorporated into the three-index model. The different demand instances of the benchmark problem have been solved using the developed model and the results are compared with the literature to demonstrate the effectiveness of the proposed three-index model.
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.
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.
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…
A duty-period-based formulation of the airline crew scheduling problem
Hoffman, K.
1994-12-31
We present a new formulation of the airline crew scheduling problem that explicitly considers the duty periods. We suggest an algorithm for solving the formulation by a column generation approach with branch-and-bound. Computational results are reported for a number of test problems.
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.
Problem-Solving at a Circuit-Board Assembly Machine: A Microanalysis.
ERIC Educational Resources Information Center
Kleifgen, Jo Anne; Frenz-Belken, Patricia
A study described machine operators' problem-solving actions at a computerized circuit-board assembly machine in a small manufacturing plant located on the West Coast. Participants were a machine operator and his supervisor, both from Vietnam, who were building large prototype boards for a major computer corporation. Over a 6.5 minute interval,…
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.
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
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…
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…
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.
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.
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.
NASA Astrophysics Data System (ADS)
Marwati, Rini; Yulianti, Kartika; Pangestu, Herny Wulandari
2016-02-01
A fuzzy evolutionary algorithm is an integration of an evolutionary algorithm and a fuzzy system. In this paper, we present an application of a genetic algorithm to a fuzzy evolutionary algorithm to detect and to solve chromosomes conflict. A chromosome conflict is identified by existence of any two genes in a chromosome that has the same values as two genes in another chromosome. Based on this approach, we construct an algorithm to solve a lecture scheduling problem. Time codes, lecture codes, lecturer codes, and room codes are defined as genes. They are collected to become chromosomes. As a result, the conflicted schedule turns into chromosomes conflict. Built in the Delphi program, results show that the conflicted lecture schedule problem is solvable by this algorithm.
An information theoretic view of the scheduling problem in whole-body CAD
NASA Astrophysics Data System (ADS)
Zhan, Yiqiang; Zhou, Xiang Sean; Krishnan, Arun
2008-03-01
Emerging whole-body imaging technologies push computer aided detection/diagnosis (CAD) to scale up to a whole-body level, which involves multiple organs or anatomical structure. To be exploited in this paper is the fact that the various tasks in whole-body CAD are often highly dependent (e.g., the localization of the femur heads strongly predicts the position of the iliac bifurcation of the aorta). One way to effectively employ task dependency is to schedule the tasks such that outputs of some tasks are used to guide the others. In this sense, optimal task scheduling is key to improve overall performance of a whole-body CAD system. In this paper, we propose a method for task scheduling that is optimal in an information-theoretic sense. The central idea is to schedule tasks in such an order that each operation achieves maximum expected information gain over all the tasks. The formulation embeds two intuitive principles: (1) a task with higher confidence tends to be scheduled earlier; (2) a task with higher predictive power for other tasks tends to be scheduled earlier. More specifically, task dependency is modeled by conditional probability; the outcome of each task is assumed to be probabilistic as well; and the objective function is based on the reduction of the summed conditional entropy over all tasks. The validation is carried out on a challenging CAD problem, multi-organ localization in whole-body CT. Compared to unscheduled and ad hoc scheduled organ detection/localization, our scheduled execution achieves higher accuracy with much less computation time.
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.…
Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem.
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.
Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem.
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
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.
ERIC Educational Resources Information Center
Waters, Melissa B.; Lerman, Dorothea C.; Hovanetz, Alyson N.
2009-01-01
The separate and combined effects of visual schedules and extinction plus differential reinforcement of other behavior (DRO) were evaluated to decrease transition-related problem behavior of 2 children diagnosed with autism. Visual schedules alone were ineffective in reducing problem behavior when transitioning from preferred to nonpreferred…
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.
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.
A divide-and-conquer strategy with particle swarm optimization for the job shop scheduling problem
NASA Astrophysics Data System (ADS)
Zhang, Rui; Wu, Cheng
2010-07-01
An optimization algorithm based on the 'divide-and-conquer' methodology is proposed for solving large job shop scheduling problems with the objective of minimizing total weighted tardiness. The algorithm adopts a non-iterative framework. It first searches for a promising decomposition policy for the operation set by using a simulated annealing procedure in which the solutions are evaluated with reference to the upper bound and the lower bound of the final objective value. Subproblems are then constructed according to the output decomposition policy and each subproblem is related to a subset of operations from the original operation set. Subsequently, all these subproblems are sequentially solved by a particle swarm optimization algorithm, which leads directly to a feasible solution to the original large-scale scheduling problem. Numerical computational experiments are carried out for both randomly generated test problems and the real-world production data from a large speed-reducer factory in China. Results show that the proposed algorithm can achieve satisfactory solution quality within reasonable computational time for large-scale job shop scheduling problems.
Cultural algorithms, an alternative heuristic to solve the job shop scheduling problem
NASA Astrophysics Data System (ADS)
Cortes Rivera, Daniel; Landa Becerra, Ricardo; Coello Coello, Carlos A.
2007-01-01
In this work, an approach for solving the job shop scheduling problem using a cultural algorithm is proposed. Cultural algorithms are evolutionary computation methods that extract domain knowledge during the evolutionary process. Additional to this extracted knowledge, the proposed approach also uses domain knowledge given a priori (based on specific domain knowledge available for the job shop scheduling problem). The proposed approach is compared with respect to a Greedy Randomized Adaptive Search Procedure (GRASP), a Parallel GRASP, a Genetic Algorithm, a Hybrid Genetic Algorithm, and a deterministic method called shifting bottleneck. The cultural algorithm proposed in this article is able to produce competitive results with respect to the two approaches previously indicated at a significantly lower computational cost than at least one of them and without using any sort of parallel processing.
NASA Astrophysics Data System (ADS)
Tabrizi, Babak H.; Farid Ghaderi, Seyed
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.
A time-shared machine repair problem with mixed spares under N-policy
NASA Astrophysics Data System (ADS)
Jain, Madhu; Shekhar, Chandra; Shukla, Shalini
2016-02-01
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.
A Novel Joint Problem of Routing, Scheduling, and Variable-Width Channel Allocation in WMNs
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
Minimizing the total completion time in a two-machine flowshop problem with time delays
NASA Astrophysics Data System (ADS)
Kais Msakni, Mohamed; Khallouli, Wael; Al-Salem, Mohamed; Ladhari, Talel
2016-07-01
This article proposes to solve the problem of minimizing the total completion time in a two-machine permutation flowshop environment in which time delays between the machines are considered. For this purpose, an enumeration algorithm based on the branch-and-bound framework is developed, which includes new lower and upper bounds as well as dominance rules. The computational study shows that problems with up to 40 jobs can be solved in a reasonable amount of time.
NASA Astrophysics Data System (ADS)
Zhu, Li; He, Yongxiang; Xue, Haidong; Chen, Leichen
Traditional genetic algorithms (GA) displays a disadvantage of early-constringency in dealing with scheduling problem. To improve the crossover operators and mutation operators self-adaptively, this paper proposes a self-adaptive GA at the target of multitask scheduling optimization under limited resources. The experiment results show that the proposed algorithm outperforms the traditional GA in evolutive ability to deal with complex task scheduling optimization.
NASA Astrophysics Data System (ADS)
Jafari, Hamed; Salmasi, Nasser
2015-04-01
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.
Borrero, Carrie S W; Vollmer, Timothy R; Borrero, John C; Bourret, Jason C; Sloman, Kimberly N; Samaha, Andrew L; Dallery, Jesse
2010-05-01
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. Results showed that relative rates of responding approximated relative rates of reinforcement. Finally, interventions for problem behavior were evaluated and differential reinforcement of alternative behavior and extinction procedures were implemented to increase appropriate behavior and decrease problem behavior. Practical considerations for the application of the generalized matching equation specific to severe problem behavior are discussed, including difficulties associated with defining a reinforced response, and obtaining steady state responding in clinical settings.
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.
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.
Insight and analysis problem solving in microbes to machines.
Clark, Kevin B
2015-11-01
A key feature for obtaining solutions to difficult problems, insight is oftentimes vaguely regarded as a special discontinuous intellectual process and/or a cognitive restructuring of problem representation or goal approach. However, this nearly century-old state of art devised by the Gestalt tradition to explain the non-analytical or non-trial-and-error, goal-seeking aptitude of primate mentality tends to neglect problem-solving capabilities of lower animal phyla, Kingdoms other than Animalia, and advancing smart computational technologies built from biological, artificial, and composite media. Attempting to provide an inclusive, precise definition of insight, two major criteria of insight, discontinuous processing and problem restructuring, are here reframed using terminology and statistical mechanical properties of computational complexity classes. Discontinuous processing becomes abrupt state transitions in algorithmic/heuristic outcomes or in types of algorithms/heuristics executed by agents using classical and/or quantum computational models. And problem restructuring becomes combinatorial reorganization of resources, problem-type substitution, and/or exchange of computational models. With insight bounded by computational complexity, humans, ciliated protozoa, and complex technological networks, for example, show insight when restructuring time requirements, combinatorial complexity, and problem type to solve polynomial and nondeterministic polynomial decision problems. Similar effects are expected from other problem types, supporting the idea that insight might be an epiphenomenon of analytical problem solving and consequently a larger information processing framework. Thus, this computational complexity definition of insight improves the power, external and internal validity, and reliability of operational parameters with which to classify, investigate, and produce the phenomenon for computational agents ranging from microbes to man-made devices. PMID
Insight and analysis problem solving in microbes to machines.
Clark, Kevin B
2015-11-01
A key feature for obtaining solutions to difficult problems, insight is oftentimes vaguely regarded as a special discontinuous intellectual process and/or a cognitive restructuring of problem representation or goal approach. However, this nearly century-old state of art devised by the Gestalt tradition to explain the non-analytical or non-trial-and-error, goal-seeking aptitude of primate mentality tends to neglect problem-solving capabilities of lower animal phyla, Kingdoms other than Animalia, and advancing smart computational technologies built from biological, artificial, and composite media. Attempting to provide an inclusive, precise definition of insight, two major criteria of insight, discontinuous processing and problem restructuring, are here reframed using terminology and statistical mechanical properties of computational complexity classes. Discontinuous processing becomes abrupt state transitions in algorithmic/heuristic outcomes or in types of algorithms/heuristics executed by agents using classical and/or quantum computational models. And problem restructuring becomes combinatorial reorganization of resources, problem-type substitution, and/or exchange of computational models. With insight bounded by computational complexity, humans, ciliated protozoa, and complex technological networks, for example, show insight when restructuring time requirements, combinatorial complexity, and problem type to solve polynomial and nondeterministic polynomial decision problems. Similar effects are expected from other problem types, supporting the idea that insight might be an epiphenomenon of analytical problem solving and consequently a larger information processing framework. Thus, this computational complexity definition of insight improves the power, external and internal validity, and reliability of operational parameters with which to classify, investigate, and produce the phenomenon for computational agents ranging from microbes to man-made devices.
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.
Human and machine diagnosis of scientific problem-solving abilities
NASA Astrophysics Data System (ADS)
Good, Ron; Kromhout, Robert; Bandler, Wyllis
Diagnosis of the problem-solving state of a novice student in science, by an accomplished teacher, is studied in order to build a computer system that will simulate the process. Although such expert systems have been successfully developed in medicine (MYCIN, INTERNIST/CADUCEUS), very little has been accomplished in science education, even though there is a reasonably close parallel between expert medical diagnosis of patients with physiological problems and expert instructional diagnosis of students with learning problems. The system described in this paper, DIPS: Diagnosis for Instruction in Problem Solving, involves a new line of research for science educators interested in interdisciplinary efforts and ways in which computer technology might be used to better understand how to improve science learning. The basic architecture of the DIPS system is outlined and explained in terms of instruction and research implications, and the role of such intelligent computer systems in science education of the future is considered.
Research on earth observing satellite segmenting and scheduling problem for area targets
NASA Astrophysics Data System (ADS)
He, Renjie; Ruan, Qiming
2005-10-01
The mission of an Earth Observing Satellite (EOS) is to acquire images of specified areas on the Earth surface, in response to observation requests from customers for strategic, environmental, commercial, agricultural, and civil analysis and research. A target imaged can have one out of two shapes: a spot and a large polygonal area. A spot can be covered by a single scene of satellite sensor, while a polygonal area may require cutting-up into several contiguous strips to be completely imaged. Because of the orbit restriction, satellite can only view target during specific windows of opportunity when flying over the target. Furthermore, the satellite can only be tasked during such access time windows. Hence a scheduling method of satellite observing tasks has to be taken into account for utilizing satellite sensor efficiently. This paper intends to solve a specific segmenting and scheduling problem for area targets, which concerned with an optical observing satellite equipped with line array CCD sensor. In the paper, based on the analysis of characters of satellite sensor and observed area target, a new method of segmenting area target is given. And on the basis of segmenting results of area target, a scheduling model for multi area targets is proposed. In the paper end, experimental results and analysis are also presented.
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…
An Efficient Estimation of Distribution Algorithm for Job Shop Scheduling Problem
NASA Astrophysics Data System (ADS)
He, Xiao-Juan; Zeng, Jian-Chao; Xue, Song-Dong; Wang, Li-Fang
An estimation of distribution algorithm with probability model based on permutation information of neighboring operations for job shop scheduling problem was proposed. The probability model was given using frequency information of pair-wise operations neighboring. Then the structure of optimal individual was marked and the operations of optimal individual were partitioned to some independent sub-blocks. To avoid repeating search in same area and improve search speed, each sub-block was taken as a whole to be adjusted. Also, stochastic adjustment to the operations within each sub-block was introduced to enhance the local search ability. The experimental results show that the proposed algorithm is more robust and efficient.
Several problems posed by aerothermal calculations in machines
NASA Astrophysics Data System (ADS)
Dutoya, Denis
Problems encountered in the quantitative prediction of high-temperature aerodynamic flows are discussed, with emphasis placed on problems relating to spatial scale and time scale. It is suggested that the discretization networks employed should be adapted to the configuration under consideration, that special attention be given to the stability of the calculated states, and that the validity of the physical models employed should be verified for each new flow type studied. The MATILDA code is used to solve the Navier-Stokes equations for the example of a diesel engine cylinder.
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-LEPT 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.
Solving short-term electric hydrothermal scheduling problems by exponential multiplier methods
NASA Astrophysics Data System (ADS)
Mijangos, Eugenio
2008-06-01
The short-term electric hydrothermal scheduling (STEHS) problem consists in optimizing the production of hydro and thermal electric generation units over a short time period (up to one week long). The problem described in this work can be modelled as a nonlinear network flow problem with linear and nonlinear side constraints. The minimization of this kind of problem can be performed by exploiting the efficiency of network flow techniques. It lies in minimizing approximately a series of augmented Lagrangian functions including only the side constraints, subject to balance constraints in the nodes and capacity bounds. One of the drawbacks of the multiplier methods with quadratic penalty function is that the augmented Lagrangian is not twice differentiable when it is applied to problems with inequality constraints. This article overcomes this difficulty by using the exponential multiplier method. In order to improve the performance some parameters are tuned. The efficiency of this method over STEHS test problems is illustrated by comparing its CPU-times with those of the quadratic multiplier method and with those of the general purpose codes MINOS, SNOPT, and KNITRO. Numerical results are promising.
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.
Whitley, L. Darrell; Watson, Jean-Paul; Howe, Adele E.
2005-06-01
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 of 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.
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
Analysis problems for sequential dynamical systems and communicating state machines
Barrett, C. L.; Hunt, H. B.; Marathe, M. V.; Ravi, S. S.; Rosenkrantz, D. J.; Stearns, R. E.
2001-01-01
A simple sequential dynamical system (SDS) is a triple (G, F, {pi}), where (i) G(V, E) is an undirected graph with n nodes with each node having a 1-bit state, (ii) F = {l_brace} f{sub 1},f{sub 2},...,f{sub n}{r_brace} is a set of local transition functions with f{sub i} denoting a Boolean function associated with node Vv{sub i} and (iii) {pi} is a fixed permutation of (i.e., a total order on) the nodes in V. A single SDS transition is obtained by updating the states of the nodes in V by evaluating the function associated with each of them in the order given by {pi}. Such a (finite) SDS is a mathematical abstraction of simulation systems [BMR99, BR99]. In this paper, we characterize the computational complexity of determining several phase space properties of SDSs. The properties considered are t-REACHABILITY ('Can a given SDS starting from configuration I reach configuration B in t or fewer transitions?'), REACHABILITY('Can a given SDS starting from configuration I ever reach configuration B?') and FIXED POINT REACHABILITY ('Can a given SDS starting from configuration I ever reach configuration in which it stays for ever?'). Our main result is a sharp dichotomy between classes of SDSs whose behavior is 'easy' to predict and those whose behavior is 'hard' to predict. Specifically, we show the following. (1) The t-REACHABILITY, REACHABILITY and the FIXED POINT REACHABILITY problems for SDSs are PSPACE-complete, even when restricted to graphs of bounded bandwidth (and hence of bounded pathwidth and treewidth) and when the function associated with each node is symmetric. The result holds even for regular graphs of constant degree where all the nodes compute the same symmetric Boolean function. (2) In contrast, the t-REACHABILITYm REACHABILITY and FIXED POINT REACHABILITY problems are solvable in polynomial time for SDSs when the Boolean function associated with each node is symmetric and monotone. Two important consequences of our results are the following: (i) The
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.
A stochastic model for the cell formation problem considering machine reliability
NASA Astrophysics Data System (ADS)
Esmailnezhad, Bahman; Fattahi, Parviz; Kheirkhah, Amir Saman
2015-03-01
This paper presents a new mathematical model to solve cell formation problem in cellular manufacturing systems, where inter-arrival time, processing time, and machine breakdown time are probabilistic. The objective function maximizes the number of operations of each part with more arrival rate within one cell. Because a queue behind each machine; queuing theory is used to formulate the model. To solve the model, two metaheurstic algorithms such as modified particle swarm optimization and genetic algorithm are proposed. For the generation of initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Both metaheurstic algorithms are compared against global solutions obtained from Lingo software's branch and bound (B&B). Also, a statistical method will be used for comparison of solutions of two metaheurstic algorithms. The results of numerical examples indicate that considering the machine breakdown has significant effect on block structures of machine-part matrixes.
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.
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.
NASA Astrophysics Data System (ADS)
Matsui, Shouichi; Watanabe, Isamu; Tokoro, Ken-Ichi
A new genetic algorithm is proposed for solving job-shop scheduling problems where the total number of search points is limited. The objective of the problem is to minimize the makespan. The solution is represented by an operation sequence, i.e., a permutation of operations. The proposed algorithm is based on the framework of the parameter-free genetic algorithm. It encodes a permutation using random keys into a chromosome. A schedule is derived from a permutation using a hybrid scheduling (HS), and the parameter of HS is also encoded in a chromosome. Experiments using benchmark problems show that the proposed algorithm outperforms the previously proposed algorithms, genetic algorithm by Shi et al. and the improved local search by Nakano et al., for large-scale problems under the constraint of limited number of search points.
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.
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.
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
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.
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.
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.
New fuzzy support vector machine for the class imbalance problem in medical datasets classification.
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.
Sun, Ming; Zhao, Lin; Cao, Wei; Xu, Yaoqun; Dai, Xuefeng; Wang, Xiaoxu
2010-09-01
Noisy chaotic neural network (NCNN), which can exhibit stochastic chaotic simulated annealing (SCSA), has been proven to be a powerful tool in solving combinatorial optimization problems. In order to retain the excellent optimization property of SCSA and improve the optimization performance of the NCNN using hysteretic dynamics without increasing network parameters, we first construct an equivalent model of the NCNN and then control noises in the equivalent model to propose a novel hysteretic noisy chaotic neural network (HNCNN). Compared with the NCNN, the proposed HNCNN can exhibit both SCSA and hysteretic dynamics without introducing extra system parameters, and can increase the effective convergence toward optimal or near-optimal solutions at higher noise levels. Broadcast scheduling problem (BSP) in packet radio networks (PRNs) is to design an optimal time-division multiple-access (TDMA) frame structure with minimal frame length, maximal channel utilization, and minimal average time delay. In this paper, the proposed HNCNN is applied to solve BSP in PRNs to demonstrate its performance. Simulation results show that the proposed HNCNN with higher noise amplitudes is more likely to find an optimal or near-optimal TDMA frame structure with a minimal average time delay than previous algorithms.
Duan, Qianqian; Yang, Genke; Xu, Guanglin; Pan, Changchun
2014-01-01
This paper is devoted to develop an approximation method for scheduling refinery crude oil operations by taking into consideration the demand uncertainty. In the stochastic model the demand uncertainty is modeled as random variables which follow a joint multivariate distribution with a specific correlation structure. Compared to deterministic models in existing works, the stochastic model can be more practical for optimizing crude oil operations. Using joint chance constraints, the demand uncertainty is treated by specifying proximity level on the satisfaction of product demands. However, the joint chance constraints usually hold strong nonlinearity and consequently, it is still hard to handle it directly. In this paper, an approximation method combines a relax-and-tight technique to approximately transform the joint chance constraints to a serial of parameterized linear constraints so that the complicated problem can be attacked iteratively. The basic idea behind this approach is to approximate, as much as possible, nonlinear constraints by a lot of easily handled linear constraints which will lead to a well balance between the problem complexity and tractability. Case studies are conducted to demonstrate the proposed methods. Results show that the operation cost can be reduced effectively compared with the case without considering the demand correlation. PMID:24757433
Duan, Qianqian; Yang, Genke; Xu, Guanglin; Pan, Changchun
2014-01-01
This paper is devoted to develop an approximation method for scheduling refinery crude oil operations by taking into consideration the demand uncertainty. In the stochastic model the demand uncertainty is modeled as random variables which follow a joint multivariate distribution with a specific correlation structure. Compared to deterministic models in existing works, the stochastic model can be more practical for optimizing crude oil operations. Using joint chance constraints, the demand uncertainty is treated by specifying proximity level on the satisfaction of product demands. However, the joint chance constraints usually hold strong nonlinearity and consequently, it is still hard to handle it directly. In this paper, an approximation method combines a relax-and-tight technique to approximately transform the joint chance constraints to a serial of parameterized linear constraints so that the complicated problem can be attacked iteratively. The basic idea behind this approach is to approximate, as much as possible, nonlinear constraints by a lot of easily handled linear constraints which will lead to a well balance between the problem complexity and tractability. Case studies are conducted to demonstrate the proposed methods. Results show that the operation cost can be reduced effectively compared with the case without considering the demand correlation. PMID:24757433
Duan, Qianqian; Yang, Genke; Xu, Guanglin; Pan, Changchun
2014-01-01
This paper is devoted to develop an approximation method for scheduling refinery crude oil operations by taking into consideration the demand uncertainty. In the stochastic model the demand uncertainty is modeled as random variables which follow a joint multivariate distribution with a specific correlation structure. Compared to deterministic models in existing works, the stochastic model can be more practical for optimizing crude oil operations. Using joint chance constraints, the demand uncertainty is treated by specifying proximity level on the satisfaction of product demands. However, the joint chance constraints usually hold strong nonlinearity and consequently, it is still hard to handle it directly. In this paper, an approximation method combines a relax-and-tight technique to approximately transform the joint chance constraints to a serial of parameterized linear constraints so that the complicated problem can be attacked iteratively. The basic idea behind this approach is to approximate, as much as possible, nonlinear constraints by a lot of easily handled linear constraints which will lead to a well balance between the problem complexity and tractability. Case studies are conducted to demonstrate the proposed methods. Results show that the operation cost can be reduced effectively compared with the case without considering the demand correlation.
Non-clairvoyant Scheduling Games
NASA Astrophysics Data System (ADS)
Dürr, Christoph; Nguyen, Kim Thang
In a scheduling game, each player owns a job and chooses a machine to execute it. While the social cost is the maximal load over all machines (makespan), the cost (disutility) of each player is the completion time of its own job. In the game, players may follow selfish strategies to optimize their cost and therefore their behaviors do not necessarily lead the game to an equilibrium. Even in the case there is an equilibrium, its makespan might be much larger than the social optimum, and this inefficiency is measured by the price of anarchy - the worst ratio between the makespan of an equilibrium and the optimum. Coordination mechanisms aim to reduce the price of anarchy by designing scheduling policies that specify how jobs assigned to a same machine are to be scheduled. Typically these policies define the schedule according to the processing times as announced by the jobs. One could wonder if there are policies that do not require this knowledge, and still provide a good price of anarchy. This would make the processing times be private information and avoid the problem of truthfulness. In this paper we study these so-called non-clairvoyant policies. In particular, we study the RANDOM policy that schedules the jobs in a random order without preemption, and the EQUI policy that schedules the jobs in parallel using time-multiplexing, assigning each job an equal fraction of CPU time.
A 16-bit Coherent Ising Machine for One-Dimensional Ring and Cubic Graph Problems
Takata, Kenta; Marandi, Alireza; Hamerly, Ryan; Haribara, Yoshitaka; Maruo, Daiki; Tamate, Shuhei; Sakaguchi, Hiromasa; Utsunomiya, Shoko; Yamamoto, Yoshihisa
2016-01-01
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. PMID:27659312
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.
Spruyt, Karen; Raubuck, Danielle L; Grogan, Katie; Gozal, David; Stein, Mark A
2012-01-01
Background Night-to-night variability in sleep of children with attention deficit hyperactivity disorder (ADHD) may be a mediator of behavioral phenotype. We examined the potential association between alertness, sleep, and eating behaviors in children with ADHD and comorbid problems. Methods Sleep was monitored by actigraphy for 7 days. Questionnaires were used to assess sleep complaints, habits and food patterns by parental report, and sleep complaints and sleepiness by child report. Results The group comprised 18 children, including 15 boys, aged 9.4 ± 1.7 years, 88.9% Caucasian, who took one or multiple medications. Children slept on average for 6 hours and 58 minutes with a variability of 1 hour 3 minutes relative to the mean, and their sleepiness scores were highly variable from day to day. Most children had a normal body mass index (BMI). Sleepiness and BMI were associated with sleep schedules and food patterns, such that they accounted for 76% of variance, predominantly by the association of BMI with mean wake after sleep onset and by bedtime sleepiness, with wake after sleep onset variability. Similarly, 97% of variance was shared with eating behaviors, such as desserts and snacks, and fast food meals were associated with morning sleepiness. Conclusion Disrupted sleep and sleepiness appears to favor unhealthy food patterns and may place children with ADHD at increased risk for obesity. PMID:23616725
NASA Astrophysics Data System (ADS)
Trunfio, Roberto
2015-06-01
In a recent article, Guo, Cheng and Wang proposed a randomized search algorithm, called modified generalized extremal optimization (MGEO), to solve the quay crane scheduling problem for container groups under the assumption that schedules are unidirectional. The authors claim that the proposed algorithm is capable of finding new best solutions with respect to a well-known set of benchmark instances taken from the literature. However, as shown in this note, there are some errors in their work that can be detected by analysing the Gantt charts of two solutions provided by MGEO. In addition, some comments on the method used to evaluate the schedule corresponding to a task-to-quay crane assignment and on the search scheme of the proposed algorithm are provided. Finally, to assess the effectiveness of the proposed algorithm, the computational experiments are repeated and additional computational experiments are provided.
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.
Kew, William; Mitchell, John B O
2015-09-01
The application of Machine Learning to cheminformatics is a large and active field of research, but there exist few papers which discuss whether ensembles of different Machine Learning methods can improve upon the performance of their component methodologies. Here we investigated a variety of methods, including kernel-based, tree, linear, neural networks, and both greedy and linear ensemble methods. These were all tested against a standardised methodology for regression with data relevant to the pharmaceutical development process. This investigation focused on QSPR problems within drug-like chemical space. We aimed to investigate which methods perform best, and how the 'wisdom of crowds' principle can be applied to ensemble predictors. It was found that no single method performs best for all problems, but that a dynamic, well-structured ensemble predictor would perform very well across the board, usually providing an improvement in performance over the best single method. Its use of weighting factors allows the greedy ensemble to acquire a bigger contribution from the better performing models, and this helps the greedy ensemble generally to outperform the simpler linear ensemble. Choice of data preprocessing methodology was found to be crucial to performance of each method too.
Personalized Education; Solving a Group Formation and Scheduling Problem for Educational Content
ERIC Educational Resources Information Center
Bahargam, Sanaz; Erdos, Dóra; Bestavros, Azer; Terzi, Evimaria
2015-01-01
Whether teaching in a classroom or a Massive Online Open Course it is crucial to present the material in a way that benefits the audience as a whole. We identify two important tasks to solve towards this objective; (1) group students so that they can maximally benefit from peer interaction and (2) find an optimal schedule of the educational…
Space languages: Solving the classic scheduling problem in Ada and Lisp
NASA Technical Reports Server (NTRS)
Davis, Stephen; Hays, Dan; Wolfsberger, John W.
1988-01-01
The comparison of programming languages is best seen while evaluating similar systems. The strengths and weaknesses of both languages were investigated as the scheduler was being implemented. Some features used in both languages shall be object-oriented paradigms, parallel programming, search and production heuristics, and other classical artificial intelligence implementations.
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.
NASA Technical Reports Server (NTRS)
Swanson, Keith; Bresina, John; Drummond, Mark
1994-01-01
This paper presents a technique for building robust telescope schedules that tend not to break. The technique is called Just-In-Case (JIC) scheduling and it implements the common sense idea of being prepared for likely errors, just in case they should occur. The JIC algorithm analyzes a given schedule, determines where it is likely to break, reinvokes a scheduler to generate a contingent schedule for each highly probable break case, and produces a 'multiply contingent' schedule. The technique was developed for an automatic telescope scheduling problem, and the paper presents empirical results showing that Just-In-Case scheduling performs extremely well for this problem.
Quay crane scheduling with dual cycling
NASA Astrophysics Data System (ADS)
Wang, Dandan; Li, Xiaoping
2015-10-01
In this article, the dual cycling quay crane scheduling problem (D-QCSP) with hatches is addressed to minimize the operation cycles of quay cranes. The problem is decomposed into two sub-problems: the intra-group stage (sequencing stacks within each hatch) and the inter-group stage (scheduling all hatches). A new stack sequencing method is constructed for stacks of each hatch, which is modelled as a two-machine non-permutation flow shop scheduling problem. By removing inner gaps using left-shifting, the adapted hatch scheduling sub-problem is modelled as a two-machine grouped flow shop scheduling problem, which contains more precise processing times. A composite heuristic is proposed for the D-QCSP. Based on the derived lower bound, the heuristic is compared with the best existing heuristics on a large number of instances. Experimental results illustrate that the proposal outperforms the existing methods on all instances and dual cycling needs many fewer quay crane operating cycles than single cycling.
NASA Astrophysics Data System (ADS)
Wang, Hongfeng; Fu, Yaping; Huang, Min; Wang, Junwei
2016-03-01
The operation process design is one of the key issues in the manufacturing and service sectors. As a typical operation process, the scheduling with consideration of the deteriorating effect has been widely studied; however, the current literature only studied single function requirement and rarely considered the multiple function requirements which are critical for a real-world scheduling process. In this article, two function requirements are involved in the design of a scheduling process with consideration of the deteriorating effect and then formulated into two objectives of a mathematical programming model. A novel multiobjective evolutionary algorithm is proposed to solve this model with combination of three strategies, i.e. a multiple population scheme, a rule-based local search method and an elitist preserve strategy. To validate the proposed model and algorithm, a series of randomly-generated instances are tested and the experimental results indicate that the model is effective and the proposed algorithm can achieve the satisfactory performance which outperforms the other state-of-the-art multiobjective evolutionary algorithms, such as nondominated sorting genetic algorithm II and multiobjective evolutionary algorithm based on decomposition, on all the test instances.
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.
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%.
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.
NASA Astrophysics Data System (ADS)
Basri, Azyanzuhaila Hasan; Zainuddin, Zaitul Marlizawati
2014-09-01
High efficiency of port operation is required to succeed in the competition between port container terminals. Berth Allocation and Quay Crane Scheduling are the most important part in container terminal operations. The integrated model is formulated as a MIP problem with the objective to minimize the sum of the dwell times, where a vessel's dwell time is measured between arrival and departure including both times waiting to be berthed and servicing time while berthed. The construction of suitable mathematical model is formulated by considering various practical constraints.
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.
Agnetis, Alessandro; Coppi, Alberto; Corsini, Matteo; Dellino, Gabriella; Meloni, Carlo; Pranzo, Marco
2014-03-01
This research aims at supporting hospital management in making prompt Operating Room (OR) planning decisions, when either unpredicted events occur or alternative scenarios or configurations need to be rapidly evaluated. We design and test a planning tool enabling managers to efficiently analyse several alternatives to the current OR planning and scheduling. To this aim, we propose a decomposition approach. More specifically, we first focus on determining the Master Surgical Schedule (MSS) on a weekly basis, by assigning the different surgical disciplines to the available sessions. Next, we allocate surgeries to each session, focusing on elective patients only. Patients are selected from the waiting lists according to several parameters, including surgery duration, waiting time and priority class of the operations. We performed computational experiments to compare the performance of our decomposition approach with an (exact) integrated approach. The case study selected for our simulations is based on the characteristics of the operating theatre (OT) of a medium-size public Italian hospital. Scalability of the method is tested for different OT sizes. A pilot example is also proposed to highlight the usefulness of our approach for decision support. The proposed decomposition approach finds satisfactory solutions with significant savings in computation time.
Learning to integrate reactivity and deliberation in uncertain planning and scheduling problems
NASA Technical Reports Server (NTRS)
Chien, Steve A.; Gervasio, Melinda T.; Dejong, Gerald F.
1992-01-01
This paper describes an approach to planning and scheduling in uncertain domains. In this approach, a system divides a task on a goal by goal basis into reactive and deliberative components. Initially, a task is handled entirely reactively. When failures occur, the system changes the reactive/deliverative goal division by moving goals into the deliberative component. Because our approach attempts to minimize the number of deliberative goals, we call our approach Minimal Deliberation (MD). Because MD allows goals to be treated reactively, it gains some of the advantages of reactive systems: computational efficiency, the ability to deal with noise and non-deterministic effects, and the ability to take advantage of unforseen opportunities. However, because MD can fall back upon deliberation, it can also provide some of the guarantees of classical planning, such as the ability to deal with complex goal interactions. This paper describes the Minimal Deliberation approach to integrating reactivity and deliberation and describe an ongoing application of the approach to an uncertain planning and scheduling domain.
Agnetis, Alessandro; Coppi, Alberto; Corsini, Matteo; Dellino, Gabriella; Meloni, Carlo; Pranzo, Marco
2014-03-01
This research aims at supporting hospital management in making prompt Operating Room (OR) planning decisions, when either unpredicted events occur or alternative scenarios or configurations need to be rapidly evaluated. We design and test a planning tool enabling managers to efficiently analyse several alternatives to the current OR planning and scheduling. To this aim, we propose a decomposition approach. More specifically, we first focus on determining the Master Surgical Schedule (MSS) on a weekly basis, by assigning the different surgical disciplines to the available sessions. Next, we allocate surgeries to each session, focusing on elective patients only. Patients are selected from the waiting lists according to several parameters, including surgery duration, waiting time and priority class of the operations. We performed computational experiments to compare the performance of our decomposition approach with an (exact) integrated approach. The case study selected for our simulations is based on the characteristics of the operating theatre (OT) of a medium-size public Italian hospital. Scalability of the method is tested for different OT sizes. A pilot example is also proposed to highlight the usefulness of our approach for decision support. The proposed decomposition approach finds satisfactory solutions with significant savings in computation time. PMID:23783452
NASA Astrophysics Data System (ADS)
Yang, Yahong; Zhao, Fuqing; Hong, Yi; Yu, Dongmei
2005-12-01
Integration of process planning with scheduling by considering the manufacturing system's capacity, cost and capacity in its workshop is a critical issue. The concurrency between them can also eliminate the redundant process and optimize the entire production cycle, but most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when constructing schedules. In this paper, a fuzzy inference system (FIS) in choosing alternative machines for integrated process planning and scheduling of a job shop manufacturing system is presented. Instead of choosing alternative machines randomly, machines are being selected based on the machines reliability. The mean time to failure (MTF) values is input in a fuzzy inference mechanism, which outputs the machine reliability. The machine is then being penalized based on the fuzzy output. The most reliable machine will have the higher priority to be chosen. In order to overcome the problem of un-utilization machines, sometimes faced by unreliable machine, the particle swarm optimization (PSO) have been used to balance the load for all the machines. Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling.
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.
NASA Astrophysics Data System (ADS)
Cram, Ana Catalina
As worldwide environmental awareness grow, alternative sources of energy have become important to mitigate climate change. Biogas in particular reduces greenhouse gas emissions that contribute to global warming and has the potential of providing 25% of the annual demand for natural gas in the U.S. In 2011, 55,000 metric tons of methane emissions were reduced and 301 metric tons of carbon dioxide emissions were avoided through the use of biogas alone. Biogas is produced by anaerobic digestion through the fermentation of organic material. It is mainly composed of methane with a rage of 50 to 80% in its concentration. Carbon dioxide covers 20 to 50% and small amounts of hydrogen, carbon monoxide and nitrogen. The biogas production systems are anaerobic digestion facilities and the optimal operation of an anaerobic digester requires the scheduling of all batches from multiple feedstocks during a specific time horizon. The availability times, biomass quantities, biogas production rates and storage decay rates must all be taken into account for maximal biogas production to be achieved during the planning horizon. Little work has been done to optimize the scheduling of different types of feedstock in anaerobic digestion facilities to maximize the total biogas produced by these systems. Therefore, in the present thesis, a new genetic algorithm is developed with the main objective of obtaining the optimal sequence in which different feedstocks will be processed and the optimal time to allocate to each feedstock in the digester with the main objective of maximizing the production of biogas considering different types of feedstocks, arrival times and decay rates. Moreover, all batches need to be processed in the digester in a specified time with the restriction that only one batch can be processed at a time. The developed algorithm is applied to 3 different examples and a comparison with results obtained in previous studies is presented.
A Method of Solving Scheduling Problems Using Improved Guided Genetic Algorithm
NASA Astrophysics Data System (ADS)
Ou, Gyouhi; Tamura, Hiroki; Tanno, Koichi; Tang, Zheng
In this paper, an improved guided genetic algorithm is proposed forJob-shop schueduling problem. The proposed method is improved by genetic algorithm using multipliers which can be adjusted during the search process. The simulation results based on some benchmark problems that proves the proposed method can find better solutions than genetic algorithm and original guided genetic algorithm.
2012-01-01
Background Selecting an appropriate classifier for a particular biological application poses a difficult problem for researchers and practitioners alike. In particular, choosing a classifier depends heavily on the features selected. For high-throughput biomedical datasets, feature selection is often a preprocessing step that gives an unfair advantage to the classifiers built with the same modeling assumptions. In this paper, we seek classifiers that are suitable to a particular problem independent of feature selection. We propose a novel measure, called "win percentage", for assessing the suitability of machine classifiers to a particular problem. We define win percentage as the probability a classifier will perform better than its peers on a finite random sample of feature sets, giving each classifier equal opportunity to find suitable features. Results First, we illustrate the difficulty in evaluating classifiers after feature selection. We show that several classifiers can each perform statistically significantly better than their peers given the right feature set among the top 0.001% of all feature sets. We illustrate the utility of win percentage using synthetic data, and evaluate six classifiers in analyzing eight microarray datasets representing three diseases: breast cancer, multiple myeloma, and neuroblastoma. After initially using all Gaussian gene-pairs, we show that precise estimates of win percentage (within 1%) can be achieved using a smaller random sample of all feature pairs. We show that for these data no single classifier can be considered the best without knowing the feature set. Instead, win percentage captures the non-zero probability that each classifier will outperform its peers based on an empirical estimate of performance. Conclusions Fundamentally, we illustrate that the selection of the most suitable classifier (i.e., one that is more likely to perform better than its peers) not only depends on the dataset and application but also on the
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.
Effects of fixed-time reinforcement schedules on resurgence of problem behavior.
Marsteller, Tonya M; St Peter, Claire C
2014-01-01
Resurgence of problem behavior following the discontinuation of differential reinforcement of alternative behavior (DRA) may be prevented by response-independent reinforcer delivery. In basic research, response-independent reinforcer delivery following DRA prevented resurgence of the initially reinforced response and maintained alternative responding (Lieving & Lattal, 2003, Experiment 3). We evaluated the generality of these results by assessing if fixed-time (FT) reinforcer delivery following DRA would prevent resurgence of problem behavior and maintain appropriate behavior with 4 children with disabilities. For all participants, extinction following DRA produced resurgence of previously reinforced problem behavior and reduced appropriate requests, but FT reinforcer delivery following DRA mitigated resurgence of problem behavior and maintained appropriate requests.
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.
NASA Astrophysics Data System (ADS)
Kubota, Yoshitsune; Numata, Kazumiti
In this paper we propose and evaluate some idea to improve an existing exact algorithm for Input-Output Scheduling Problem (IOSP) in automated warehouses. The existing algorithm is based on LP relaxation of IOSP, which is solved by the column generation method allowing relaxed columns (routes). Our idea is, expecting to enhance LP solution, to impliment the column generation using only exact routes, and to reduce consequently increasing calculation cost by dropping (pegging) unusable edges. The pegging test is done in the preprocessing phase by solving Lagrangian relaxation of IOSP formulated in node cover decision variables. The results of computational experiments show that the proposed algorithm can solve slightly large sized instances in less execution time than existing one.
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
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.
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.
Trajectory classification in circular restricted three-body problem using support vector machine
NASA Astrophysics Data System (ADS)
Li, Weipeng; Huang, Hai; Peng, Fujun
2015-07-01
In the circular restricted three-body problem (CR3BP), transit orbit is a class of orbit which can pass through the bottleneck region of the zero velocity curve and escapes from the vicinity of the primary or the secondary. This kind of orbit plays a very important role in the design of space exploration missions. A kind of low-energy interplanetary transfer, which is called Interplanetary Superhighway (IPS), can be realized by utilizing transit orbits. To use the transit orbit in actual mission design, a key issue is to find an algorithm which can separate the states corresponding to transit orbits from the states corresponding to other types of orbits rapidly. In fact, the distribution of transit orbit in the phase space has been investigated by numerical method, and a Fourier series approximation method has been introduced to describe the boundary of transit orbits. However, the Fourier series approximation method needs several hundred sets of Fourier series. The coefficients of these Fourier series are neither easy to be computed nor convenient to be stored, which makes the method can hardly be used in actual mission design. In this paper, the support vector machine (SVM) is used to classify the trajectories in the CR3BP. Using the Gaussian kernel, the 6-dimensional states in the CR3BP are mapped into an infinite-dimensional space, and the bound of the transit orbits is described by a hyperplane. A training data generation method is introduced, which reduces the size of training data by generating the states near the hyperplane. The numerical results show that the proposed algorithm gives the good correct rate of classification, and its computing speed is much faster than that of the Fourier series approximation method.
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.
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.
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.
Time sharing massively parallel machines. Draft
Gorda, B.; Wolski, R.
1995-03-01
As part of the Massively Parallel Computing Initiative (MPCI) at the Lawrence Livermore National Laboratory, the authors have developed a simple, effective and portable time sharing mechanism by scheduling gangs of processes on tightly coupled parallel machines. By time-sharing the resources, the system interleaves production and interactive jobs. Immediate priority is given to interactive use, maintaining good response time. Production jobs are scheduled during idle periods, making use of the otherwise unused resources. In this paper the authors discuss their experience with gang scheduling over the 3 year life-time of the project. In section 2, they motivate the project and discuss some of its details. Section 3.0 describes the general scheduling problem and how gang scheduling addresses it. In section 4.0, they describe the implementation. Section 8.0 presents results culled over the lifetime of the project. They conclude this paper with some observations and possible future directions.
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.
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.
ERIC Educational Resources Information Center
Davis, Harold S.; Bechard, Joseph E.
A flexible schedule allows teachers to change group size, group composition, and class length according to the purpose of the lesson. This pamphlet presents various "master" schedules for flexible scheduling: (1) Simple block schedules, (2) back-to-back schedules, (3) interdisciplinary schedules, (4) school-wide block schedules, (5) open-lab…
Stochastic programming methods for scheduling of airport runway operations under uncertainty
NASA Astrophysics Data System (ADS)
Solveling, Gustaf
Runway systems at airports have been identified as a major source of delay in the aviation system and efficient runway operations are, therefore, important to maintain and/or increase the capacity of the entire aviation system. The goal of the airport runway scheduling problem is to schedule a set of aircraft and minimize a given objective while maintaining separation requirements and enforcing other operational constraints. Uncertain factors such as weather, surrounding traffic and pilot behavior affect when aircraft can be scheduled, and these factors need to be considered in planning models. In this thesis we propose two stochastic programs to address the stochastic airport runway scheduling problem and similarly structured machine scheduling problems. In the first part, we develop a two-stage stochastic integer programming model and analyze it by developing alternative formulations and solution methods. As part of our analysis, we first show that a restricted version of the stochastic runway scheduling problem is equivalent to a machine scheduling problem on a single machine with sequence dependent setup times and stochastic due dates. We then extend this restricted model by considering characteristics specific to the runway scheduling problem and present two different stochastic integer programming models. We derive some tight valid inequalities for these formulations, and we propose a solution methodology based on sample average approximation and Lagrangian based scenario decomposition. Realistic data sets are then used to perform a detailed computational study involving implementations and analyses of several different configurations of the models. The results from the computational tests indicate that practically implementable truncated versions of the proposed solution algorithm almost always produce very high quality solutions. In the second part, we propose a sampling based stochastic program for a general machine scheduling problem with similar
Investigating the Human Computer Interaction Problems with Automated Teller Machine Navigation Menus
ERIC Educational Resources Information Center
Curran, Kevin; King, David
2008-01-01
Purpose: The automated teller machine (ATM) has become an integral part of our society. However, using the ATM can often be a frustrating experience as people frequently reinsert cards to conduct multiple transactions. This has led to the research question of whether ATM menus are designed in an optimal manner. This paper aims to address the…
Polynomial optimization techniques for activity scheduling. Optimization based prototype scheduler
NASA Technical Reports Server (NTRS)
Reddy, Surender
1991-01-01
Polynomial optimization techniques for activity scheduling (optimization based prototype scheduler) are presented in the form of the viewgraphs. The following subject areas are covered: agenda; need and viability of polynomial time techniques for SNC (Space Network Control); an intrinsic characteristic of SN scheduling problem; expected characteristics of the schedule; optimization based scheduling approach; single resource algorithms; decomposition of multiple resource problems; prototype capabilities, characteristics, and test results; computational characteristics; some features of prototyped algorithms; and some related GSFC references.
NASA Astrophysics Data System (ADS)
Petrelli, Maurizio; Perugini, Diego
2016-10-01
Machine-learning methods are evaluated to study the intriguing and debated topic of discrimination among different tectonic environments using geochemical and isotopic data. Volcanic rocks characterized by a whole geochemical signature of major elements (SiO2, TiO2, Al2O3, Fe2O3T, CaO, MgO, Na2O, K2O), selected trace elements (Sr, Ba, Rb, Zr, Nb, La, Ce, Nd, Hf, Sm, Gd, Y, Yb, Lu, Ta, Th) and isotopes (206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb, 87Sr/86Sr and 143Nd/144Nd) have been extracted from open-access and comprehensive petrological databases (i.e., PetDB and GEOROC). The obtained dataset has been analyzed using support vector machines, a set of supervised machine-learning methods, which are considered particularly powerful in classification problems. Results from the application of the machine-learning methods show that the combined use of major, trace elements and isotopes allows associating the geochemical composition of rocks to the relative tectonic setting with high classification scores (93 %, on average). The lowest scores are recorded from volcanic rocks deriving from back-arc basins (65 %). All the other tectonic settings display higher classification scores, with oceanic islands reaching values up to 99 %. Results of this study could have a significant impact in other petrological studies potentially opening new perspectives for petrologists and geochemists. Other examples of applications include the development of more robust geothermometers and geobarometers and the recognition of volcanic sources for tephra layers in tephro-chronological studies.
NASA Astrophysics Data System (ADS)
Effati, Meysam; Thill, Jean-Claude; Shabani, Shahin
2015-04-01
The contention of this paper is that many social science research problems are too "wicked" to be suitably studied using conventional statistical and regression-based methods of data analysis. This paper argues that an integrated geospatial approach based on methods of machine learning is well suited to this purpose. Recognizing the intrinsic wickedness of traffic safety issues, such approach is used to unravel the complexity of traffic crash severity on highway corridors as an example of such problems. The support vector machine (SVM) and coactive neuro-fuzzy inference system (CANFIS) algorithms are tested as inferential engines to predict crash severity and uncover spatial and non-spatial factors that systematically relate to crash severity, while a sensitivity analysis is conducted to determine the relative influence of crash severity factors. Different specifications of the two methods are implemented, trained, and evaluated against crash events recorded over a 4-year period on a regional highway corridor in Northern Iran. Overall, the SVM model outperforms CANFIS by a notable margin. The combined use of spatial analysis and artificial intelligence is effective at identifying leading factors of crash severity, while explicitly accounting for spatial dependence and spatial heterogeneity effects. Thanks to the demonstrated effectiveness of a sensitivity analysis, this approach produces comprehensive results that are consistent with existing traffic safety theories and supports the prioritization of effective safety measures that are geographically targeted and behaviorally sound on regional highway corridors.
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.
Lole, Lisa; Gonsalvez, Craig J; Barry, Robert J; Blaszczynski, Alex
2014-06-01
Physiological arousal is purportedly a key determinant in the development and maintenance of gambling behaviors, with problem gambling conceptualized in terms of abnormal autonomic responses. Theoretical conceptualizations of problem gambling are discordant regarding the nature of deficit in this disorder; some accounts posit that problem gamblers are hypersensitive to reward, and others that they are hyposensitive to reward and/or punishment. Previous research examining phasic electrodermal responses in gamblers has been limited to laboratory settings, and reactions to real gaming situations need to be examined. Skin conductance responses (SCRs) to losses, wins, and losses disguised as wins (LDWs) were recorded from 15 problem gamblers (PGs) and 15 nonproblem gamblers (NPGs) while they wagered their own money during electronic gaming machine play. PGs demonstrated significantly reduced SCRs to reward. SCRs to losses and LDWs did not differ for either PGs or NPGs. This hyposensitivity to wins may reflect abnormalities in incentive processing, and may represent a potential biological marker for problem gambling. PMID:24588340
Scheduling constrained tools using heuristic techniques
NASA Astrophysics Data System (ADS)
Maram, Venkataramana; Rahman, Syariza Abdul; Maram, Sandhya Rani
2015-12-01
One of the main challenge to the current manufacturing production planning is to provide schedules of operations to maximize resource utilization to yield highest overall productivity. This is achieved by scheduling available resources to activities. There can be many different real time scenarios with different combination of input resources to produce parts. In this paper, the problem is simplified to single machine with individual process times and due dates to represent the real world scheduling problem. The main objective function is to minimize the total tardiness or late jobs. Nearest greedy method of assignment problem algorithm is used to find the initial solution followed by Simulated Annealing (SA) algorithm for the improvement part. Simulated Annealing is one of the meta-heuristic techniques in solving combinatorial optimization problem. The general purpose Microsoft Visual C++ is used to developed algorithm for finding the best solution. The proposed hybrid approach able to generate best schedule in 7th and optimal in 170th iteration with tardiness 8 and 7 hours respectively.
SPINN: a straightforward machine learning solution to the pulsar candidate selection problem
NASA Astrophysics Data System (ADS)
Morello, V.; Barr, E. D.; Bailes, M.; Flynn, C. M.; Keane, E. F.; van Straten, W.
2014-09-01
We describe SPINN (Straightforward Pulsar Identification using Neural Networks), a high-performance machine learning solution developed to process increasingly large data outputs from pulsar surveys. SPINN has been cross-validated on candidates from the southern High Time Resolution Universe (HTRU) survey and shown to identify every known pulsar found in the survey data while maintaining a false positive rate of 0.64 per cent. Furthermore, it ranks 99 per cent of pulsars among the top 0.11 per cent of candidates, and 95 per cent among the top 0.01 per cent. In conjunction with the PEASOUP pipeline, it has already discovered four new pulsars in a re-processing of the intermediate Galactic latitude area of HTRU, three of which have spin periods shorter than 5 ms. SPINN's ability to reduce the amount of candidates to visually inspect by up to four orders of magnitude makes it a very promising tool for future large-scale pulsar surveys. In an effort to provide a common testing ground for pulsar candidate selection tools and stimulate interest in their development, we also make publicly available the set of candidates on which SPINN was cross-validated.
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.
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.
Engineering-Based Problem Solving in the Middle School: Design and Construction with Simple Machines
ERIC Educational Resources Information Center
English, Lyn D.; Hudson, Peter; Dawes, Les
2013-01-01
Incorporating engineering concepts into middle school curriculum is seen as an effective way to improve students' problem-solving skills. A selection of findings is reported from a science, technology, engineering and mathematics (STEM)-based unit in which students in the second year (grade 8) of a three-year longitudinal study explored…
MONTO: A Machine-Readable Ontology for Teaching Word Problems in Mathematics
ERIC Educational Resources Information Center
Lalingkar, Aparna; Ramnathan, Chandrashekar; Ramani, Srinivasan
2015-01-01
The Indian National Curriculum Framework has as one of its objectives the development of mathematical thinking and problem solving ability. However, recent studies conducted in Indian metros have expressed concern about students' mathematics learning. Except in some private coaching academies, regular classroom teaching does not include problem…
NASA Astrophysics Data System (ADS)
Chen, Xili; Hao, Xinchang; Lin, Hao Wen; Murata, Tomohiro
The applications of composite dispatching rules for multi objective dynamic scheduling have been widely studied in literature. In general, a composite dispatching rule is a combination of several elementary dispatching rules, which is designed to optimize multiple objectives of interest under a certain scheduling environment. The relative importance of elementary dispatching rules is modeled by weight factors. A critical issue for implementation of composite dispatching rule is that the inappropriate weight values may result in poor performance. This paper presents an offline scheduling knowledge acquisition method based on reinforcement learning using simulation technique. The scheduling knowledge is applied to adjust the appropriate weight values of elementary dispatching rules in composite manner with respect to work in process fluctuation of machines during online scheduling. Implementation of the proposed method in a two objectives dynamic job shop scheduling problem is demonstrated and the results are satisfactory.
Planning and scheduling research at NASA Ames Research Center
NASA Technical Reports Server (NTRS)
Friedland, Peter
1990-01-01
Planning and scheduling is the area of artificial intelligence research that focuses on the determination of a series of operations to achieve some set of (possibly) interacting goals and the placement of those operations in a timeline that allows them to be accomplished given available resources. Work in this area at the NASA Ames Research Center ranging from basic research in constrain-based reasoning and machine learning, to the development of efficient scheduling tools, to the application of such tools to complex agency problems is described.
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.
Multiagent scheduling method with earliness and tardiness objectives in flexible job shops.
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.
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 task 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.,
Adaptive Parallel Job Scheduling with Flexible CoScheduling
Frachtenberg, Eitan; Feitelson, Dror; Petrini, Fabrizio; Fernandez, Juan
2005-11-01
Abstract—Many scientific and high-performance computing applications consist of multiple processes running on different processors that communicate frequently. Because of their synchronization needs, these applications can suffer severe performance penalties if their processes are not all coscheduled to run together. Two common approaches to coscheduling jobs are batch scheduling, wherein nodes are dedicated for the duration of the run, and gang scheduling, wherein time slicing is coordinated across processors. Both work well when jobs are load-balanced and make use of the entire parallel machine. However, these conditions are rarely met and most realistic workloads consequently suffer from both internal and external fragmentation, in which resources and processors are left idle because jobs cannot be packed with perfect efficiency. This situation leads to reduced utilization and suboptimal performance. Flexible CoScheduling (FCS) addresses this problem by monitoring each job’s computation granularity and communication pattern and scheduling jobs based on their synchronization and load-balancing requirements. In particular, jobs that do not require stringent synchronization are identified, and are not coscheduled; instead, these processes are used to reduce fragmentation. FCS has been fully implemented on top of the STORM resource manager on a 256-processor Alpha cluster and compared to batch, gang, and implicit coscheduling algorithms. This paper describes in detail the implementation of FCS and its performance evaluation with a variety of workloads, including large-scale benchmarks, scientific applications, and dynamic workloads. The experimental results show that FCS saturates at higher loads than other algorithms (up to 54 percent higher in some cases), and displays lower response times and slowdown than the other algorithms in nearly all scenarios.
NASA Technical Reports Server (NTRS)
Tanner, Steve; Hughes, Angi; Byrd, Jim
1987-01-01
Resupply scheduling for the Space Station presents some formidable logistics problems. One of the most basic problems is assigning supplies to a series of shuttle resupply missions. A prototype logistics expert system which constructs resupply schedules was developed. This prototype is able to reconstruct feasible resupply plans. In addition, analysts can use the system to evaluate the impact of adding, deleting or modifying launches, cargo space, experiments, etc.
... Labor & birth > Scheduling a c-section Scheduling a c-section E-mail to a friend Please fill ... develop before she’s born. Why can scheduling a c-section for non-medical reasons be a problem? ...
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.
Cakar, Tarik; Koker, Rasit
2015-01-01
A particle swarm optimization algorithm (PSO) has been used to solve the single machine total weighted tardiness problem (SMTWT) with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA) and simulated annealing (SA) have been used. In the subhybrid system (GA and SA), GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR) has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system. PMID:26221134
Sun, Yan; Lang, Maoxiang; Wang, Danzhu
2016-01-01
The transportation of hazardous materials is always accompanied by considerable risk that will impact public and environment security. As an efficient and reliable transportation organization, a multimodal service should participate in the transportation of hazardous materials. In this study, we focus on transporting hazardous materials through the multimodal service network and explore the hazardous materials multimodal routing problem from the operational level of network planning. To formulate this problem more practicably, minimizing the total generalized costs of transporting the hazardous materials and the social risk along the planned routes are set as the optimization objectives. Meanwhile, the following formulation characteristics will be comprehensively modelled: (1) specific customer demands; (2) multiple hazardous material flows; (3) capacitated schedule-based rail service and uncapacitated time-flexible road service; and (4) environmental risk constraint. A bi-objective mixed integer nonlinear programming model is first built to formulate the routing problem that combines the formulation characteristics above. Then linear reformations are developed to linearize and improve the initial model so that it can be effectively solved by exact solution algorithms on standard mathematical programming software. By utilizing the normalized weighted sum method, we can generate the Pareto solutions to the bi-objective optimization problem for a specific case. Finally, a large-scale empirical case study from the Beijing-Tianjin-Hebei Region in China is presented to demonstrate the feasibility of the proposed methods in dealing with the practical problem. Various scenarios are also discussed in the case study. PMID:27483294
Sun, Yan; Lang, Maoxiang; Wang, Danzhu
2016-01-01
The transportation of hazardous materials is always accompanied by considerable risk that will impact public and environment security. As an efficient and reliable transportation organization, a multimodal service should participate in the transportation of hazardous materials. In this study, we focus on transporting hazardous materials through the multimodal service network and explore the hazardous materials multimodal routing problem from the operational level of network planning. To formulate this problem more practicably, minimizing the total generalized costs of transporting the hazardous materials and the social risk along the planned routes are set as the optimization objectives. Meanwhile, the following formulation characteristics will be comprehensively modelled: (1) specific customer demands; (2) multiple hazardous material flows; (3) capacitated schedule-based rail service and uncapacitated time-flexible road service; and (4) environmental risk constraint. A bi-objective mixed integer nonlinear programming model is first built to formulate the routing problem that combines the formulation characteristics above. Then linear reformations are developed to linearize and improve the initial model so that it can be effectively solved by exact solution algorithms on standard mathematical programming software. By utilizing the normalized weighted sum method, we can generate the Pareto solutions to the bi-objective optimization problem for a specific case. Finally, a large-scale empirical case study from the Beijing–Tianjin–Hebei Region in China is presented to demonstrate the feasibility of the proposed methods in dealing with the practical problem. Various scenarios are also discussed in the case study. PMID:27483294
Sun, Yan; Lang, Maoxiang; Wang, Danzhu
2016-07-28
The transportation of hazardous materials is always accompanied by considerable risk that will impact public and environment security. As an efficient and reliable transportation organization, a multimodal service should participate in the transportation of hazardous materials. In this study, we focus on transporting hazardous materials through the multimodal service network and explore the hazardous materials multimodal routing problem from the operational level of network planning. To formulate this problem more practicably, minimizing the total generalized costs of transporting the hazardous materials and the social risk along the planned routes are set as the optimization objectives. Meanwhile, the following formulation characteristics will be comprehensively modelled: (1) specific customer demands; (2) multiple hazardous material flows; (3) capacitated schedule-based rail service and uncapacitated time-flexible road service; and (4) environmental risk constraint. A bi-objective mixed integer nonlinear programming model is first built to formulate the routing problem that combines the formulation characteristics above. Then linear reformations are developed to linearize and improve the initial model so that it can be effectively solved by exact solution algorithms on standard mathematical programming software. By utilizing the normalized weighted sum method, we can generate the Pareto solutions to the bi-objective optimization problem for a specific case. Finally, a large-scale empirical case study from the Beijing-Tianjin-Hebei Region in China is presented to demonstrate the feasibility of the proposed methods in dealing with the practical problem. Various scenarios are also discussed in the case study.
Due-Window Assignment Scheduling with Variable Job Processing Times
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
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.
NASA Technical Reports Server (NTRS)
1993-01-01
Advanced Scheduling Environment is a software product designed and marketed by AVYX, Inc. to provide scheduling solutions for complex manufacturing environments. It can be adapted to specific scheduling and manufacturing processes and has led to substantial cost savings. The system was originally developed for NASA use in scheduling Space Shuttle flights and satellite activities. AVYX, Inc. is an offshoot of a company formed to provide computer-related services to NASA. TREES-plus, the company's initial product became the programming language for the advanced scheduling environment system.
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.
Xu, Jiuping; Feng, Cuiying
2014-01-01
This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method.
Xu, Jiuping
2014-01-01
This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method. PMID:24550708
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.
Effects of industrialization on working schedules.
Kogi, K
1976-12-01
Recent increase in industrialization has resulted in attention being drawn to diversity of working schedules and their interrelation with content of work. The results of joint research revealed that adaptation to industrial jobs is made difficult because of urban conditions and modern work-control systems, such as workers in flexible-working-time systems who apparently favored regular daily working cycles. Long overtime work in smaller undertakings and the spread of shift systems are two important features of industrial work schedules, each being dominant among skilled and non-skilled jobs. Another important facet is the problem of intra-shift work-rest schedules significant for machine operations and vigilance tasks. It is suggested that phases of work that become intolerable are correlated with unnatural work rhythms. More emphasis should thus be laid on both phase-shifted work and non-self-governing work, the key factor being the relevance of attained data to real work in the whole course of schedules of work and rest.
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.
29 CFR 1910.218 - Forging machines.
Code of Federal Regulations, 2013 CFR
2013-07-01
... 29 Labor 5 2013-07-01 2013-07-01 false Forging machines. 1910.218 Section 1910.218 Labor... OCCUPATIONAL SAFETY AND HEALTH STANDARDS Machinery and Machine Guarding § 1910.218 Forging machines. (a... other identifier, for the forging machine which was inspected. (ii) Scheduling and recording...
29 CFR 1910.218 - Forging machines.
Code of Federal Regulations, 2012 CFR
2012-07-01
... 29 Labor 5 2012-07-01 2012-07-01 false Forging machines. 1910.218 Section 1910.218 Labor... OCCUPATIONAL SAFETY AND HEALTH STANDARDS Machinery and Machine Guarding § 1910.218 Forging machines. (a... other identifier, for the forging machine which was inspected. (ii) Scheduling and recording...
29 CFR 1910.218 - Forging machines.
Code of Federal Regulations, 2014 CFR
2014-07-01
... 29 Labor 5 2014-07-01 2014-07-01 false Forging machines. 1910.218 Section 1910.218 Labor... OCCUPATIONAL SAFETY AND HEALTH STANDARDS Machinery and Machine Guarding § 1910.218 Forging machines. (a... other identifier, for the forging machine which was inspected. (ii) Scheduling and recording...
Flexible Job Shop Scheduling with Multi-level Job Structures
NASA Astrophysics Data System (ADS)
Jang, Yang-Ja; Kim, Ki-Dong; Jang, Seong-Yong; Park, Jinwoo
This paper deals with a scheduling problem in a flexible job shop with multi-level job structures where end products are assembled from sub-assemblies or manufactured components. For such shops MRP (Material Requirement Planning) logic is frequently used to synchronize and pace the production activities for the required parts. However, in MRP, the planning of operational-level activities is left to short term scheduling. So, we need a good scheduling algorithm to generate feasible schedules taking into account shop floor characteristics and multi-level job structures used in MRP. In this paper, we present a GA (Genetic Algorithm) solution for this complex scheduling problem based on a new gene to reflect the machine assignment, operation sequences and the levels of the operations relative to final assembly operation. The relative operation level is the control parameter that paces the completion timing of the components belonging to the same branch in the multi-level job hierarchy. We compare the genetic algorithm with several dispatching rules in terms of total tardiness and the genetic algorithm shows outstanding performance for about forty modified standard job-shop problem instances.
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.
Southwell, Jenni; Boreham, Paul; Laffan, Warren
2008-06-01
Local gambling venues are an important contemporary context for older people's gambling in many parts of the world typically being more accessible to this segment of the population than traditional, centralised gambling venues, such as casinos. This study, undertaken in South East Queensland, analyses older people's electronic gaming machine (EGM) behaviour and motivations, specifically in the context of licensed social and recreational clubs-a popular local gambling venue in many parts of Australia. The study gathered data via a postal survey of 80 managers of licensed clubs, interviews with Gambling Help services and a survey of 414 people aged 60+ who regularly play EGMs, self-administered on site at local clubs. The analysis undertaken suggests that certain age-related circumstances of older people-such as being without a partner, having a disability that impacts on everyday activities, having a low annual income, and no longer participating in the workforce-are associated with higher overall levels of motivation for playing EGMs and greater reliance on EGMs to meet social, recreational and mental health needs. Over a quarter of the older people surveyed (27%) reported drawing on their savings to fund their EGM gambling. Certain categories of older people, including those who were without a partner and those with a disability, were more likely to report drawing on their savings to fund EGM play and betting more than they could afford to lose, pointing to age-related vulnerabilities older people may experience to the negative impacts of gambling given the greater likelihood of their dependency on smaller, fixed incomes. The explanatory contribution of a range of demographic and motivational variables on problem/moderate risk gambling status was computed via a logistic regression model. Younger age (60-69), male gender, single marital status and being motivated to play EGMs to experience excitement and to win money all emerged as significant predictors in the
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.
Zhang, Weizhe; Bai, Enci; He, Hui; Cheng, Albert M.K.
2015-01-01
Reducing energy consumption is becoming very important in order to keep battery life and lower overall operational costs for heterogeneous real-time multiprocessor systems. In this paper, we first formulate this as a combinatorial optimization problem. Then, a successful meta-heuristic, called Shuffled Frog Leaping Algorithm (SFLA) is proposed to reduce the energy consumption. Precocity remission and local optimal avoidance techniques are proposed to avoid the precocity and improve the solution quality. Convergence acceleration significantly reduces the search time. Experimental results show that the SFLA-based energy-aware meta-heuristic uses 30% less energy than the Ant Colony Optimization (ACO) algorithm, and 60% less energy than the Genetic Algorithm (GA) algorithm. Remarkably, the running time of the SFLA-based meta-heuristic is 20 and 200 times less than ACO and GA, respectively, for finding the optimal solution. PMID:26110406
Zhang, Weizhe; Bai, Enci; He, Hui; Cheng, Albert M K
2015-06-11
Reducing energy consumption is becoming very important in order to keep battery life and lower overall operational costs for heterogeneous real-time multiprocessor systems. In this paper, we first formulate this as a combinatorial optimization problem. Then, a successful meta-heuristic, called Shuffled Frog Leaping Algorithm (SFLA) is proposed to reduce the energy consumption. Precocity remission and local optimal avoidance techniques are proposed to avoid the precocity and improve the solution quality. Convergence acceleration significantly reduces the search time. Experimental results show that the SFLA-based energy-aware meta-heuristic uses 30% less energy than the Ant Colony Optimization (ACO) algorithm, and 60% less energy than the Genetic Algorithm (GA) algorithm. Remarkably, the running time of the SFLA-based meta-heuristic is 20 and 200 times less than ACO and GA, respectively, for finding the optimal solution.
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.
Kleck, W
1982-04-01
Structuring a schedule - whether by Critical Path Method (CPM) or Precedence Charting System (PCS) - involves estimating the duration of one or more activities and arranging them in the most logical sequence. Given the start date, the completion date is relatively simple to determine. What is then so complicated about the process. It is complicated by the people involved - the people who make the schedules and the people who attempt to follow them. Schedules are an essential part of project management and construction contract administration. Much of the material available pertains to the mechanics of schedules, the types of logic networks, the ways that data can be generated and presented. This paper sheds light on other facets of the subject - the statistical and philosophical fundamentals involved in scheduling.
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
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.
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.
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.
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.
Amirghasemi, Mehrdad; Zamani, Reza
2014-01-01
This paper presents an effective procedure for solving the job shop problem. Synergistically combining small and large neighborhood schemes, the procedure consists of four components, namely (i) a construction method for generating semi-active schedules by a forward-backward mechanism, (ii) a local search for manipulating a small neighborhood structure guided by a tabu list, (iii) a feedback-based mechanism for perturbing the solutions generated, and (iv) a very large-neighborhood local search guided by a forward-backward shifting bottleneck method. The combination of shifting bottleneck mechanism and tabu list is used as a means of the manipulation of neighborhood structures, and the perturbation mechanism employed diversifies the search. A feedback mechanism, called repeat-check, detects consequent repeats and ignites a perturbation when the total number of consecutive repeats for two identical makespan values reaches a given threshold. The results of extensive computational experiments on the benchmark instances indicate that the combination of these four components is synergetic, in the sense that they collectively make the procedure fast and robust. PMID:24808999
Amirghasemi, Mehrdad; Zamani, Reza
2014-01-01
This paper presents an effective procedure for solving the job shop problem. Synergistically combining small and large neighborhood schemes, the procedure consists of four components, namely (i) a construction method for generating semi-active schedules by a forward-backward mechanism, (ii) a local search for manipulating a small neighborhood structure guided by a tabu list, (iii) a feedback-based mechanism for perturbing the solutions generated, and (iv) a very large-neighborhood local search guided by a forward-backward shifting bottleneck method. The combination of shifting bottleneck mechanism and tabu list is used as a means of the manipulation of neighborhood structures, and the perturbation mechanism employed diversifies the search. A feedback mechanism, called repeat-check, detects consequent repeats and ignites a perturbation when the total number of consecutive repeats for two identical makespan values reaches a given threshold. The results of extensive computational experiments on the benchmark instances indicate that the combination of these four components is synergetic, in the sense that they collectively make the procedure fast and robust.
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.
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.
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.
Intelligent perturbation algorithms for space scheduling optimization
NASA Technical Reports Server (NTRS)
Kurtzman, Clifford R.
1991-01-01
Intelligent perturbation algorithms for space scheduling optimization are presented in the form of the viewgraphs. The following subject areas are covered: optimization of planning, scheduling, and manifesting; searching a discrete configuration space; heuristic algorithms used for optimization; use of heuristic methods on a sample scheduling problem; intelligent perturbation algorithms are iterative refinement techniques; properties of a good iterative search operator; dispatching examples of intelligent perturbation algorithm and perturbation operator attributes; scheduling implementations using intelligent perturbation algorithms; major advances in scheduling capabilities; the prototype ISF (industrial Space Facility) experiment scheduler; optimized schedule (max revenue); multi-variable optimization; Space Station design reference mission scheduling; ISF-TDRSS command scheduling demonstration; and example task - communications check.
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.
NASA Astrophysics Data System (ADS)
Hoffmann, Achim; Mahidadia, Ashesh
The purpose of this chapter is to present fundamental ideas and techniques of machine learning suitable for the field of this book, i.e., for automated scientific discovery. The chapter focuses on those symbolic machine learning methods, which produce results that are suitable to be interpreted and understood by humans. This is particularly important in the context of automated scientific discovery as the scientific theories to be produced by machines are usually meant to be interpreted by humans. This chapter contains some of the most influential ideas and concepts in machine learning research to give the reader a basic insight into the field. After the introduction in Sect. 1, general ideas of how learning problems can be framed are given in Sect. 2. The section provides useful perspectives to better understand what learning algorithms actually do. Section 3 presents the Version space model which is an early learning algorithm as well as a conceptual framework, that provides important insight into the general mechanisms behind most learning algorithms. In section 4, a family of learning algorithms, the AQ family for learning classification rules is presented. The AQ family belongs to the early approaches in machine learning. The next, Sect. 5 presents the basic principles of decision tree learners. Decision tree learners belong to the most influential class of inductive learning algorithms today. Finally, a more recent group of learning systems are presented in Sect. 6, which learn relational concepts within the framework of logic programming. This is a particularly interesting group of learning systems since the framework allows also to incorporate background knowledge which may assist in generalisation. Section 7 discusses Association Rules - a technique that comes from the related field of Data mining. Section 8 presents the basic idea of the Naive Bayesian Classifier. While this is a very popular learning technique, the learning result is not well suited for
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.
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.
Xiao, Feng; Shen, Hong-Bin
2015-11-23
The α-helical transmembrane proteins constitute 25% of the entire human proteome space and are difficult targets in high-resolution wet-lab structural studies, calling for accurate computational predictors. We present a novel sequence-based method called MemBrain-Rasa to predict relative solvent accessibility surface area (rASA) from primary sequences. MemBrain-Rasa features by an ensemble prediction protocol composed of a statistical machine-learning engine, which is trained in the sequential feature space, and a segment template similarity-based engine, which is constructed with solved structures and sequence alignment. We locally constructed a comprehensive database of residue relative solvent accessibility surface area from the solved protein 3D structures in the PDB database. It is searched against for segment templates that are expected to be structurally similar to the query sequence's segments. The segment template-based prediction is then fused with the support vector regression outputs using knowledge rules. Our experiments show that pure machine learning output cannot cover the entire rASA solution space and will have a serious prediction preference problem due to the relatively small size of membrane protein structures that can be used as the training samples. The template-based engine solves this problem very well, resulting in significant improvement of the prediction performance. MemBrain-Rasa achieves a Pearson correlation coefficient of 0.733 and mean absolute error of 13.593 on the benchmark dataset, which are 26.4% and 26.1% better than existing predictors. MemBrain-Rasa represents a new progress in structure modeling of α-helical transmembrane proteins. MemBrain-Rasa is available at www.csbio.sjtu.edu.cn/bioinf/MemBrain/.
Heuristics: Intelligent search strategies for computer problem solving
Pearl, J.
1984-01-01
Heuristics stand for strategies using readily accessible information to control problem-solving processes in man and machine. This book presents an analysis of the nature and the power of typical heuristic methods, primarily those used in artificial intelligence and operations research, to solve problems in areas such as reasoning, design, scheduling, planning, signal interpretation, symbolic computation, and combinatorial optimization. It is intended for advanced undergraduate or graduate students in artificial intelligence and for researchers in engineering, mathematics, and operations research.
Perspex machine II: visualization
NASA Astrophysics Data System (ADS)
Anderson, James A. D. W.
2005-01-01
We review the perspex machine and improve it by reducing its halting conditions to one condition. We also introduce a data structure, called the "access column," that can accelerate a wide class of perspex programs. We show how the perspex can be visualised as a tetrahedron, artificial neuron, computer program, and as a geometrical transformation. We discuss the temporal properties of the perspex machine, dissolve the famous time travel paradox, and present a hypothetical time machine. Finally, we discuss some mental properties and show how the perspex machine solves the mind-body problem and, specifically, how it provides one physical explanation for the occurrence of paradigm shifts.
Perspex machine II: visualization
NASA Astrophysics Data System (ADS)
Anderson, James A. D. W.
2004-12-01
We review the perspex machine and improve it by reducing its halting conditions to one condition. We also introduce a data structure, called the "access column," that can accelerate a wide class of perspex programs. We show how the perspex can be visualised as a tetrahedron, artificial neuron, computer program, and as a geometrical transformation. We discuss the temporal properties of the perspex machine, dissolve the famous time travel paradox, and present a hypothetical time machine. Finally, we discuss some mental properties and show how the perspex machine solves the mind-body problem and, specifically, how it provides one physical explanation for the occurrence of paradigm shifts.
Machine Shop Grinding Machines.
ERIC Educational Resources Information Center
Dunn, James
This curriculum manual is one in a series of machine shop curriculum manuals intended for use in full-time secondary and postsecondary classes, as well as part-time adult classes. The curriculum can also be adapted to open-entry, open-exit programs. Its purpose is to equip students with basic knowledge and skills that will enable them to enter the…
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.
A procedure for thinning the schedule of time-out.
Donaldson, Jeanne M; Vollmer, Timothy R
2012-01-01
Few studies have evaluated ways to thin punishment schedules. The purpose of this study was to examine the effects of using variable ratio (VR) schedules to thin the time-out schedule gradually. Warnings were used in some conditions to assist potentially with schedule thinning, but this analysis was limited. Participants were 3 young students who engaged in problem behavior during enriched time-in periods. Dense schedules of intermittent time-out were effective at reducing problem behavior.
Progress in Documentation: Machine Translation and Machine-Aided Translation.
ERIC Educational Resources Information Center
Hutchins, W. J.
1978-01-01
Discusses the prospects for fully automatic machine translation of good quality. Sections include history and background, operational and experimental machine translation systems of recent years, descriptions of interactive systems and machine-assisted translation, and a general survey of present problems and future possibilities. (VT)
Dawn Usage, Scheduling, and Governance Model
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; 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.
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.
ISAPS: Intelligent Scheduling And Planning System
King, M.S.; Rutherford, W.C.; Grice, J.V. . Kansas City Div.); Kessel, K.L.; Orel, M. )
1990-08-01
ISAPS is a scheduling and planning tool for shop floor personnel working in a Flexible Manufacturing System (FMS) environment. The ISAP system has two integrated components: the Predictive Scheduler (PS) and the Reactive Scheduler (RS). These components work cooperatively to satisfy the four goals of the ISAP system, which are: (G1) meet production due dates, (G2) maximize machining center utilization, (G3) minimize cutting tool migration, and (G4) minimize product flow time. The PS is used to establish schedules for new production requirements. The RS is used to adjust the schedules produced by the PS for unforeseen events that occur during production operations. The PS and RS subsystems have been developed using IntelliCorp's Knowledge Engineering Environment (KEE), an expert system development shell, and Common LISP. Software Quality Assurance (SQA) techniques have been incorporated throughout the development effort to assure the ISAP system meets the manufacturing goals and end user requirements. 5 refs., 4 figs.
Scheduling the Secondary School.
ERIC Educational Resources Information Center
Dempsey, Richard A.; Traverso, Henry P.
This "how-to-do-it" manual on the intricacies of school scheduling offers both technical information and common sense advice about the process of secondary school scheduling. The first of six chapters provides an overview of scheduling; chapter 2 examines specific considerations for scheduling; chapter 3 surveys the scheduling models and their…
Parallel job scheduling policies to improve fairness : a case study.
Leung, Vitus Joseph; Sabin, Gerald; Sadayappan, Ponnuswamy
2008-02-01
Balancing fairness, user performance, and system performance is a critical concern when developing and installing parallel schedulers. Sandia uses a customized scheduler to manage many of their parallel machines. A primary function of the scheduler is to ensure that the machines have good utilization and that users are treated in a 'fair' manner. A separate compute process allocator (CPA) ensures that the jobs on the machines are not too fragmented in order to maximize throughput. Until recently, there has been no established technique to measure the fairness of parallel job schedulers. This paper introduces a 'hybrid' fairness metric that is similar to recently proposed metrics. The metric uses the Sandia version of a 'fairshare' queuing priority as the basis for fairness. The hybrid fairness metric is used to evaluate a Sandia workload. Using these results, multiple scheduling strategies are introduced to improve performance while satisfying user and system performance constraints.
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
Successive overrelaxation for laplacian support vector machine.
Qi, Zhiquan; Tian, Yingjie; Shi, Yong
2015-04-01
Semisupervised learning (SSL) problem, which makes use of both a large amount of cheap unlabeled data and a few unlabeled data for training, in the last few years, has attracted amounts of attention in machine learning and data mining. Exploiting the manifold regularization (MR), Belkin et al. proposed a new semisupervised classification algorithm: Laplacian support vector machines (LapSVMs), and have shown the state-of-the-art performance in SSL field. To further improve the LapSVMs, we proposed a fast Laplacian SVM (FLapSVM) solver for classification. Compared with the standard LapSVM, our method has several improved advantages as follows: 1) FLapSVM does not need to deal with the extra matrix and burden the computations related to the variable switching, which make it more suitable for large scale problems; 2) FLapSVM’s dual problem has the same elegant formulation as that of standard SVMs. This means that the kernel trick can be applied directly into the optimization model; and 3) FLapSVM can be effectively solved by successive overrelaxation technology, which converges linearly to a solution and can process very large data sets that need not reside in memory. In practice, combining the strategies of random scheduling of subproblem and two stopping conditions, the computing speed of FLapSVM is rigidly quicker to that of LapSVM and it is a valid alternative to PLapSVM. PMID:25961091
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.
Scheduling Reconsidered (Again!)
ERIC Educational Resources Information Center
Hentschke, Guilbert C.; Fowler, William J.
1974-01-01
Computer technicians bring to school scheduling a certain naivete regarding the operation of schools. School administrators play a fundamental role of informing technicians about education scheduling needs. (Author)
Iterative refinement scheduling
NASA Technical Reports Server (NTRS)
Biefeld, Eric
1992-01-01
We present a heuristics-based approach to deep space mission scheduling which is modeled on the approach used by expert human schedulers in producing schedules for planetary encounters. New chronological evaluation techniques are used to focus the search by using information gained during the scheduling process to locate, classify, and resolve regions of conflict. Our approach is based on the assumption that during the construction of a schedule there exist several disjunct temporal regions where the demand for one resource type or a single temporal constraint dominates (bottleneck regions). If the scheduler can identify these regions and classify them based on their dominant constraint, then the scheduler can select the scheduling heuristic.
41 CFR 51-8.12 - Fee schedule.
Code of Federal Regulations, 2010 CFR
2010-07-01
... MATERIALS § 51-8.12 Fee schedule. (a) This schedule sets forth fees to be charged for processing requests... manual search time, or the first two minutes of computer search time, except on requests seeking... microfilm, audio-visual materials, or machine-readable documentation (i.e., magnetic tape or disk) shall...
Metalworking and machining fluids
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.
Linux Kernel Co-Scheduling For Bulk Synchronous Parallel Applications
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.
Intelligent perturbation algorithms to space scheduling optimization
NASA Technical Reports Server (NTRS)
Kurtzman, Clifford R.
1991-01-01
The limited availability and high cost of crew time and scarce resources make optimization of space operations critical. Advances in computer technology coupled with new iterative search techniques permit the near optimization of complex scheduling problems that were previously considered computationally intractable. Described here is a class of search techniques called Intelligent Perturbation Algorithms. Several scheduling systems which use these algorithms to optimize the scheduling of space crew, payload, and resource operations are also discussed.
Optimal randomized scheduling by replacement
Saias, I.
1996-05-01
In the replacement scheduling problem, a system is composed of n processors drawn from a pool of p. The processors can become faulty while in operation and faulty processors never recover. A report is issued whenever a fault occurs. This report states only the existence of a fault but does not indicate its location. Based on this report, the scheduler can reconfigure the system and choose another set of n processors. The system operates satisfactorily as long as, upon report of a fault, the scheduler chooses n non-faulty processors. We provide a randomized protocol maximizing the expected number of faults the system can sustain before the occurrence of a crash. The optimality of the protocol is established by considering a closely related dual optimization problem. The game-theoretic technical difficulties that we solve in this paper are very general and encountered whenever proving the optimality of a randomized algorithm in parallel and distributed computation.
Sort-Mid tasks scheduling algorithm in grid computing.
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. PMID:26644937
Sort-Mid tasks scheduling algorithm in grid computing.
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.
Sort-Mid tasks scheduling algorithm in grid computing
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
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.
An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints
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
Machine learning and radiology.
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.
Machine Learning and Radiology
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
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...
Process Development and Micro-Machining of MARBLE Foam-Cored Rexolite Hemi-Shell Ablator Capsules
Randolph, Randall Blaine; Oertel, John A.; Schmidt, Derek William; Lee, Matthew Nicholson; Patterson, Brian M.; Henderson, Kevin C.; Hamilton, Christopher Eric
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
NASA Astrophysics Data System (ADS)
Carvalho, Fernando D.; Sebastiao, Pedro; Henriques, Bernardo G.
1989-01-01
One of the problems of the production of cables is the measurement of the thickness plastic cover at the production line. If for some reason the thickness of the plastic is smaller than the minimum necessary several meters of cable may be lost. If the problem exists in the middle of a long cable and the default is not detected in time, the loss will be significant. To solve this problem it is possible to use automatic measuring machines which may detect a default as soon as it happens. It is also possible to interact with the production line in order to avoid any losses. In this paper it is presented a non contact measuring machine, developed for this purpose. The machine uses a laser which is scanned through a field of 80 mm. The interruption of the beam gives information about the external dimension of the object. The technical study of the resolution, sensitivity and precision are presented on the paper. Also the hardware solution and the software are presented. The machine has an interface which allows communication with a PC. The PC may receive information from several measuring units and to interact with machines installed at the production line. The prototype is finished and is going to be tested in the industry.
El-Refaie, Ayman Mohamed Fawzi; Reddy, Patel Bhageerath
2012-07-17
An interior permanent magnet electric machine is disclosed. The interior permanent magnet electric machine comprises a rotor comprising a plurality of radially placed magnets each having a proximal end and a distal end, wherein each magnet comprises a plurality of magnetic segments and at least one magnetic segment towards the distal end comprises a high resistivity magnetic material.
Ritson, D. )
1989-05-01
This talk examines methods available to minimize, but never entirely eliminate, degradation of machine performance caused by terrain following. Breaking of planar machine symmetry for engineering convenience and/or monetary savings must be balanced against small performance degradation, and can only be decided on a case-by-case basis. 5 refs.
Bhatia, Swapnil; LaBoda, Craig; Yanez, Vanessa; Haddock-Angelli, Traci; Densmore, Douglas
2016-08-19
We define a new inversion-based machine called a permuton of n genetic elements, which allows the n elements to be rearranged in any of the n·(n - 1)·(n - 2)···2 = n! distinct orderings. We present two design algorithms for architecting such a machine. We define a notion of a feasible design and use the framework to discuss the feasibility of the permuton architectures. We have implemented our design algorithms in a freely usable web-accessible software for exploration of these machines. Permutation machines could be used as memory elements or state machines and explicitly illustrate a rational approach to designing biological systems.
Bhatia, Swapnil; LaBoda, Craig; Yanez, Vanessa; Haddock-Angelli, Traci; Densmore, Douglas
2016-08-19
We define a new inversion-based machine called a permuton of n genetic elements, which allows the n elements to be rearranged in any of the n·(n - 1)·(n - 2)···2 = n! distinct orderings. We present two design algorithms for architecting such a machine. We define a notion of a feasible design and use the framework to discuss the feasibility of the permuton architectures. We have implemented our design algorithms in a freely usable web-accessible software for exploration of these machines. Permutation machines could be used as memory elements or state machines and explicitly illustrate a rational approach to designing biological systems. PMID:27383067
[Fee schedules and cost containment].
Herrmann, François R; Perneger, Thomas
2009-11-11
Medical fee schedules are controversial. In this paper we examine the reasons that justify the imposition of fee schedules in the presence of a socially financed health insurance system, and examine the ways of constructing a medical fee schedule. The weakness of fee-for-service tariffs is that they do not allow a control of health care costs if the volume of services is unchecked. Current solutions to this problem--audit of doctors' average cost per case, freeze on new medical practices, or the insurers' discretion in choosing the doctors they reimburse--have multiple drawbacks. Alternatives to fee-for-service payment--such as flat fees, or payment based on the quality of medical services--are discussed.
A task scheduler framework for self-powered wireless sensors.
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. PMID:14582879
Batch Scheduling a Fresh Approach
NASA Technical Reports Server (NTRS)
Cardo, Nicholas P.; Woodrow, Thomas (Technical Monitor)
1994-01-01
The Network Queueing System (NQS) was designed to schedule jobs based on limits within queues. As systems obtain more memory, the number of queues increased to take advantage of the added memory resource. The problem now becomes too many queues. Having a large number of queues provides users with the capability to gain an unfair advantage over other users by tailoring their job to fit in an empty queue. Additionally, the large number of queues becomes confusing to the user community. The High Speed Processors group at the Numerical Aerodynamics Simulation (NAS) Facility at NASA Ames Research Center developed a new approach to batch job scheduling. This new method reduces the number of queues required by eliminating the need for queues based on resource limits. The scheduler examines each request for necessary resources before initiating the job. Also additional user limits at the complex level were added to provide a fairness to all users. Additional tools which include user job reordering are under development to work with the new scheduler. This paper discusses the objectives, design and implementation results of this new scheduler
Dedicated heterogeneous node scheduling including backfill scheduling
Wood, Robert R.; Eckert, Philip D.; Hommes, Gregg
2006-07-25
A method and system for job backfill scheduling dedicated heterogeneous nodes in a multi-node computing environment. Heterogeneous nodes are grouped into homogeneous node sub-pools. For each sub-pool, a free node schedule (FNS) is created so that the number of to chart the free nodes over time. For each prioritized job, using the FNS of sub-pools having nodes useable by a particular job, to determine the earliest time range (ETR) capable of running the job. Once determined for a particular job, scheduling the job to run in that ETR. If the ETR determined for a lower priority job (LPJ) has a start time earlier than a higher priority job (HPJ), then the LPJ is scheduled in that ETR if it would not disturb the anticipated start times of any HPJ previously scheduled for a future time. Thus, efficient utilization and throughput of such computing environments may be increased by utilizing resources otherwise remaining idle.
TEACHING MACHINES AND PROGRAMED INSTRUCTION.
ERIC Educational Resources Information Center
JOHNSON, HERBERT; AND OTHERS
THE TEACHING MACHINE AND PROGRAMED INSTRUCTION ARE EXPLAINED ANALYTICALLY IN TERMS OF LABORATORY PROCEDURES. AN EXPLANATION IS GIVEN OF THE PSYCHOLOGY OF THE STUDENT. TEACHING MACHINES ALLEVIATE SOME PROBLEMS OF STUDENT-TEACHER RELATIONSHIPS, SUCH AS APATHY, STUBBORNNESS, AND RESENTMENT. HIGHER LEVELS OF CONCENTRATION ARE MAINTAINED. SOME…
Noncontingent Reinforcement: A Further Examination of Schedule Effects during Treatment
ERIC Educational Resources Information Center
Wallace, Michelle D.; Iwata, Brian A.; Hanley, Gregory P.; Thompson, Rachel H.; Roscoe, Eileen M.
2012-01-01
We conducted 2 studies to determine whether dense and thin NCR schedules exert different influences over behavior and whether these influences change as dense schedules are thinned. In Study 1, we observed that thin as well as dense NCR schedules effectively decreased problem behavior exhibited by 3 individuals. In Study 2, we compared the effects…
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.
49 CFR 214.533 - Schedule of repairs subject to availability of parts.
Code of Federal Regulations, 2013 CFR
2013-10-01
... 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....
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... 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....
49 CFR 214.533 - Schedule of repairs subject to availability of parts.
Code of Federal Regulations, 2014 CFR
2014-10-01
... 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....
49 CFR 214.533 - Schedule of repairs subject to availability of parts.
Code of Federal Regulations, 2012 CFR
2012-10-01
... 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....
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....
Chemical Kiloton Experiment schedules
NASA Astrophysics Data System (ADS)
The Lawrence Livermore National Laboratory will conduct a large chemical explosion (CE) called the Chemical Kiloton Experiment (CKE) in the Rainier Mesa area of the Nevada Test site. The explosion will involve a 30/70 Emulsion-to-ANFO blend of 1,147,000 kg to supply 1 kt, and is scheduled for January 29. It will be heavily instrumented with close-in, free-field surface seismic and regional seismic measurements. The CKE is located near several DNA-sponsored nuclear explosions (NEs) and will provide a unique opportunity for fundamental studies on explosion phenomenology (for example, CE/NE equivalence), scaling with CEs and NEs, and integration of multiple monitoring methods. This experiment will also address some critical proliferation monitoring problems such as CE masking of NEs, CEs as false alarms, CEs for regional calibration, and on-site inspection.
Parallel-batch scheduling and transportation coordination with waiting time constraint.
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.
Immunization Schedules for Adults
... ACIP Vaccination Recommendations Why Immunize? Vaccines: The Basics Immunization Schedules for Adults in Easy-to-read Formats ... previous immunizations. View or Print a Schedule Recommended Immunizations for Adults (19 Years and Older) by Age ...
Childhood Immunization Schedule
... Recommendations Why Immunize? Vaccines: The Basics Instant Childhood Immunization Schedule Recommend on Facebook Tweet Share Compartir Get ... date. See Disclaimer for additional details. Based on Immunization Schedule for Children 0 through 6 Years of ...
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.
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.
Job scheduling in a heterogenous grid environment
Oliker, Leonid; Biswas, Rupak; Shan, Hongzhang; Smith, Warren
2004-02-11
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.
Block Scheduling. Research Brief
ERIC Educational Resources Information Center
Muir, Mike
2003-01-01
What are the effects of block scheduling? Results of transitioning from traditional to block scheduling are mixed. Some studies indicate no change in achievement results, nor change in teachers' opinions about instructional strategies. Other studies show that block scheduling doesn't work well for Advanced Placement or Music courses, that "hard to…
NASA Technical Reports Server (NTRS)
1983-01-01
Castle Industries, Inc. is a small machine shop manufacturing replacement plumbing repair parts, such as faucet, tub and ballcock seats. Therese Castley, president of Castle decided to introduce Monel because it offered a chance to improve competitiveness and expand the product line. Before expanding, Castley sought NERAC assistance on Monel technology. NERAC (New England Research Application Center) provided an information package which proved very helpful. The NASA database was included in NERAC's search and yielded a wealth of information on machining Monel.
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.
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…
Goodman, G.V.R.
1987-01-01
The lack of available techniques prompted the development of a mixed integer model to optimize the scheduling of equipment and the distribution of overburden in a typical mountaintop removal operation. Using this format, a (0-1) integer model and transportation model were constructed to determine the optimal equipment schedule and optimal overburden distribution, respectively. To solve this mixed integer program, the model was partitioned into its binary and real-valued components. Each problem was successively solved and their values added to form estimates of the value of the mixed integer program. Optimal convergence was indicated when the difference between two successive estimates satisfied some pre-specific accuracy value. The performance of the mixed integer model was tested against actual field data to determine its practical applications. To provide the necessary input information, production data was obtained from a single seam, mountaintop removal operation located in the Appalachian coal field. As a means of analyzing the resultant equipment schedule, the total idle time was calculated for each machine type and each lift location. Also, the final overburden assignments were analyzed by determining the distribution of spoil material for various overburden removal productivities. Subsequent validation of the mixed integer model was conducted in two distinct areas. The first dealt with changes in algorithmic data and their effects on the optimality of the model. The second area concerned variations in problem structure, specifically those dealing with changes in problem size and other user-inputed values such as equipment productivities or required reclamation.
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.
NASA Technical Reports Server (NTRS)
Clement, Bradley; Johnston, Mark; Wax, Allan; Chouinard, Caroline
2008-01-01
The DSN (Deep Space Network) Scheduling Engine targets all space missions that use DSN services. It allows clients to issue scheduling, conflict identification, conflict resolution, and status requests in XML over a Java Message Service interface. The scheduling requests may include new requirements that represent a set of tracks to be scheduled under some constraints. This program uses a heuristic local search to schedule a variety of schedule requirements, and is being infused into the Service Scheduling Assembly, a mixed-initiative scheduling application. The engine resolves conflicting schedules of resource allocation according to a range of existing and possible requirement specifications, including optional antennas; start of track and track duration ranges; periodic tracks; locks on track start, duration, and allocated antenna; MSPA (multiple spacecraft per aperture); arraying/VLBI (very long baseline interferometry)/delta DOR (differential one-way ranging); continuous tracks; segmented tracks; gap-to-track ratio; and override or block-out of requirements. The scheduling models now include conflict identification for SOA(start of activity), BOT (beginning of track), RFI (radio frequency interference), and equipment constraints. This software will search through all possible allocations while providing a best-effort solution at any time. The engine reschedules to accommodate individual emergency tracks in 0.2 second, and emergency antenna downtime in 0.2 second. The software handles doubling of one mission's track requests over one week (to 42 total) in 2.7 seconds. Further tests will be performed in the context of actual schedules.
NASA Technical Reports Server (NTRS)
Adair, Jerry R.
1994-01-01
This paper is a consolidated report on ten major planning and scheduling systems that have been developed by the National Aeronautics and Space Administration (NASA). A description of each system, its components, and how it could be potentially used in private industry is provided in this paper. The planning and scheduling technology represented by the systems ranges from activity based scheduling employing artificial intelligence (AI) techniques to constraint based, iterative repair scheduling. The space related application domains in which the systems have been deployed vary from Space Shuttle monitoring during launch countdown to long term Hubble Space Telescope (HST) scheduling. This paper also describes any correlation that may exist between the work done on different planning and scheduling systems. Finally, this paper documents the lessons learned from the work and research performed in planning and scheduling technology and describes the areas where future work will be conducted.
Integrated online job-shop scheduling system
NASA Astrophysics Data System (ADS)
Zhao, Xing; Chen, Kuan H.; Luh, Peter B.; Chiueh, T. D.; Chang, ShihChang; Thakur, Lakshman S.
1999-11-01
The rapid development of information technology and e- commerce requires fast response form scheduling systems. Based on the Lagrangian relaxation approach for job shop scheduling, this paper present an integrated system that will generate schedules quickly. The Lagrangian relaxation approach is an iterative optimization process, where dynamic programming is solved in each iteration. Since dynamic programming is computational expensive especially for large problems, this paper develops the simplified dynamic programming, which will cut the computation time of each iteration by one order. Furthermore, a digital circuit to be embedded in PC is designed to implement the iterative optimization algorithm, leading to another order of speed improvement. The resulting integrated scheduling system consists of the hardware for optimization and the related software. It is estimated that two orders of magnitude gain in speed can be obtained, which will make on-line scheduling for practical job shops possible.
Debugging Fortran on a shared memory machine
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.
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.
Advances of implementing NC machine tools discussed
NASA Astrophysics Data System (ADS)
Kukuyev, Y. P.; Trukhan, Y. V.
1984-11-01
Numerical control machine tools which are one of the principal resources of reequipment, mechanization and automation of small series and series production in machine building were examined. The continually increasing volume of NC machine tools which are produced and introduced is economically significant for introduction of these machine tools to operation and organization of their effective use. Organizational and technical measures were directed at solving these problems. To insure the fastest introduction of NC machine tools into operation and their technical maintenance, a number of setting up organizations was organized. Setting up services are also provided by the plants manufacturing the NC machine tools, and appropriate subdivisions are created for this purpose.
Distributed job scheduling in MetaCentrum
NASA Astrophysics Data System (ADS)
Tóth, Šimon; Ruda, Miroslav
2015-05-01
MetaCentrum - The Czech National Grid provides access to various resources across the Czech Republic. The utilized resource management and scheduling system is based on a heavily modified version of the Torque Batch System. This open source resource manager is maintained in a local fork and was extended to facilitate the requirements of such a large installation. This paper provides an overview of unique features deployed in MetaCentrum. Notably, we describe our distributed setup that encompasses several standalone independent servers while still maintaining full cooperative scheduling across the grid. We also present the benefits of our virtualized infrastructure that enables our schedulers to dynamically request ondemand virtual machines, that are then used to facilitate the varied requirements of users in our system, as well as enabling support for user requested virtual clusters that can be further interconnected using a private VLAN.
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.
NASA Astrophysics Data System (ADS)
Muscettola, Nicola; Smith, Steven S.
1996-09-01
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.
A scheduling model for astronomy
NASA Astrophysics Data System (ADS)
Solar, M.; Michelon, P.; Avarias, J.; Garces, M.
2016-04-01
Astronomical scheduling problem has several external conditions that change dynamically at any time during observations, like weather condition (humidity, temperature, wind speed, opacity, etc.), and target visibility conditions (target over the horizon, Sun/Moon blocking the target). Therefore, a dynamic re-scheduling is needed. An astronomical project will be scheduled as one or more Scheduling Blocks (SBs) as an atomic unit of astronomical observations. We propose a mixed integer linear programming (MILP) solution to select the best SBs, favors SBs with high scientific values, and thus maximizing the quantity of completed observation projects. The data content of Atacama Large Millimeter/Submillimeter Array (ALMA) projects of cycle 0 and cycle 1 were analyzed, and a synthetic set of tests of the real instances was created. Two configurations, one of 5000 SBs in a 3 months season and another 10,000 SBs a 6 months season were created. These instances were evaluated with excellent results. Through the testing it is showed that the MILP proposal has optimal solutions.
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.
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.
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.
NASA Technical Reports Server (NTRS)
1995-01-01
The Orbotron is a tri-axle exercise machine patterned after a NASA training simulator for astronaut orientation in the microgravity of space. It has three orbiting rings corresponding to roll, pitch and yaw. The user is in the middle of the inner ring with the stomach remaining in the center of all axes, eliminating dizziness. Human power starts the rings spinning, unlike the NASA air-powered system. Marketed by Fantasy Factory (formerly Orbotron, Inc.), the machine can improve aerobic capacity, strength and endurance in five to seven minute workouts.
Schedule Uncertainty Control: A Literature Review
NASA Astrophysics Data System (ADS)
Jun-yan, Liu
Risk control on project schedule is one of the focus problems in the academic circle and the practical area all the time. Lots of research about risk control on project schedule have been fulfilled and many achievements have appeared in recent several decades. The literature on the techniques of schedule uncertainty control was reviewed. A summary analysis on those chievements is presented such as CPM, PERT, MC, BBN, and so on and in light of that summary analysis a deep discussion in terms of advantages and disadvantages of existing research has been analyzed, so that researchers can continue to refine their research.
Intelligent scheduling support for the US Coast Guard
Darby-Dowman, K.; Lucas, C.; Mitra, G.; Fink, R.; Kingsley, L.; Smith, J.W.
1992-12-31
This paper will discuss a joint effort by the U.S. Coast Guard Research & Development Center, Idaho National Engineering Laboratory and Brunel University to provide the necessary tools to increase the human scheduler`s capability to handle the scheduling process more efficiently and effectively. Automating the scheduling process required a system that could think independently of the scheduler, that is, the systems needed its own control mechanism and knowledge base. Further, automated schedule generation became a design requirement and sophisticated algorithms were formulated to solve a complex combinatorial problem. In short, the resulting design can be viewed as a hybrid knowledge-based mathematical programming application system. This document contains an overview of the integrated system, a discrete optimization model for scheduling, and schedule diagnosis and analysis.
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
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.
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
Linux Kernel Co-Scheduling and Bulk Synchronous Parallelism
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.
Hagopian, Louis P; Boelter, Eric W; Jarmolowicz, David P
2011-01-01
This paper extends the Tiger, Hanley, and Bruzek (2008) review of functional communication training (FCT) by reviewing the published literature on reinforcement schedule thinning following FCT. As noted by Tiger et al. and others, schedule thinning may be necessary when the newly acquired communication response occurs excessively, to the extent that reinforcing it consistently is not practical in the natural environment. We provide a review of this literature including a discussion of each of the more commonly used schedule arrangements used for this purpose, outcomes obtained, a description of methods for progressing toward the terminal schedule, and a description of supplemental treatment components aimed at maintaining low levels of problem behavior during schedule thinning. Recommendations for schedule thinning are then provided. Finally, conceptual issues related to the reemergence of problem behavior during schedule thinning and areas for future research are discussed. PMID:22532899
NASA Technical Reports Server (NTRS)
Halbfinger, Eliezer M.; Smith, Barry D.
1991-01-01
The Air Force Space Command schedules telemetry, tracking and control activities across the Air Force Satellite Control network. The Range Scheduling Aid (RSA) is a rapid prototype combining a user-friendly, portable, graphical interface with a sophisticated object-oriented database. The RSA has been a rapid prototyping effort whose purpose is to elucidate and define suitable technology for enhancing the performance of the range schedulers. Designing a system to assist schedulers in their task and using their current techniques as well as enhancements enabled by an electronic environment, has created a continuously developing model that will serve as a standard for future range scheduling systems. The RSA system is easy to use, easily ported between platforms, fast, and provides a set of tools for the scheduler that substantially increases his productivity.
ERIC Educational Resources Information Center
Fendrich, Jean
2002-01-01
Collectors everywhere know that local antique shops and flea markets are treasure troves just waiting to be plundered. Science teachers might take a hint from these hobbyists, for the next community yard sale might be a repository of old, quirky items that are just the things to get students thinking about simple machines. By introducing some…
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
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.
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.
Mixed-Integer Formulations for Constellation Scheduling
NASA Astrophysics Data System (ADS)
Valicka, C.; Hart, W.; Rintoul, M.
Remote sensing systems have expanded the set of capabilities available for and critical to national security. Cooperating, high-fidelity sensing systems and growing mission applications have exponentially increased the set of potential schedules. A definitive lack of advanced tools places an increased burden on operators, as planning and scheduling remain largely manual tasks. This is particularly true in time-critical planning activities where operators aim to accomplish a large number of missions through optimal utilization of single or multiple sensor systems. Automated scheduling through identification and comparison of alternative schedules remains a challenging problem applicable across all remote sensing systems. Previous approaches focused on a subset of sensor missions and do not consider ad-hoc tasking. We have begun development of a robust framework that leverages the Pyomo optimization modeling language for the design of a tool to assist sensor operators planning under the constraints of multiple concurrent missions and uncertainty. Our scheduling models have been formulated to address the stochastic nature of ad-hoc tasks inserted under a variety of scenarios. Operator experience is being leveraged to select appropriate model objectives. Successful development of the framework will include iterative development of high-fidelity mission models that consider and expose various schedule performance metrics. Creating this tool will aid time-critical scheduling by increasing planning efficiency, clarifying the value of alternative modalities uniquely provided by multi-sensor systems, and by presenting both sets of organized information to operators. Such a tool will help operators more quickly and fully utilize sensing systems, a high interest objective within the current remote sensing operations community. Preliminary results for mixed-integer programming formulations of a sensor scheduling problem will be presented. Assumptions regarding sensor geometry
Hybrid Scheduling Model for Independent Grid Tasks
Shanthini, J.; Kalaikumaran, T.; Karthik, S.
2015-01-01
Grid computing facilitates the resource sharing through the administrative domains which are geographically distributed. Scheduling in a distributed heterogeneous environment is intrinsically very hard because of the heterogeneous nature of resource collection. Makespan and tardiness are two different measures of scheduling, and many of the previous researches concentrated much on reduction of makespan, which measures the machine utilization. In this paper, we propose a hybrid scheduling algorithm for scheduling independent grid tasks with the objective of reducing total weighted tardiness of grid tasks. Tardiness is to measure the due date performance, which has a direct impact on cost for executing the jobs. In this paper we propose BG_ATC algorithm which is a combination of best gap (BG) search and Apparent Tardiness Cost (ATC) indexing algorithm. Furthermore, we implemented these two algorithms in two different phases of the scheduling process. In addition to that, the comparison was made on results with various benchmark algorithms and the experimental results show that our algorithm outperforms the benchmark algorithms. PMID:26543897
Hybrid Scheduling Model for Independent Grid Tasks.
Shanthini, J; Kalaikumaran, T; Karthik, S
2015-01-01
Grid computing facilitates the resource sharing through the administrative domains which are geographically distributed. Scheduling in a distributed heterogeneous environment is intrinsically very hard because of the heterogeneous nature of resource collection. Makespan and tardiness are two different measures of scheduling, and many of the previous researches concentrated much on reduction of makespan, which measures the machine utilization. In this paper, we propose a hybrid scheduling algorithm for scheduling independent grid tasks with the objective of reducing total weighted tardiness of grid tasks. Tardiness is to measure the due date performance, which has a direct impact on cost for executing the jobs. In this paper we propose BG_ATC algorithm which is a combination of best gap (BG) search and Apparent Tardiness Cost (ATC) indexing algorithm. Furthermore, we implemented these two algorithms in two different phases of the scheduling process. In addition to that, the comparison was made on results with various benchmark algorithms and the experimental results show that our algorithm outperforms the benchmark algorithms. PMID:26543897
Hybrid Scheduling Model for Independent Grid Tasks.
Shanthini, J; Kalaikumaran, T; Karthik, S
2015-01-01
Grid computing facilitates the resource sharing through the administrative domains which are geographically distributed. Scheduling in a distributed heterogeneous environment is intrinsically very hard because of the heterogeneous nature of resource collection. Makespan and tardiness are two different measures of scheduling, and many of the previous researches concentrated much on reduction of makespan, which measures the machine utilization. In this paper, we propose a hybrid scheduling algorithm for scheduling independent grid tasks with the objective of reducing total weighted tardiness of grid tasks. Tardiness is to measure the due date performance, which has a direct impact on cost for executing the jobs. In this paper we propose BG_ATC algorithm which is a combination of best gap (BG) search and Apparent Tardiness Cost (ATC) indexing algorithm. Furthermore, we implemented these two algorithms in two different phases of the scheduling process. In addition to that, the comparison was made on results with various benchmark algorithms and the experimental results show that our algorithm outperforms the benchmark algorithms.
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.
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.
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.
NASA Technical Reports Server (NTRS)
1981-01-01
Manufacturer of the Model 2210 copying machine was looking for a plastic valve bushing material that could be produced by a low-cost injection molding process to replace the unsuitable valve bushing they were using. NERAC conducted a computer search of the NASA database and was able to supply Nashua Corporation with several technical reports in their area of interest. Information aided the company's development of a urethane valve bushing which solved the problem and created a dramatic reduction in unit cost.
Introducing Machine Learning Concepts with WEKA.
Smith, Tony C; Frank, Eibe
2016-01-01
This chapter presents an introduction to data mining with machine learning. It gives an overview of various types of machine learning, along with some examples. It explains how to download, install, and run the WEKA data mining toolkit on a simple data set, then proceeds to explain how one might approach a bioinformatics problem. Finally, it includes a brief summary of machine learning algorithms for other types of data mining problems, and provides suggestions about where to find additional information.
NASA Astrophysics Data System (ADS)
Prasetyaningsih, E.; Suprayogi; Samadhi, TMAA; Halim, AH
2016-02-01
This paper studies production and delivery batch scheduling problems for a single- supplier-to-a-single-manufacturer case, with multiple capacitated vehicles wherein different holding costs between in-process and completed parts are allowed. In the problem, the parts of a single item are first batched,then the resulting batches are processed on a single machine. All completed batches are transported in a number of deliveries in order to be received at a common due date. The objective is to find the integrated schedule of production and delivery batches so as to satisfy its due date and to minimize the total cost of associated in-process parts inventory, completed parts inventory and delivery. It should be noted that both holding costs constitute a derivation of the so-called actual flow time, and the delivery cost is proportional to the required number of deliveries. The problem can be formulated as an integer non-linier programming and it is solved optimally by Lingo 11.0 software. Numerical experiences show that there are two patterns of batch sizes affected by the ratio of holding costs of in-process and completed parts. It can be used by practitioners to solve the realistic integrated production and delivery batch scheduling problem.
SOFIA's Choice: Scheduling Observations for an Airborne Observatory
NASA Technical Reports Server (NTRS)
Frank, Jeremy; Kurklu, Elif; Koga, Dennis (Technical Monitor)
2002-01-01
We describe the problem of scheduling observations for an airborne observatory. The problem is more complex than traditional scheduling problems in that it incorporates complex constraints relating the feasibility of an astronomical observation to the position and time of a mobile observatory, as well as traditional temporal constraints and optimization criteria. We describe the problem, its proposed solution and the empirical validation of that solution.
Transportation Baseline Schedule
Fawcett, Ricky Lee; John, Mark Earl
2000-01-01
The “1999 National Transportation Program - Transportation Baseline Report” presents data that form a baseline to enable analysis and planning for future Department of Energy (DOE) Environmental Management (EM) waste/material transportation. The companion “1999 Transportation ‘Barriers’ Analysis” analyzes the data and identifies existing and potential problems that may prevent or delay transportation activities based on the data presented. The “1999 Transportation Baseline Schedule” (this report) uses the same data to provide an overview of the transportation activities of DOE EM waste/materials. This report can be used to identify areas where stakeholder interface is needed, and to communicate to stakeholders the quantity/schedule of shipments going through their area. Potential bottlenecks in the transportation system can be identified; the number of packages needed, and the capacity needed at receiving facilities can be planned. This report offers a visualization of baseline DOE EM transportation activities for the 11 major sites and the “Geologic Repository Disposal” site (GRD).
Job Scheduling with Efficient Resource Monitoring in Cloud Datacenter.
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
Job Scheduling with Efficient Resource Monitoring in Cloud Datacenter
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
Job Scheduling with Efficient Resource Monitoring in Cloud Datacenter.
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.
ERIC Educational Resources Information Center
Williamson, Ronald
2010-01-01
Driven by stable or declining financial resources many school districts are considering the costs and benefits of a seven-period day. While there is limited evidence that any particular scheduling model has a greater impact on student learning than any other, it is clear that the school schedule is a tool that can significantly impact teacher…
ERIC Educational Resources Information Center
Ubben, Gerald C.
1976-01-01
Achieving flexibility without losing student accountability is a challenge that faces every school. With a fluid block schedule, as described here, accountability is maintained without inhibiting flexibility. An additional advantage is that three levels of schedule decision making take some of the pressure off the principal. (Editor)
ERIC Educational Resources Information Center
Haley, Marjorie
A discussion of block scheduling for second language instruction looks at the advantages and disadvantages and offers some suggestions for classroom management and course organization. It is argued that block scheduling may offer a potential solution to large classes, insufficient time for labs, too little individualized instruction; few…
Fundamentals of School Scheduling.
ERIC Educational Resources Information Center
Schroth, Gwen
The ability of the school administrator to schedule teachers' and students' time so that each receives the most from each school day has become an essential skill. This book has been prepared for school administrators at the elementary and middle school levels who need appropriate management techniques for scheduling students into classes. Chapter…
NASA Technical Reports Server (NTRS)
Logan, J. R.; Pulvermacher, M. K.
1991-01-01
Range Scheduling Aid (RSA) is presented in the form of the viewgraphs. The following subject areas are covered: satellite control network; current and new approaches to range scheduling; MITRE tasking; RSA features; RSA display; constraint based analytic capability; RSA architecture; and RSA benefits.
What's behind Block Scheduling?
ERIC Educational Resources Information Center
Gierke, Carolyn
1999-01-01
Discussion of block scheduling in secondary schools focuses on its impact on the school library media center. Discusses increased demand for library services, scheduling classes, the impact on librarians' time, teaching information technology, local area networks, and the increased pace of activity. (LRW)
Medlin, John B.
1976-05-25
A charging machine for loading fuel slugs into the process tubes of a nuclear reactor includes a tubular housing connected to the process tube, a charging trough connected to the other end of the tubular housing, a device for loading the charging trough with a group of fuel slugs, means for equalizing the coolant pressure in the charging trough with the pressure in the process tubes, means for pushing the group of fuel slugs into the process tube and a latch and a seal engaging the last object in the group of fuel slugs to prevent the fuel slugs from being ejected from the process tube when the pusher is removed and to prevent pressure liquid from entering the charging machine.
NASA Technical Reports Server (NTRS)
Globus, Al; Saini, Subhash
1998-01-01
Recent computational efforts at NASA Ames Research Center and computation and experiment elsewhere suggest that a nanotechnology of machine phase functionalized fullerenes may be synthetically accessible and of great interest. We have computationally demonstrated that molecular gears fashioned from (14,0) single-walled carbon nanotubes and benzyne teeth should operate well at 50-100 gigahertz. Preliminary results suggest that these gears can be cooled by a helium atmosphere and a laser motor can power fullerene gears if a positive and negative charge have been added to form a dipole. In addition, we have unproven concepts based on experimental and computational evidence for support structures, computer control, a system architecture, a variety of components, and manufacture. Combining fullerene machines with the remarkable mechanical properties of carbon nanotubes, there is some reason to believe that a focused effort to develop fullerene nanotechnology could yield materials with tremendous properties.
Owen, Whitney H.
1980-01-01
A polyphase rotary induction machine for use as a motor or generator utilizing a single rotor assembly having two series connected sets of rotor windings, a first stator winding disposed around the first rotor winding and means for controlling the current induced in one set of the rotor windings compared to the current induced in the other set of the rotor windings. The rotor windings may be wound rotor windings or squirrel cage windings.
NASA Technical Reports Server (NTRS)
Wang, Yeou-Fang; Baldwin, John
2007-01-01
TIGRAS is client-side software, which provides tracking-station equipment planning, allocation, and scheduling services to the DSMS (Deep Space Mission System). TIGRAS provides functions for schedulers to coordinate the DSN (Deep Space Network) antenna usage time and to resolve the resource usage conflicts among tracking passes, antenna calibrations, maintenance, and system testing activities. TIGRAS provides a fully integrated multi-pane graphical user interface for all scheduling operations. This is a great improvement over the legacy VAX VMS command line user interface. TIGRAS has the capability to handle all DSN resource scheduling aspects from long-range to real time. TIGRAS assists NASA mission operations for DSN tracking of station equipment resource request processes from long-range load forecasts (ten years or longer), to midrange, short-range, and real-time (less than one week) emergency tracking plan changes. TIGRAS can be operated by NASA mission operations worldwide to make schedule requests for the DSN station equipment.
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.
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.
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.
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.
Intelligent perturbation algorithms for space scheduling optimization
NASA Technical Reports Server (NTRS)
Kurtzman, Clifford R.
1990-01-01
The optimization of space operations is examined in the light of optimization heuristics for computer algorithms and iterative search techniques. Specific attention is given to the search concepts known collectively as intelligent perturbation algorithms (IPAs) and their application to crew/resource allocation problems. IPAs iteratively examine successive schedules which become progressively more efficient, and the characteristics of good perturbation operators are listed. IPAs can be applied to aerospace systems to efficiently utilize crews, payloads, and resources in the context of systems such as Space-Station scheduling. A program is presented called the MFIVE Space Station Scheduling Worksheet which generates task assignments and resource usage structures. The IPAs can be used to develop flexible manifesting and scheduling for the Industrial Space Facility.
Monitoring Building Systems for Schedule Compliance
Jensen, Andrew M.; Belew, Shan T.
2013-02-19
As Pacific Northwest National Laboratory (PNNL) initiated a Core Business Hours program, it became a challenge to ensure that the hundreds of systems campus wide were operating within their programmed schedules. Therefore, a collaborative exchange between PNNL operations and PNNL researchers developing the Decision Support for Operations and Maintenance (DSOM) software package was initiated to create a tool to solve this problem. This new DSOM tool verifies systems are operating within scheduled operation times by polling Building Automation and Control Network (BACnet) identifiers of systems’ on/off or command statuses. The tool records the time spent in operation state (ON) and totalizes each system over a rolling 7-day period, highlighting systems that are running over the scheduled hours. This snapshot view allows building management to look quickly at the entire campus to ensure that systems are not operating beyond their scheduled hours.
A synthesized heuristic task scheduling algorithm.
Dai, Yanyan; Zhang, Xiangli
2014-01-01
Aiming at the static task scheduling problems in heterogeneous environment, a heuristic task scheduling algorithm named HCPPEFT is proposed. In task prioritizing phase, there are three levels of priority in the algorithm to choose task. First, the critical tasks have the highest priority, secondly the tasks with longer path to exit task will be selected, and then algorithm will choose tasks with less predecessors to schedule. In resource selection phase, the algorithm is selected task duplication to reduce the interresource communication cost, besides forecasting the impact of an assignment for all children of the current task permits better decisions to be made in selecting resources. The algorithm proposed is compared with STDH, PEFT, and HEFT algorithms through randomly generated graphs and sets of task graphs. The experimental results show that the new algorithm can achieve better scheduling performance.
Uncertainty Reasoning in Astronomy--Applications to Classification and Optimal Telescope Scheduling
NASA Astrophysics Data System (ADS)
Adorf, Hans-Martin
1995-06-01
be used to efficiently solve the underlying constraint satisfaction problem (CSP). The chapter "The processing of HST observing programmes" introduces the HST long range planning problem and explains why the scheduling of pointed observations with this astronomical satellite in a low Earth orbit is difficult. The chapter "Connectionism and neural networks" provides some conceptual background on artificial neural networks. It is shown how a simple scheduling problem can be mapped onto the topology of an artificial neural network. Adding a suitable network dynamics turns such a network into an efficient problem solver. The chapter "A discrete stochastic neural network algorithm for constraint satisfaction problems" describes the novel "guarded discrete stochastic" (GDS) network algorithm, a relative of the standard Hopfield-Tank network, which is surprisingly efficient in finding solutions to large constraint satisfaction problems even on serial machines. The chapter "Scheduling with neural networks --- The case of Hubble Space Telescope" finally describes how the GDS network is integrated into Spike, the scheduling system developed at the Space Telescope Science Institute, Baltimore, for creating long range HST observing schedules. The methodology described here is quite general and is applicable to scheduling other ground-based or space-born astronomical observatories. (SECTION: Dissertation Summaries)
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.
Scheduling Projects with Multiskill Learning Effect
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
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.
Features of plastics edge cutting machining
NASA Astrophysics Data System (ADS)
Handozhko, A. V.; Shcherbakov, A. N.; Zaharov, L. A.; Gavrilenko, T. V.
2016-04-01
This article describes the features of pieces from thermoplastic materials in the form of electrical insulators cut by a disk edge tool. The problems in question are possible defects arising during machining and technological conditions that reduce their quantity. The necessity of required machining conditions matching substantiated in accordance with a specific grade of the material which is treated. Equipment and machining attachments, developed for experimental studies, determine the rational conditions of plastic electrical insulators machining. As a result of experiments the dependences of cut face quality parameters of plastics are obtained by machining conditions. The obtained results allowed us to make valid conclusions and recommendations.
Computerizing the Reference Desk Schedule.
ERIC Educational Resources Information Center
deHaas, Pat
1983-01-01
Discussion of the scheduling procedures of librarians' hours at the reference desk at the Rutherford Humanities and Social Sciences Library, University of Alberta, highlights services provided, the preference table system, and manual scheduling versus computer scheduling. (EJS)
Chen, B; Hickling, T; Krnjajic, M; Hanley, W; Clark, G; Nitao, J; Knapp, D; Hiller, L; Mugge, M
2007-01-09
In this project, the basic problem is to automatically separate test samples into one of two categories: clean or corrupt. This type of classification problem is known as a two-class classification problem or detection problem. In what follows, we refer to clean examples as negative examples and corrupt examples as positive examples. In a detection problem, a classifier decision on any one sample can be grouped into one of four decision categories: true negative, true positive, false negative and false positive. These four categories are illustrated by Table 1. True negatives and true positives are cases where the classifier has made the correct decision. False positives are cases where the classifier decides positive when the true nature of the sample was negative, and false negatives are cases where the classifier decides negative when the sample was actually positive. To evaluate the performance of a classifier, we run the classifier on all the samples of a data set and then count all the instances of true negatives, true positives, false negatives, and false positives. All of the performance metrics in this report are then formed from a combination of these four basic decision categories.
Intelligent scheduling support for the US Coast Guard
Darby-Dowman, K.; Lucas, C.; Mitra, G. ); Fink, R. ); Kingsley, L.; Smith, J.W. )
1992-01-01
This paper will discuss a joint effort by the U.S. Coast Guard Research Development Center, Idaho National Engineering Laboratory and Brunel University to provide the necessary tools to increase the human scheduler's capability to handle the scheduling process more efficiently and effectively. Automating the scheduling process required a system that could think independently of the scheduler, that is, the systems needed its own control mechanism and knowledge base. Further, automated schedule generation became a design requirement and sophisticated algorithms were formulated to solve a complex combinatorial problem. In short, the resulting design can be viewed as a hybrid knowledge-based mathematical programming application system. This document contains an overview of the integrated system, a discrete optimization model for scheduling, and schedule diagnosis and analysis.
Design, implementation, and operation of a class based batch queue scheduler for VAX/VMS
Chadwick, K.
1988-05-20
Fermilab found that the standard VMS batch configuration options were inadequate for the job mix that exists on the Fermilab central computer facility VAX cluster. Accordingly, Fermilab designed and implemented a class based batch queue scheduler. This scheduler makes use of the standard VMS job controller and batch system. Users interact with the scheduler at job submission time by specification of CPU time limits and batch job characteristics. This scheduler allows Fermilab to make efficient use of our large heterogeneous VAX cluster which contains machines ranging from a VAX 780 to a VAX 8800. The scheduler was implemented using the VMS system services $GETQUI and $SNDJBC, without changes to the existing VMS job scheduler. As a result, the scheduler should remain compatible with future VMS versions. This session will discuss the design goals, implementation, and operational experience with Fermilab's class based batch queue scheduler.
A comparison of dense-to-lean and fixed lean schedules of alternative reinforcement and extinction.
Hagopian, Louis P; Toole, Lisa M; Long, Ethan S; Bowman, Lynn G; Lieving, Gregory A
2004-01-01
Behavior-reduction interventions typically employ dense schedules of alternative reinforcement in conjunction with operant extinction for problem behavior. After problem behavior is reduced in the initial treatment stages, schedule thinning is routinely conducted to make the intervention more practical in natural environments. In the current investigation, two methods for thinning alternative reinforcement schedules were compared for 3 clients who exhibited severe problem behavior. In the dense-to-lean (DTL) condition, reinforcement was delivered on relatively dense schedules (using noncontingent reinforcement for 1 participant and functional communication training for 2 participants), followed by systematic schedule thinning to progressively leaner schedules. During the fixed lean (FL) condition, reinforcement was delivered on lean schedules (equivalent to the terminal schedule of the DTL condition). The FL condition produced a quicker attainment of individual treatment goals for 2 of the 3 participants. The results are discussed in terms of the potential utility of using relatively lean schedules at treatment outset. PMID:15529889
Ada and cyclic runtime scheduling
NASA Technical Reports Server (NTRS)
Hood, Philip E.
1986-01-01
An important issue that must be faced while introducing Ada into the real time world is efficient and prodictable runtime behavior. One of the most effective methods employed during the traditional design of a real time system is the cyclic executive. The role cyclic scheduling might play in an Ada application in terms of currently available implementations and in terms of implementations that might be developed especially to support real time system development is examined. The cyclic executive solves many of the problems faced by real time designers, resulting in a system for which it is relatively easy to achieve approporiate timing behavior. Unfortunately a cyclic executive carries with it a very high maintenance penalty over the lifetime of the software that is schedules. Additionally, these cyclic systems tend to be quite fragil when any aspect of the system changes. The findings are presented of an ongoing SofTech investigation into Ada methods for real time system development. The topics covered include a description of the costs involved in using cyclic schedulers, the sources of these costs, and measures for future systems to avoid these costs without giving up the runtime performance of a cyclic system.
NASA Schedule Management Handbook
NASA Technical Reports Server (NTRS)
2011-01-01
The purpose of schedule management is to provide the framework for time-phasing, resource planning, coordination, and communicating the necessary tasks within a work effort. The intent is to improve schedule management by providing recommended concepts, processes, and techniques used within the Agency and private industry. The intended function of this handbook is two-fold: first, to provide guidance for meeting the scheduling requirements contained in NPR 7120.5, NASA Space Flight Program and Project Management Requirements, NPR 7120.7, NASA Information Technology and Institutional Infrastructure Program and Project Requirements, NPR 7120.8, NASA Research and Technology Program and Project Management Requirements, and NPD 1000.5, Policy for NASA Acquisition. The second function is to describe the schedule management approach and the recommended best practices for carrying out this project control function. With regards to the above project management requirements documents, it should be noted that those space flight projects previously established and approved under the guidance of prior versions of NPR 7120.5 will continue to comply with those requirements until project completion has been achieved. This handbook will be updated as needed, to enhance efficient and effective schedule management across the Agency. It is acknowledged that most, if not all, external organizations participating in NASA programs/projects will have their own internal schedule management documents. Issues that arise from conflicting schedule guidance will be resolved on a case by case basis as contracts and partnering relationships are established. It is also acknowledged and understood that all projects are not the same and may require different levels of schedule visibility, scrutiny and control. Project type, value, and complexity are factors that typically dictate which schedule management practices should be employed.
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
Deo, Rahul C
2015-11-17
Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. PMID:26572668
Deo, Rahul C
2015-11-17
Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.
A component analysis of schedule thinning during functional communication training.
Betz, Alison M; Fisher, Wayne W; Roane, Henry S; Mintz, Joslyn C; Owen, Todd M
2013-01-01
One limitation of functional communication training (FCT) is that individuals may request reinforcement via the functional communication response (FCR) at exceedingly high rates. Multiple schedules with alternating periods of reinforcement and extinction of the FCR combined with gradually lengthening the extinction-component interval can effectively address this limitation. However, the extent to which each of these components contributes to the effectiveness of the overall approach remains uncertain. In the current investigation, we evaluated the first component by comparing rates of the FCR and problem behavior under mixed and multiple schedules and evaluated the second component by rapidly switching from dense mixed and multiple schedules to lean multiple schedules without gradually thinning the density of reinforcement. Results indicated that multiple schedules decreased the overall rate of reinforcement for the FCR and maintained the strength of the FCR and low rates of problem behavior without gradually thinning the reinforcement schedule.
49 CFR 214.531 - Schedule of repairs; general.
Code of Federal Regulations, 2012 CFR
2012-10-01
... 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....
49 CFR 214.531 - Schedule of repairs; general.
Code of Federal Regulations, 2011 CFR
2011-10-01
... 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....
49 CFR 214.531 - Schedule of repairs; general.
Code of Federal Regulations, 2014 CFR
2014-10-01
... 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....
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....
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).
De Bock, Hendrik Pieter Jacobus; Alexander, James Pellegrino; El-Refaie, Ayman Mohamed Fawzi; Gerstler, William Dwight; Shah, Manoj Ramprasad; Shen, Xiaochun
2016-06-21
An apparatus, such as an electrical machine, is provided. The apparatus can include a rotor defining a rotor bore and a conduit disposed in and extending axially along the rotor bore. The conduit can have an annular conduit body defining a plurality of orifices disposed axially along the conduit and extending through the conduit body. The rotor can have an inner wall that at least partially defines the rotor bore. The orifices can extend through the conduit body along respective orifice directions, and the rotor and conduit can be configured to provide a line of sight along the orifice direction from the respective orifices to the inner wall.
Rohwein, G.J.; Lancaster, K.T.; Lawson, R.N.
1986-06-01
TEMPO is a transformer powered megavolt pulse generator with an output pulse of 100 ns duration. The machine was designed for burst mode operation at pulse repetition rates up to 10 Hz with minimum pulse-to-pulse voltage variations. To meet the requirement for pulse duration a nd a 20-..omega.. output impedance within reasonable size constraints, the pulse forming transmission line was designed as two parallel water-insulated, strip-type Blumleins. Stray capacitance and electric fields along the edges of the line elements were controlled by lining the tank with plastic sheet.
Cardoso, Goncalo; Stadler, Michael; Bozchalui, Mohammed C.; Sharma, Ratnesh; Marnay, Chris; Barbosa-Povoa, Ana; Ferrao, Paulo
2013-12-06
The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem.
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.
S-CHART - SCHEDULING CHART PROGRAM
NASA Technical Reports Server (NTRS)
Klinkner, E. R.
1994-01-01
The Scheduling Chart Program (S-Chart) uses simple menu choices to produce high quality Gantt type scheduling and production time-line charts with minimal data entry requirements. The software produces high quality charts (20, 40, and 80 day options) in 10-20 minutes including start up, data entry, and printing. Reprints take less than one minute. Some of the more significant features are as follows: speed and ease of use with menu driven selections from start to finish, storage and catalogs of all data sets for rapid access and manipulation, compact program size to permit storage of dozens of data sets on the program disc, and creation of ASCII text files of graphs for rapid printing. The final product of the plot routine is an extended ASCII character file which shows the events scheduled and actual work dates. This file may be printed from the program, from DOS, or imported to a word processor for customizing. S-Chart is not intended to take the place of commercial scheduling/project management software. However, it is a simple, somewhat limited Gantt chart plotter and scheduling tracking program with an emphasis on straightforward data entry. There is no complex relationship building between the scheduled events. Dates are entered as dates, not as algebraic functions. All the files of S-Chart, except for the graph picture and the stand-alone executable, were written in 100% dBASE III compatible code. The executable code was created with CLIPPER and the graph picture files are written in ASCII code. S-Chart was implemented on an IBM PC series machine under DOS and requires 215k bytes of memory. The program was developed in 1988.
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
Reinforcement schedule thinning following treatment with functional communication training.
Hanley, G P; Iwata, B A; Thompson, R H
2001-01-01
We evaluated four methods for increasing the practicality of functional communication training (FCT) by decreasing the frequency of reinforcement for alternative behavior. Three participants whose problem behaviors were maintained by positive reinforcement were treated successfully with FCT in which reinforcement for alternative behavior was initially delivered on fixed-ratio (FR) 1 schedules. One participant was then exposed to increasing delays to reinforcement under FR 1, a graduated fixed-interval (FI) schedule, and a graduated multiple-schedule arrangement in which signaled periods of reinforcement and extinction were alternated. Results showed that (a) increasing delays resulted in extinction of the alternative behavior, (b) the FI schedule produced undesirably high rates of the alternative behavior, and (c) the multiple schedule resulted in moderate and stable levels of the alternative behavior as the duration of the extinction component was increased. The other 2 participants were exposed to graduated mixed-schedule (unsignaled alternation between reinforcement and extinction components) and multiple-schedule (signaled alternation between reinforcement and extinction components) arrangements in which the durations of the reinforcement and extinction components were modified. Results obtained for these 2 participants indicated that the use of discriminative stimuli in the multiple schedule facilitated reinforcement schedule thinning. Upon completion of treatment, problem behavior remained low (or at zero), whereas alternative behavior was maintained as well as differentiated during a multiple-schedule arrangement consisting of a 4-min extinction period followed by a 1-min reinforcement period. PMID:11317985
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.
Online stochastic optimization of radiotherapy patient scheduling.
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.
Applications of the connection machine
Waltz, D.L.
1987-01-01
The Connection Machine development effort was initiated in the belief that parallel processing and artificial intelligence could together accelerate the rate of progress toward truly intelligent machines. This progress is the result of the ease with which the machine can be programmed and the dramatic increase in compute power that the machine can bring to bear. The authors have been able to run many trials of experiments in instances where previously, running just one would have been considered an achievement and no further experimentation would have been done. This has enabled exploring a great many more hypotheses and to work on much larger problems that had been possible on previous-generation artificial intelligence workstations. The ease of programming is in part the result of a decision to use existing serial machines (the Symbolics 3600 or Digital Equipment Corporation VAX), thus leaving unchanged the operating systems, editors, file systems, debuggers, network communications systems, and so on, so as to provide familiar programming environments. The Connection Machine is programmed in conservative extensions of Common Lisp and C. Users familiar with these languages and with front-end computer systems have been able to produce results on the Connection Machine on the first day that they use it.
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. PMID:23277612
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.
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.
Advanced coordinate measuring machine at Sandia National Laboratories/California
Pilkey, R.D.; Klevgard, P.A.
1993-03-01
Sandia National Laboratories/California has acquired a new Moore M-48V CNC five-axis universal coordinate measuring machine (CMM). Site preparation, acceptance testing, and initial performance results are discussed. Unique features of the machine include a ceramic ram and vacuum evacuated laser pathways (VELPS). The implementation of a VELPS system on the machine imposed certain design requirements and entailed certain start-up problems. The machine`s projected capabilities, workload, and research possibilities are outlined.
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.
Becker, H.R.
1984-12-04
A mining machine is disclosed comprising a mobile base and a cutting head assembly at a forward end of the mobile base having a cutter drum rotatable about an output shaft disposed along the longitudinal axis of the cutter drum. A drive system for the cutting head assembly comprises at least one motor for driving at least one toothed motor pinion and a generally cylindrical combination gear having generally circular end surfaces. A bevel or face gear is formed in at least one of the end surfaces, having teeth adapted to mate with and be driven by the toothed motor pinion. The combination gear has a worm gear formed in the outside cylindrical surface, which is disposed in driving engagement with the teeth of an output gear integrally and coaxially connected to the output shaft of the cutter drum.
A flexible nurse scheduling support system.
Ozkarahan, I
1989-01-01
Salaries paid to nursing personnel constitute the largest chunk of a hospital's budget. Therefore, this human resource must be utilized efficiently. Hospitals provide continuous service without the exception of holidays and personal preferences. This causes the nurses' discontent in shift scheduling. And the consequence of this discontent is the nurse shortage. This and the pressures on hospitals to limit costs increase the importance of the nurse scheduling problem. Scheduling nursing personnel in hospitals is very complex due to the variety of conflicting interests or objectives between hospitals and nurses. Also, the demand, which varies widely 24-h a day 7-day a week is skill specific and hard to forecast. In the face of this complexity, the present nurse scheduling models have met with little success. In this paper, we propose a more flexible decision support system that will satisfy the interests of both hospitals and nurses through alternative models that attempt to accommodate flexible work patterns as it integrates time of the day (TOD) and day of the week (DOW) scheduling problems. PMID:2582748
Spanning Tree Calculations on D-Wave 2 Machines
NASA Astrophysics Data System (ADS)
Novotny, M. A.; Hobl, Q. L.; Hall, J. S.; Michielsen, K.
2016-02-01
Calculations on D-Wave machines are presented, both for the 500-qubit and the 1000-qubit machines. Results are presented for spanning trees on the available K4,4 Chimera graphs of both machines. Comparing trees of approximately the same size, the frequency of finding the ground state for the 1000-qubit machine is significantly improved over the 500- qubit older generation machine. Spanning trees are difficult problems for solution by adiabatic quantum computers, so the enhanced frequency of finding the ground state for newer machine generations and larger machines is encouraging for this immature technology.
Machine speech and speaking about machines
Nye, A.
1996-12-31
Current philosophy of language prides itself on scientific status. It boasts of being no longer contaminated with queer mental entities or idealist essences. It theorizes language as programmable variants of formal semantic systems, reimaginable either as the properly epiphenomenal machine functions of computer science or the properly material neural networks of physiology. Whether or not such models properly capture the physical workings of a living human brain is a question that scientists will have to answer. I, as a philosopher, come at the problem from another direction. Does contemporary philosophical semantics, in its dominant truth-theoretic and related versions, capture actual living human thought as it is experienced, or does it instead reflect, regardless of (perhaps dubious) scientific credentials, pathology of thought, a pathology with a disturbing social history.
MLZ: Machine Learning for photo-Z
NASA Astrophysics Data System (ADS)
Carrasco Kind, Matias; Brunner, Robert
2014-03-01
The parallel Python framework MLZ (Machine Learning and photo-Z) computes fast and robust photometric redshift PDFs using Machine Learning algorithms. It uses a supervised technique with prediction trees and random forest through TPZ that can be used for a regression or a classification problem, or a unsupervised methods with self organizing maps and random atlas called SOMz. These machine learning implementations can be efficiently combined into a more powerful one resulting in robust and accurate probability distributions for photometric redshifts.
Distributed project scheduling at NASA: Requirements for manual protocols and computer-based support
NASA Technical Reports Server (NTRS)
Richards, Stephen F.
1992-01-01
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.
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.
A harmonized immunization schedule for Canada: A call to action
MacDonald, NE; Bortolussi, R
2011-01-01
In Canada, the National Advisory Committee on Immunization systematically reviews the evidence for the effectiveness and safety of new and old vaccines, and sets a ‘minimum’ recommended schedule. However, in contrast to other industrialized countries where single, harmonized countrywide immunization schedules are de rigeur, Canada has a confusing system, with each province and territory defining its own schedule – and none are the same. The time has come to rectify this decades-old patient equity and safety problem. The Canadian Paediatric Society calls for a harmonized schedule to improve the health and safety of Canadian children and youth. PMID:22211070
Association installs condom machine.
1994-08-01
On the occasion of World Population Day (11 July), India installed its first condom vending machine. The machine was inaugurated by Mr. Eruch Lala, an official of the Family Planning Association of India, as part of the association's campaign to help the country curb its rapid population growth rate and stem the spread of AIDS (acquired immune deficiency syndrome). Each condom, called sangam ("union" in English) costs Rupees 2 (about 6.5 US cents). The machine is located at a textile mill in Bombay. The Association said it would install at least 60 such machines in Bombay over the coming months. "A psychological advantage of the machine is that the user need not personally meet the dispenser and can collect a condom without any embarrassment," Mr. Lala said. "The machine is expected to promote efforts at curbing population growth and prevent the spread of AIDS," he said. In a separate report, AIDS has been found to be racing through India just eight years after the first case was detected. Prostitutes, drug addicts and untested blood supplies are the conduits. More than half of the prostitutes in cities such as Bombay have HIV (human immunodeficiency virus), which causes AIDS. The truck drivers and itinerant workers they serve carry it to their own villages, according to the report by Mr. Thomas Wagner writing for the Associated Press. There are 43 million cases of sexually transmitted diseases reported each year in the country, according to the report. The HIV virus has been reported in all 25 states of India. Although the AIDS pandemic came to India later than most large countries, the National AIDS Control Organization estimates there are 1.62 million cases in the population, up 60% from 1993, according to the report. "AIDS is no longer just a problem of high-risk groups; it has spread to every area of India," Dr. P.R. Das Gupta of the national AIDS agency said in an interview. "So many people are migrating from their villages in search of jobs that this
A software tool for dataflow graph scheduling
NASA Technical Reports Server (NTRS)
Jones, Robert L., III
1994-01-01
A graph-theoretic design process and software tool is presented for selecting a multiprocessing scheduling solution for a class of computational problems. The problems of interest are those that can be described using a dataflow graph and are intended to be executed repetitively on multiple processors. The dataflow paradigm is very useful in exposing the parallelism inherent in algorithms. It provides a graphical and mathematical model which describes a partial ordering of algorithm tasks based on data precedence.
Single Bacteria as Turing Machines
NASA Astrophysics Data System (ADS)
Bos, Julia; Zang, Qiucen; Vyawahare, Saurabh; Austin, Robert
2014-03-01
In Allan Turing's famous 1950 paper on Computing Machinery and Intelligence, he started with the provocative statement: ``I propose to consider the question, `Can machines think?' This should begin with definitions of the meaning of the terms `machine' and `think'.'' In our own work on exploring the way that organisms respond to stress and evolve, it seems at times as if they come to remarkably fast solutions to problems, indicating some sort of very clever computational machinery. I'll discuss how it would appear that bacteria can indeed create a form of a Turing Machine, the first example of a computer, and how they might use this algorithm to do rapid evolution to solve a genomics problem.
An Evaluation of Parallel Job Scheduling for ASCI Blue-Pacific
Franke, H.; Jann, J.; Moreira, J.; Pattnaik, P.; Jette, M.
1999-11-09
In this paper we analyze the behavior of a gang-scheduling strategy that we are developing for the ASCI Blue-Pacific machines. Using actual job logs for one of the ASCI machines we generate a statistical model of the current workload with hyper Erlang distributions. We then vary the parameters of those distributions to generate various workloads, representative of different operating points of the machine. Through simulation we obtain performance parameters for three different scheduling strategies: (i) first-come first-serve, (ii) gang-scheduling, and (iii) backfilling. Our results show that backfilling, can be very effective for the common operating points in the 60-70% utilization range. However, for higher utilization rates, time-sharing techniques such as gang-scheduling offer much better performance.
From human-machine interaction to human-machine cooperation.
Hoc, J M
2000-07-01
Since the 1960s, the rapid growth of information systems has led to the wide development of research on human-computer interaction (HCI) that aims at the designing of human-computer interfaces presenting ergonomic properties, such as friendliness, usability, transparency, etc. Various work situations have been covered--clerical work, computer programming, design, etc. However, they were mainly static in the sense that the user fully controls the computer. More recently, public and private organizations have engaged themselves in the enterprise of managing more and more complex and coupled systems by the means of automation. Modern machines not only process information, but also act on dynamic situations as humans have done in the past, managing stock exchange, industrial plants, aircraft, etc. These dynamic situations are not fully controlled and are affected by uncertain factors. Hence, degrees of freedom must be maintained to allow the humans and the machine to adapt to unforeseen contingencies. A human-machine cooperation (HMC) approach is necessary to address the new stakes introduced by this trend. This paper describes the possible improvement of HCI by HMC, the need for a new conception of function allocation between humans and machines, and the main problems encountered within the new forms of human-machine relationship. It proposes a conceptual framework to study HMC from a cognitive point of view in highly dynamic situations like aircraft piloting or air-traffic control, and concludes on the design of 'cooperative' machines.
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.
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.
Expanding symmetric multiprocessor capability through gang scheduling
Jette, M.A.
1998-03-01
Symmetric Multiprocessor (SMP) systems normally provide both space- sharing and time-sharing to insure high system utilization and good responsiveness. However the prevailing lack of concurrent scheduling for parallel programs precludes SMP use in addressing many large-scale problems. Tightly synchronized communications are impractical and normal time-sharing reduces the benefit of cache memory. Evidence gathered at Lawrence Livermore National Laboratory (LLNL) indicates that gang scheduling can increase the capability of SMP systems and parallel program performance without adverse impact upon system utilization or responsiveness.
Autonomous scheduling technology for Earth orbital missions
NASA Technical Reports Server (NTRS)
Srivastava, S.
1982-01-01
The development of a dynamic autonomous system (DYASS) of resources for the mission support of near-Earth NASA spacecraft is discussed and the current NASA space data system is described from a functional perspective. The future (late 80's and early 90's) NASA space data system is discussed. The DYASS concept, the autonomous process control, and the NASA space data system are introduced. Scheduling and related disciplines are surveyed. DYASS as a scheduling problem is also discussed. Artificial intelligence and knowledge representation is considered as well as the NUDGE system and the I-Space system.
Planning, scheduling, and control for automatic telescopes
NASA Technical Reports Server (NTRS)
Drummond, Mark; Swanson, Keith; Philips, Andy; Levinson, Rich; Bresina, John
1992-01-01
This paper presents an argument for the appropriateness of Entropy Reduction Engine (ERE) technology to the planning, scheduling, and control components of Automatic Photoelectric Telescope (APT) management. The paper is organized as follows. In the next section, we give a brief summary of the planning and scheduling requirements for APTs. Following this, in section 3, we give an ERE project precis, couched primarily in terms of project objectives. Section 4 gives a sketch of the match-up between problem and technology, and section 5 outlines where we want to go with this work.
Workshop Scheduling in the MRO Context
NASA Astrophysics Data System (ADS)
Rupp, Benjamin; Pauli, Dirk; Feller, Sebastian; Skyttä, Manu
2010-09-01
Scheduling is an important task in production planning, as it can significantly increase the productivity of a workshop. In this paper we concentrate on a job-shop problem which arises at workshops of typical MRO service providers. An MRO does not only need to minimize the production time (the makespan) and maximize the plant utilization, it also needs to maximize the service and protection level of its stock. Hence, it has several objective functions which usually contradict each other. In this paper we present the novel CTO algorithm which helps to find a schedule regarding the mentioned objective functions.
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.
McShea, Daniel W
2013-12-01
Wants, preferences, and cares are physical things or events, not ideas or propositions, and therefore no chain of pure logic can conclude with a want, preference, or care. It follows that no pure-logic machine will ever want, prefer, or care. And its behavior will never be driven in the way that deliberate human behavior is driven, in other words, it will not be motivated or goal directed. Therefore, if we want to simulate human-style interactions with the world, we will need to first understand the physical structure of goal-directed systems. I argue that all such systems share a common nested structure, consisting of a smaller entity that moves within and is driven by a larger field that contains it. In such systems, the smaller contained entity is directed by the field, but also moves to some degree independently of it, allowing the entity to deviate and return, to show the plasticity and persistence that is characteristic of goal direction. If all this is right, then human want-driven behavior probably involves a behavior-generating mechanism that is contained within a neural field of some kind. In principle, for goal directedness generally, the containment can be virtual, raising the possibility that want-driven behavior could be simulated in standard computational systems. But there are also reasons to believe that goal-direction works better when containment is also physical, suggesting that a new kind of hardware may be necessary. PMID:23792091
McShea, Daniel W
2013-12-01
Wants, preferences, and cares are physical things or events, not ideas or propositions, and therefore no chain of pure logic can conclude with a want, preference, or care. It follows that no pure-logic machine will ever want, prefer, or care. And its behavior will never be driven in the way that deliberate human behavior is driven, in other words, it will not be motivated or goal directed. Therefore, if we want to simulate human-style interactions with the world, we will need to first understand the physical structure of goal-directed systems. I argue that all such systems share a common nested structure, consisting of a smaller entity that moves within and is driven by a larger field that contains it. In such systems, the smaller contained entity is directed by the field, but also moves to some degree independently of it, allowing the entity to deviate and return, to show the plasticity and persistence that is characteristic of goal direction. If all this is right, then human want-driven behavior probably involves a behavior-generating mechanism that is contained within a neural field of some kind. In principle, for goal directedness generally, the containment can be virtual, raising the possibility that want-driven behavior could be simulated in standard computational systems. But there are also reasons to believe that goal-direction works better when containment is also physical, suggesting that a new kind of hardware may be necessary.
Intelligent retail logistics scheduling
Rowe, J.; Jewers, K.; Codd, A.; Alcock, A.
1996-12-31
The Supply Chain Integrated Ordering Network (SCION) Depot Bookings system automates the planning and scheduling of perishable and non-perishable commodities and the vehicles that carry them into J. Sainsbury depots. This is a strategic initiative, enabling the business to make the key move from weekly to daily ordering. The system is mission critical, managing the inwards flow of commodities from suppliers into J. Sainsbury`s depots. The system leverages Al techniques to provide a business solution that meets challenging functional and performance needs. The SCION Depot Bookings system is operational providing schedules for 22 depots across the UK.
Dimensioning, Tolerancing, and Machine Finishes.
ERIC Educational Resources Information Center
Adams, George C.
Intended for use with the vocational education student interested in technical drawing, this guide provides answers to questions relating to dimensioning and tolerancing machine drawings. It also gives examples of standard dimensioning practices, tolerancing applications, and finish applications. The problems and examples presented are based on…
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.
What is a "schedule of reinforcement"?
Schoenfeld, W N; Cole, B K
1975-01-01
Several ambiguities in the present terminology of behavior theory obscure some important theoretical assumptions and experimental details in current research. Left unclarified, such ambiguities impede the accurate analysis of laboratory procedures, and prevent reliable communication among researchers. This paper focuses on the term "schedule of reinforcement". It points out that two distinguishable operational rules are implicated in the term: in the case where reinforcement is of the so-called response contingent type, the "schedule" is really a rule to identify the response to be reinforced; in the case of non-contingent reinforcement, the "schedule" is truly a rule for delivery of reinforcement. Other terminological ambiguities that are encountered in a discussion of this term include "reinforcement" and "intermittency." A resolution of these problems will necessarily involve the procedures of non-contingent reinforcement, and the parameter of reinforcement probability.
Scheduling Earth Observing Satellites with Evolutionary Algorithms
NASA Technical Reports Server (NTRS)
Globus, Al; Crawford, James; Lohn, Jason; Pryor, Anna
2003-01-01
We hypothesize that evolutionary algorithms can effectively schedule coordinated fleets of Earth observing satellites. The constraints are complex and the bottlenecks are not well understood, a condition where evolutionary algorithms are often effective. This is, in part, because evolutionary algorithms require only that one can represent solutions, modify solutions, and evaluate solution fitness. To test the hypothesis we have developed a representative set of problems, produced optimization software (in Java) to solve them, and run experiments comparing techniques. This paper presents initial results of a comparison of several evolutionary and other optimization techniques; namely the genetic algorithm, simulated annealing, squeaky wheel optimization, and stochastic hill climbing. We also compare separate satellite vs. integrated scheduling of a two satellite constellation. While the results are not definitive, tests to date suggest that simulated annealing is the best search technique and integrated scheduling is superior.
An intelligent CNC machine control system architecture
Miller, D.J.; Loucks, C.S.
1996-10-01
Intelligent, agile manufacturing relies on automated programming of digitally controlled processes. Currently, processes such as Computer Numerically Controlled (CNC) machining are difficult to automate because of highly restrictive controllers and poor software environments. It is also difficult to utilize sensors and process models for adaptive control, or to integrate machining processes with other tasks within a factory floor setting. As part of a Laboratory Directed Research and Development (LDRD) program, a CNC machine control system architecture based on object-oriented design and graphical programming has been developed to address some of these problems and to demonstrate automated agile machining applications using platform-independent software.
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.
Scheduling prioritized patients in emergency department laboratories.
Azadeh, A; Hosseinabadi Farahani, M; Torabzadeh, S; Baghersad, M
2014-11-01
This research focuses on scheduling patients in emergency department laboratories according to the priority of patients' treatments, determined by the triage factor. The objective is to minimize the total waiting time of patients in the emergency department laboratories with emphasis on patients with severe conditions. The problem is formulated as a flexible open shop scheduling problem and a mixed integer linear programming model is proposed. A genetic algorithm (GA) is developed for solving the problem. Then, the response surface methodology is applied for tuning the GA parameters. The algorithm is tested on a set of real data from an emergency department. Simulation results show that the proposed algorithm can significantly improve the efficiency of the emergency department by reducing the total waiting time of prioritized patients.
ERIC Educational Resources Information Center
Purdue Univ., Lafayette, IN. Educational Research Center.
This 116-item interview schedule designed for parents who failed to respond to the Questionnaire for Parents, is individually administered to the mother of the child of elementary school age. It consists of scales measuring 14 parent variables plus a section devoted to demographic variables: (1) parent's achievement aspirations for the child, (2)…
CMS multicore scheduling strategy
Perez-Calero Yzquierdo, Antonio; Hernandez, Jose; Holzman, Burt; Majewski, Krista; McCrea, Alison
2014-01-01
In the next years, processor architectures based on much larger numbers of cores will be most likely the model to continue 'Moore's Law' style throughput gains. This not only results in many more jobs in parallel running the LHC Run 1 era monolithic applications, but also the memory requirements of these processes push the workernode architectures to the limit. One solution is parallelizing the application itself, through forking and memory sharing or through threaded frameworks. CMS is following all of these approaches and has a comprehensive strategy to schedule multicore jobs on the GRID based on the glideinWMS submission infrastructure. The main component of the scheduling strategy, a pilot-based model with dynamic partitioning of resources that allows the transition to multicore or whole-node scheduling without disallowing the use of single-core jobs, is described. This contribution also presents the experiences made with the proposed multicore scheduling schema and gives an outlook of further developments working towards the restart of the LHC in 2015.
CMS multicore scheduling strategy
NASA Astrophysics Data System (ADS)
Pérez-Calero Yzquierdo, Antonio; Hernández, Jose; Holzman, Burt; Majewski, Krista; McCrea, Alison; Cms Collaboration
2014-06-01
In the next years, processor architectures based on much larger numbers of cores will be most likely the model to continue "Moore's Law" style throughput gains. This not only results in many more jobs in parallel running the LHC Run 1 era monolithic applications, but also the memory requirements of these processes push the workernode architectures to the limit. One solution is parallelizing the application itself, through forking and memory sharing or through threaded frameworks. CMS is following all of these approaches and has a comprehensive strategy to schedule multicore jobs on the GRID based on the glideinWMS submission infrastructure. The main component of the scheduling strategy, a pilot-based model with dynamic partitioning of resources that allows the transition to multicore or whole-node scheduling without disallowing the use of single-core jobs, is described. This contribution also presents the experiences made with the proposed multicore scheduling schema and gives an outlook of further developments working towards the restart of the LHC in 2015.
Class Schedules--Computer Loaded or Student Self-Scheduled?
ERIC Educational Resources Information Center
Wall, Edward F.
1979-01-01
In the two-step process of student scheduling, the initial phase of course selection is the most important. At Chesterton High School in Indiana, student self-scheduling is preferred over computer loading. (Author/MLF)
Code of Federal Regulations, 2011 CFR
2011-07-01
... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Approval of machines constructed of components... Electrically Operated Mining Equipment § 18.95 Approval of machines constructed of components approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field...
Code of Federal Regulations, 2013 CFR
2013-07-01
... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Approval of machines constructed of components... Electrically Operated Mining Equipment § 18.95 Approval of machines constructed of components approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field...
Code of Federal Regulations, 2014 CFR
2014-07-01
... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Approval of machines constructed of components... Electrically Operated Mining Equipment § 18.95 Approval of machines constructed of components approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field...
Code of Federal Regulations, 2012 CFR
2012-07-01
... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Approval of machines constructed of components... Electrically Operated Mining Equipment § 18.95 Approval of machines constructed of components approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field...
Code of Federal Regulations, 2010 CFR
2010-07-01
... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Approval of machines constructed of components... Electrically Operated Mining Equipment § 18.95 Approval of machines constructed of components approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field...
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.
Scheduling techniques in the Request Oriented Scheduling Engine (ROSE)
NASA Technical Reports Server (NTRS)
Zoch, David R.
1991-01-01
Scheduling techniques in the ROSE are presented in the form of the viewgraphs. The following subject areas are covered: agenda; ROSE summary and history; NCC-ROSE task goals; accomplishments; ROSE timeline manager; scheduling concerns; current and ROSE approaches; initial scheduling; BFSSE overview and example; and summary.
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. PMID:27536537
Planning and Scheduling for Environmental Sensor Networks
NASA Astrophysics Data System (ADS)
Frank, J. D.
2005-12-01
Environmental Sensor Networks are a new way of monitoring the environment. They comprise autonomous sensor nodes in the environment that record real-time data, which is retrieved, analyzed, integrated with other data sets (e.g. satellite images, GIS, process models) and ultimately lead to scientific discoveries. Sensor networks must operate within time and resource constraints. Sensors have limited onboard memory, energy, computational power, communications windows and communications bandwidth. The value of data will depend on when, where and how it was collected, how detailed the data is, how long it takes to integrate the data, and how important the data was to the original scientific question. Planning and scheduling of sensor networks is necessary for effective, safe operations in the face of these constraints. For example, power bus limitations may preclude sensors from simultaneously collecting data and communicating without damaging the sensor; planners and schedulers can ensure these operations are ordered so that they do not happen simultaneously. Planning and scheduling can also ensure best use of the sensor network to maximize the value of collected science data. For example, if data is best recorded using a particular camera angle but it is costly in time and energy to achieve this, planners and schedulers can search for times when time and energy are available to achieve the optimal camera angle. Planning and scheduling can handle uncertainty in the problem specification; planners can be re-run when new information is made available, or can generate plans that include contingencies. For example, if bad weather may prevent the collection of data, a contingent plan can check lighting conditions and turn off data collection to save resources if lighting is not ideal. Both mobile and immobile sensors can benefit from planning and scheduling. For example, data collection on otherwise passive sensors can be halted to preserve limited power and memory
IMPROVED RESULTS FOR STACKELBERG SCHEDULING STRATEGIES.
Kumar, A.; Marathe, M. V.; Kapernick, R. J.
2001-01-01
We continue the study initiated in [Ro01] on Stackelberg Scheduling Strategies. We are given a set of n independent parallel machines or equivalently a set of n parallel edges on which certain flow has to be sent. Each edge e is endowed with a latency function l{sub e}({center_dot}). The setting is that of a non-cooperative game: players choose edges so as minimize their individual latencies. Additionally, there is a single player who control as fraction ?? of the total flow. The goal is to find a strategy for the leader (i.e. an assignment of flow to indivual links) such that the selfish users react so as to minimize the total latency of the system. Building on the recent results in [Ro01, RT00], we show the following: 1. We devise a fully polynomial approximate scheme for the problem of finding the cheapest Stackelberg Strategy: given a performance requirement, our algorithm runs in time polynomial in n and {var_epsilon} and produces a Stackelberg strategy s, whose associated cost is within a 1 + {var_epsilon} factor of the optimum stackelberg strategy s*. The result is extended to obtain a polynomial-approximation scheme when instances are restricted to layered directed graphs in which each layer has a bounded number of vertices. 2. We then consider a two round Stackelberg strategy (denoted 2SS). In this strategy, the game consists of three rounds: a move by the leader followed by the moves of all the followers folowed again by a move by the leader who possibly reassigns some of the flows. We show that 2SS always dominates the one round scheme, and for some classes of latency functions, is guaranteed to be closer to the global social optimum. We also consider the variant where the leader plays after the selfish users have routed themselves, and observe that this dominates the one-round scheme. Extensions of the results to the special case when all the latency functions are linear are also presented. Our results extend the earlier results and answer an open
29 CFR 778.327 - Temporary or sporadic reduction in schedule.
Code of Federal Regulations, 2010 CFR
2010-07-01
... COMPENSATION Special Problems Reduction in Workweek Schedule with No Change in Pay § 778.327 Temporary or sporadic reduction in schedule. (a) The problem of reduction in the workweek is somewhat different where a... announced above. However, reduction on a more temporary or sporadic basis presents a different problem....
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.
Advance Resource Provisioning in Bulk Data Scheduling
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 improve 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.
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.
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.
ERIC Educational Resources Information Center
De Bono, Edward
A group of children were presented with several tasks, including the invention of a sleep machine and a machine to weigh elephants. The tasks were chosen to involve the children in coping with problems of a distinct character. A study of the children's drawings and interpretations shows that children's thinking ability is not very different from…
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.
Using the principles of circadian physiology enhances shift schedule design
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 and 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.
Surprise Benefits of Arena Scheduling
ERIC Educational Resources Information Center
Surloff, Andrew
2008-01-01
One of the most challenging tasks a principal must accomplish every year is the construction of the master schedule. Free from the magnetic scheduling boards and wall charts of yesteryear, principals now have technological tools--such as programs that offer schools solutions for their scheduling needs--that can save time and enable them to work…
Scheduling and Achievement. Research Brief
ERIC Educational Resources Information Center
Walker, Karen
2006-01-01
To use a block schedule or a traditional schedule? Which structure will produce the best and highest achievement rates for students? The research is mixed on this due to numerous variables such as: (1) socioeconomic levels; (2) academic levels; (3) length of time a given schedule has been in operation; (4) strategies being used in the classrooms;…
ERIC Educational Resources Information Center
Murray, Shannon
2008-01-01
Flexible modular scheduling (flex mod)--a schedule philosophy and system that has been in place at Wausau West High School in Wausau, Wisconsin, for the last 35 years and aligns nicely with current research on student learning--is getting more and more attention from high school administrators across the country. Flexible modular scheduling was…
Flexible Scheduling: Making the Transition
ERIC Educational Resources Information Center
Creighton, Peggy Milam
2008-01-01
Citing literature that supports the benefits of flexible scheduling on student achievement, the author exhorts readers to campaign for flexible scheduling in their library media centers. She suggests tips drawn from the work of Graziano (2002), McGregor (2006) and Stripling (1997) for making a smooth transition from fixed to flexible scheduling:…
Federal Register 2010, 2011, 2012, 2013, 2014
2010-07-22
... Items), Schedule L (Balance Sheets per Books), Schedule M-1 (Reconciliation of Income (Loss) per Books.... (Schedule K-1), Balance Sheets per Books (Schedule L), Reconciliation of Income (Loss) per Books With...
Revisiting conjugate schedules.
MacAleese, Kenneth R; Ghezzi, Patrick M; Rapp, John T
2015-07-01
The effects of conjugate reinforcement on the responding of 13 college students were examined in three experiments. Conjugate reinforcement was provided via key presses that changed the clarity of pictures displayed on a computer monitor in a manner proportional to the rate of responding. Experiment 1, which included seven parameters of clarity change per response, revealed that responding decreased as the percentage clarity per response increased for all five participants. These results indicate that each participant's responding was sensitive to intensity change, which is a parameter of conjugate reinforcement schedules. Experiment 2 showed that responding increased during conjugate reinforcement phases and decreased during extinction phases for all four participants. Experiment 3 also showed that responding increased during conjugate reinforcement and further showed that responding decreased during a conjugate negative punishment condition for another four participants. Directions for future research with conjugate schedules are briefly discussed. PMID:26150349
Revisiting conjugate schedules.
MacAleese, Kenneth R; Ghezzi, Patrick M; Rapp, John T
2015-07-01
The effects of conjugate reinforcement on the responding of 13 college students were examined in three experiments. Conjugate reinforcement was provided via key presses that changed the clarity of pictures displayed on a computer monitor in a manner proportional to the rate of responding. Experiment 1, which included seven parameters of clarity change per response, revealed that responding decreased as the percentage clarity per response increased for all five participants. These results indicate that each participant's responding was sensitive to intensity change, which is a parameter of conjugate reinforcement schedules. Experiment 2 showed that responding increased during conjugate reinforcement phases and decreased during extinction phases for all four participants. Experiment 3 also showed that responding increased during conjugate reinforcement and further showed that responding decreased during a conjugate negative punishment condition for another four participants. Directions for future research with conjugate schedules are briefly discussed.
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.
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.
NASA Technical Reports Server (NTRS)
1997-01-01
Vern Wedeven, president of Wedeven Associates, developed the WAM4, a computer-aided "smart" test machine for simulating stress on equipment, based on his bearing lubrication expertise gained while working for Lewis Research Center. During his NASA years from the 1970s into the early 1980s, Wedeven initiated an "Interdisciplinary Collaboration in Tribology," an effort that involved NASA, six universities, and several university professors. The NASA-sponsored work provided foundation for Wedeven in 1983 to form his own company. Several versions of the smart test machine, the WAM1, WAM2, and WAM3, have proceeded the current version, WAM4. This computer-controlled device can provide detailed glimpses at gear and bearing points of contact. WAM4 can yield a three-dimensional view of machinery as an operator adds "what-if" thermal and lubrication conditions, contact stress, and surface motion. Along with NASA, a number of firms, including Pratt & Whitney, Caterpillar Tractor, Exxon, and Chevron have approached Wedeven for help on resolving lubrication problems.
Advanced coordinate measuring machine at Sandia National Laboratories
NASA Astrophysics Data System (ADS)
Pilkey, R. D.; Klevgard, P. A.
1993-03-01
Sandia National Laboratories/California has acquired a new Moore M-48V CNC five-axis universal coordinate measuring machine (CMM). Site preparation, acceptance testing, and initial performance results are discussed. Unique features of the machine include a ceramic ram and vacuum evacuated laser pathways (VELPS). The implementation of a VELPS system on the machine imposed certain design requirements and entailed certain start-up problems. The machine's projected capabilities, workload, and research possibilities are outlined.
Advanced coordinate measuring machine at Sandia National Laboratories/California
Pilkey, R.D.; Klevgard, P.A.
1993-03-01
Sandia National Laboratories/California has acquired a new Moore M-48V CNC five-axis universal coordinate measuring machine (CMM). Site preparation, acceptance testing, and initial performance results are discussed. Unique features of the machine include a ceramic ram and vacuum evacuated laser pathways (VELPS). The implementation of a VELPS system on the machine imposed certain design requirements and entailed certain start-up problems. The machine's projected capabilities, workload, and research possibilities are outlined.
Scheduling in multibeam satellites with interfering zones
NASA Astrophysics Data System (ADS)
Gopal, I. S.; Wong, C. K.; Bonuccelli, M. A.
1983-08-01
The traffic scheduling problem in a satellite-switched time-division multiple-access (SS/TDMA) system with interfering beams is studied. A two-step approach to this problem is investigated, in which the first step is the assignment of orthogonal polarization to reduce the interference and the second step is the scheduling of traffic, taking into account the 'resultant' interference. It is shown that the first step can be solved in polynomial time in most cases, while the second step is proved to be NP-complete, even for very simple patterns. Several suboptimal algorithms are suggested for this second step, and it is shown by experimental trials on randomly generated traffic patterns that on the average these algorithms produce close to optimal solutions.
Krstulic, J.F.
2000-01-27
multiple runs of each stone. Although there were problems with a few stones, the machine automatically completed measurements on a majority of the stones tested. A demo was performed in Lawrence for AccuGem stockholders and potential investors. The demo successfully demonstrated our technology on a random stone brought by an attendee. In conclusion, the project was successful in development of the basic technology required for a diamond measuring machine. Continued improvements in lighting control, edge and defect extraction, and an increased image depth-of-field will increase the reliability and consistency of measurements. Although additional work is needed to make the machine a commercial product, there are no foreseeable technical roadblocks in that process.
A Comparison of Techniques for Scheduling Earth-Observing Satellites
NASA Technical Reports Server (NTRS)
Globus, Al; Crawford, James; Lohn, Jason; Pryor, Anna
2004-01-01
Scheduling observations by coordinated fleets of Earth Observing Satellites (EOS) involves large search spaces, complex constraints and poorly understood bottlenecks, conditions where evolutionary and related algorithms are often effective. However, there are many such algorithms and the best one to use is not clear. Here we compare multiple variants of the genetic algorithm: stochastic hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on ten realistically-sized EOS scheduling problems. Schedules are represented by a permutation (non-temperal ordering) of the observation requests. A simple deterministic scheduler assigns times and resources to each observation request in the order indicated by the permutation, discarding those that violate the constraints created by previously scheduled observations. Simulated annealing performs best. Random mutation outperform a more 'intelligent' mutator. Furthermore, the best mutator, by a small margin, was a novel approach we call temperature dependent random sampling that makes large changes in the early stages of evolution and smaller changes towards the end of search.
Analytical design of intelligent machines
NASA Technical Reports Server (NTRS)
Saridis, George N.; Valavanis, Kimon P.
1987-01-01
The problem of designing 'intelligent machines' to operate in uncertain environments with minimum supervision or interaction with a human operator is examined. The structure of an 'intelligent machine' is defined to be the structure of a Hierarchically Intelligent Control System, composed of three levels hierarchically ordered according to the principle of 'increasing precision with decreasing intelligence', namely: the organizational level, performing general information processing tasks in association with a long-term memory; the coordination level, dealing with specific information processing tasks with a short-term memory; and the control level, which performs the execution of various tasks through hardware using feedback control methods. The behavior of such a machine may be managed by controls with special considerations and its 'intelligence' is directly related to the derivation of a compatible measure that associates the intelligence of the higher levels with the concept of entropy, which is a sufficient analytic measure that unifies the treatment of all the levels of an 'intelligent machine' as the mathematical problem of finding the right sequence of internal decisions and controls for a system structured in the order of intelligence and inverse order of precision such that it minimizes its total entropy. A case study on the automatic maintenance of a nuclear plant illustrates the proposed approach.
Automated Platform Management System Scheduling
NASA Technical Reports Server (NTRS)
Hull, Larry G.
1990-01-01
The Platform Management System was established to coordinate the operation of platform systems and instruments. The management functions are split between ground and space components. Since platforms are to be out of contact with the ground more than the manned base, the on-board functions are required to be more autonomous than those of the manned base. Under this concept, automated replanning and rescheduling, including on-board real-time schedule maintenance and schedule repair, are required to effectively and efficiently meet Space Station Freedom mission goals. In a FY88 study, we developed several promising alternatives for automated platform planning and scheduling. We recommended both a specific alternative and a phased approach to automated platform resource scheduling. Our recommended alternative was based upon use of exactly the same scheduling engine in both ground and space components of the platform management system. Our phased approach recommendation was based upon evolutionary development of the platform. In the past year, we developed platform scheduler requirements and implemented a rapid prototype of a baseline platform scheduler. Presently we are rehosting this platform scheduler rapid prototype and integrating the scheduler prototype into two Goddard Space Flight Center testbeds, as the ground scheduler in the Scheduling Concepts, Architectures, and Networks Testbed and as the on-board scheduler in the Platform Management System Testbed. Using these testbeds, we will investigate rescheduling issues, evaluate operational performance and enhance the platform scheduler prototype to demonstrate our evolutionary approach to automated platform scheduling. The work described in this paper was performed prior to Space Station Freedom rephasing, transfer of platform responsibility to Code E, and other recently discussed changes. We neither speculate on these changes nor attempt to predict the impact of the final decisions. As a consequence some of our
Extremal quantum cloning machines
Chiribella, G.; D'Ariano, G. M.; Perinotti, P.; Cerf, N.J.
2005-10-15
We investigate the problem of cloning a set of states that is invariant under the action of an irreducible group representation. We then characterize the cloners that are extremal in the convex set of group covariant cloning machines, among which one can restrict the search for optimal cloners. For a set of states that is invariant under the discrete Weyl-Heisenberg group, we show that all extremal cloners can be unitarily realized using the so-called double-Bell states, whence providing a general proof of the popular ansatz used in the literature for finding optimal cloners in a variety of settings. Our result can also be generalized to continuous-variable optimal cloning in infinite dimensions, where the covariance group is the customary Weyl-Heisenberg group of displacement000.
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.
Automated long-term scheduling for the SOFIA airborne observatory
NASA Astrophysics Data System (ADS)
Civeit, Thomas
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 next observing period, known as Cycle 1, is scheduled to begin in late fall 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's long-term schedules. 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.
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.
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.