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.
A Hybrid Electromagnetism-Like Algorithm for Single Machine Scheduling Problem
NASA Astrophysics Data System (ADS)
Chen, Shih-Hsin; Chang, Pei-Chann; Chan, Chien-Lung; Mani, V.
Electromagnetism-like algorithm (EM) is a population-based meta-heuristic which has been proposed to solve continuous problems effectively. In this paper, we present a new meta-heuristic that uses the EM methodology to solve the single machine scheduling problem. Single machine scheduling is a combinatorial optimization problem. Schedule representation for our problem is based on random keys. Because there is little research in solving the combinatorial optimization problem (COP) by EM, the paper attempts to employ the random-key concept enabling EM to solve COP in single machine scheduling problem. We present a hybrid algorithm that combines the EM methodology and genetic operators to obtain the best/optimal schedule for this single machine scheduling problem, which attempts to achieve convergence and diversity effect when they iteratively solve the problem. The objective in our problem is minimization of the sum of earliness and tardiness. This hybrid algorithm was tested on a set of standard test problems available in the literature. The computational results show that this hybrid algorithm performs better than the standard genetic algorithm.
Manipulating Tabu List to Handle Machine Breakdowns in Job Shop Scheduling Problems
NASA Astrophysics Data System (ADS)
Nababan, Erna Budhiarti; SalimSitompul, Opim
2011-06-01
Machine breakdowns in a production schedule may occur on a random basis that make the well-known hard combinatorial problem of Job Shop Scheduling Problems (JSSP) becomes more complex. One of popular techniques used to solve the combinatorial problems is Tabu Search. In this technique, moves that will be not allowed to be revisited are retained in a tabu list in order to avoid in gaining solutions that have been obtained previously. In this paper, we propose an algorithm to employ a second tabu list to keep broken machines, in addition to the tabu list that keeps the moves. The period of how long the broken machines will be kept on the list is categorized using fuzzy membership function. Our technique are tested to the benchmark data of JSSP available on the OR library. From the experiment, we found that our algorithm is promising to help a decision maker to face the event of machine breakdowns.
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.
Extension of the Dynasearch to the Two-Machine Permutation Flowshop Scheduling Problem
NASA Astrophysics Data System (ADS)
Tanaka, Shunji
The purpose of this study is to construct a solution algorithm for the two-machine permutation flowshop problem based on the dynasearch. The dynasearch is an efficient local search algorithm that employs a special neighborhood structure called dynasearch swap neighborhood. Its primary advantage is that the neighborhood of a solution can be explored in polynomial time although it is composed of an exponential number of solutions. The dynasearch for machine scheduling was originally developed for the single-machine total weighted tardiness problem. Then, it was extended to the problem with idle time and setup times. This study further extends the dynasearch to the two-machine permutation flowshop problem and its effectiveness is examined by numerical experiments for both total weighted tardiness and total weighted earliness-tardiness objectives.
Some single-machine scheduling problems with learning effects and two competing agents.
Li, Hongjie; Li, Zeyuan; Yin, Yunqiang
2014-01-01
This study considers a scheduling environment in which there are two agents and a set of jobs, each of which belongs to one of the two agents and its actual processing time is defined as a decreasing linear function of its starting time. Each of the two agents competes to process its respective jobs on a single machine and has its own scheduling objective to optimize. The objective is to assign the jobs so that the resulting schedule performs well with respect to the objectives of both agents. The objective functions addressed in this study include the maximum cost, the total weighted completion time, and the discounted total weighted completion time. We investigate three problems arising from different combinations of the objectives of the two agents. The computational complexity of the problems is discussed and solution algorithms where possible are presented.
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. PMID:24883359
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.
Gang scheduling a parallel machine
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.
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.
NASA Astrophysics Data System (ADS)
Liou, Cheng-Dar; Hsieh, Yi-Chih; Chen, Yin-Yann
2013-01-01
This article investigates the two-machine flow-shop group scheduling problem (GSP) with sequence-dependent setup and removal times, and job transportation times between machines. The objective is to minimise the total completion time. As known, this problem is an NP-hard problem and generalises the typical two-machine GSPs. In this article, a new encoding scheme based on permutation representation is proposed to transform a random job permutation to a feasible permutation for GSPs. The proposed encoding scheme simultaneously determines both the sequence of jobs in each group and the sequence of groups. By reasonably combining particle swarm optimisation (PSO) and genetic algorithm (GA), we develop a fast and easily implemented hybrid algorithm (HA) for solving the considered problems. The effectiveness and efficiency of the proposed HA are demonstrated and compared with those of standard PSO and GA by numerical results of various tested instances with group numbers up to 20. In addition, three different lower bounds are developed to evaluate the solution quality of the HA. Limited numerical results indicate that the proposed HA is a viable and effective approach for the studied two-machine flow-shop group scheduling problem.
NASA Astrophysics Data System (ADS)
Birgin, Ernesto G.; Ronconi, Débora P.
2012-10-01
The single machine scheduling problem with a common due date and non-identical ready times for the jobs is examined in this work. Performance is measured by the minimization of the weighted sum of earliness and tardiness penalties of the jobs. Since this problem is NP-hard, the application of constructive heuristics that exploit specific characteristics of the problem to improve their performance is investigated. The proposed approaches are examined through a computational comparative study on a set of 280 benchmark test problems with up to 1000 jobs.
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.
Critical Machine Based Scheduling -A Review
NASA Astrophysics Data System (ADS)
Vivek, P.; Saravanan, R.; Chandrasekaran, M.; Pugazhenthi, R.
2017-03-01
This article aims to identify the natural occurrence of the critical machines in scheduling. The exciting scheduling in the real time manufacturing environment is focused on considering equal weight-age of all the machines, but very few researchers were considered the real time constraint(s) like processor/ machine/ workstation availability, etc.,. This article explores the gap between the theory and practices by identifying the critical machine in scheduling and helps the researcher to find the suitable problem in their case study environment. Through the literature survey, it is evident that, in scheduling the occurrence of the critical machine is in nature. The critical machine is found in various names and gives a various range of weight-age based on the particular manufacturing environment and it plays a vital role in scheduling which includes one or more circumstances of occurrence in the production environment. Very few researchers were reported that in manufacturing environment, the critical machine occurrence is in nature, but most of the researchers were focused to optimize the manufacturing environment by only reducing the cycle time. In real-time manufacturing environment, the scheduling of critical machine(s) was keenly monitored and some weight-age was considered.
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)
Kaplan, Sezgin; Rabadi, Ghaith
2013-01-01
This article addresses the aerial refuelling scheduling problem (ARSP), where a set of fighter jets (jobs) with certain ready times must be refuelled from tankers (machines) by their due dates; otherwise, they reach a low fuel level (deadline) incurring a high cost. ARSP is an identical parallel machine scheduling problem with release times and due date-to-deadline windows to minimize the total weighted tardiness. A simulated annealing (SA) and metaheuristic for randomized priority search (Meta-RaPS) with the newly introduced composite dispatching rule, apparent piecewise tardiness cost with ready times (APTCR), are applied to the problem. Computational experiments compared the algorithms' solutions to optimal solutions for small problems and to each other for larger problems. To obtain optimal solutions, a mixed integer program with a piecewise weighted tardiness objective function was solved for up to 12 jobs. The results show that Meta-RaPS performs better in terms of average relative error but SA is more efficient.
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.
NASA Astrophysics Data System (ADS)
He, Yong; Sun, Li
2015-05-01
In this paper, we introduce a group scheduling model with general deteriorating jobs and learning effects in which deteriorating jobs and learning effects are both considered simultaneously. This means that the actual processing time of a job depends not only on the processing time of the jobs already processed, but also on its scheduled position. In our model, the group setup times are general linear functions of their starting times and the jobs in the same group have general position-dependent learning effects and time-dependent deterioration. The objective of scheduling problems is to minimise the makespan and the sum of completion times, respectively. We show that the problems remain solvable in polynomial time under the proposed model.
NASA Astrophysics Data System (ADS)
Yamana, Takashi; Iima, Hitoshi; Sannomiya, Nobuo
Although there have been many studies on parallel machine scheduling problems, the number of machines operated is fixed in these studies. It is desirable to generate a schedule with fewer machines operated from the viewpoint of the operation cost of machines. In this paper, we cope with a problem of minimizing the number of parallel machines subject to the constraint that the total tardiness is not greater than the value given in advance. For this problem, we introduce a local search method in which the number of machines operated is changed efficiently and appropriately in a short time as well as reducing the total tardiness.
An Augmented Lagrangian Approach for Scheduling Problems
NASA Astrophysics Data System (ADS)
Nishi, Tatsushi; Konishi, Masami
The paper describes an augmented Lagrangian decomposition and coordination approach for solving single machine scheduling problems to minimize the total weighted tardiness. The problem belongs to the class of NP-hard combinatorial optimization problem. We propose an augmented Lagrangian decomposition and coordination approach, which is commonly used for continuous optimization problems, for solving scheduling problems despite the fact that the problem is nonconvex and non-differentiable. The proposed method shows a good convergence to a feasible solution without heuristically constructing a feasible solution. The performance of the proposed method is compared with that of an ordinary Lagrangian relaxation.
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.
Are accidents scheduled. [safety management problems
NASA Technical Reports Server (NTRS)
Childs, C.
1976-01-01
Two major sets of safety problems associated with project scheduling are examined. The first set involves problems resulting from the improper scheduling of the safety tasks. The second involves problems which result from inadequate attention to scheduling of those project tasks which lead to tests and operations and includes condensed schedules, modified schedules, schedule workarounds, eliminated portions of the schedules and strung out schedules.
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.
The compound Atwood machine problem
NASA Astrophysics Data System (ADS)
Lopes Coelho, R.
2017-05-01
The present paper accounts for progress in physics teaching in the sense that a problem, which has been closed to students for being too difficult, is gained for the high school curriculum. This problem is the compound Atwood machine with three bodies. Its introduction into high school classes is based on a recent study on the weighing of an Atwood machine.
The Compound Atwood Machine Problem
ERIC Educational Resources Information Center
Coelho, R. Lopes
2017-01-01
The present paper accounts for progress in physics teaching in the sense that a problem, which has been closed to students for being too difficult, is gained for the high school curriculum. This problem is the compound Atwood machine with three bodies. Its introduction into high school classes is based on a recent study on the weighing of an…
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.
Scheduling Jobs and a Variable Maintenance on a Single Machine with Common Due-Date Assignment
Wan, Long
2014-01-01
We investigate a common due-date assignment scheduling problem with a variable maintenance on a single machine. The goal is to minimize the total earliness, tardiness, and due-date cost. We derive some properties on an optimal solution for our problem. For a special case with identical jobs we propose an optimal polynomial time algorithm followed by a numerical example. PMID:25147861
Scheduling jobs and a variable maintenance on a single machine with common due-date assignment.
Wan, Long
2014-01-01
We investigate a common due-date assignment scheduling problem with a variable maintenance on a single machine. The goal is to minimize the total earliness, tardiness, and due-date cost. We derive some properties on an optimal solution for our problem. For a special case with identical jobs we propose an optimal polynomial time algorithm followed by a numerical example.
Approximation algorithms for scheduling unrelated parallel machines with release dates
NASA Astrophysics Data System (ADS)
Avdeenko, T. V.; Mesentsev, Y. A.; Estraykh, I. V.
2017-01-01
In this paper we propose approaches to optimal scheduling of unrelated parallel machines with release dates. One approach is based on the scheme of dynamic programming modified with adaptive narrowing of search domain ensuring its computational effectiveness. We discussed complexity of the exact schedules synthesis and compared it with approximate, close to optimal, solutions. Also we explain how the algorithm works for the example of two unrelated parallel machines and five jobs with release dates. Performance results that show the efficiency of the proposed approach have been given.
Backtracking Techniques for the Job Shop Scheduling Constraint Satisfaction Problem
1994-01-01
shallow learning to solve one-machine scheduling problem; [Burke 89], and that of Badie et al. whose system implements a variation of deep learning in...especially when dealing with large conflicts. Graph-based backjumping and N-th order shallow/ deep learning attempt to reduce the complexity of full...backtracking, 2nd-order deep learning , and the procedure combining the DCE and LOFF back’racking heuristics13, The second study compares the complete search
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.
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.
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
2016-09-09
scheduling problem according to the Korsah et al. taxonomy (Korsah, Stentz, and Dias 2013): XD [MA- MT-TA]. The problem considers multi-task agents (MA...AAAI, 380–385. Korsah, G. A.; Stentz, A.; and Dias, M. B. 2013. A com- prehensive taxonomy for multi-robot task allocation. IJRR 32(12):1495–1512. Odom
NASA Astrophysics Data System (ADS)
Sivarami Reddy, N.; Ramamurthy, D. V., Dr.; Prahlada Rao, K., Dr.
2017-08-01
This article addresses simultaneous scheduling of machines, AGVs and tools where machines are allowed to share the tools considering transfer times of jobs and tools between machines, to generate best optimal sequences that minimize makespan in a multi-machine Flexible Manufacturing System (FMS). Performance of FMS is expected to improve by effective utilization of its resources, by proper integration and synchronization of their scheduling. Symbiotic Organisms Search (SOS) algorithm is a potent tool which is a better alternative for solving optimization problems like scheduling and proven itself. The proposed SOS algorithm is tested on 22 job sets with makespan as objective for scheduling of machines and tools where machines are allowed to share tools without considering transfer times of jobs and tools and the results are compared with the results of existing methods. The results show that the SOS has outperformed. The same SOS algorithm is used for simultaneous scheduling of machines, AGVs and tools where machines are allowed to share tools considering transfer times of jobs and tools to determine the best optimal sequences that minimize makespan.
Single-machine scheduling with family setup times in a manufacturing system
NASA Astrophysics Data System (ADS)
Chen, Wen-Jinn
2008-06-01
This study considers a single-machine scheduling problem with sequence-dependent setup times. Specifically, this article discusses the problem with several families. In this research, the study assumes that the job being processed must be stopped if workers do not want to work at the weekend. This article calls the weekend period 'vacation'. Owing to complications in the production system, the setup time will be affected if the setup time is interrupted due to vacations. An efficient heuristic is developed to solve the problem of minimizing the maximum tardiness, subject to the family-setup time and vacation constraints. The article presents a heuristic to solve large-sized problems. A branch-and-bound algorithm that utilizes several theorems is also proposed to find the optimal schedules for the problem. Computational results are provided to demonstrate the effectiveness of the heuristic.
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.
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.
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.
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.
Hidri, Lotfi; Gharbi, Anis; Louly, Mohamed Aly
2014-01-01
We focus on the two-center hybrid flow shop scheduling problem with identical parallel machines and removal times. The job removal time is the required duration to remove it from a machine after its processing. The objective is to minimize the maximum completion time (makespan). A heuristic and a lower bound are proposed for this NP-Hard problem. These procedures are based on the optimal solution of the parallel machine scheduling problem with release dates and delivery times. The heuristic is composed of two phases. The first one is a constructive phase in which an initial feasible solution is provided, while the second phase is an improvement one. Intensive computational experiments have been conducted to confirm the good performance of the proposed procedures.
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.
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.
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...
Application of decentralized cooperative problem solving in dynamic flexible scheduling
NASA Astrophysics Data System (ADS)
Guan, Zai-Lin; Lei, Ming; Wu, Bo; Wu, Ya; Yang, Shuzi
1995-08-01
The object of this study is to discuss an intelligent solution to the problem of task-allocation in shop floor scheduling. For this purpose, the technique of distributed artificial intelligence (DAI) is applied. Intelligent agents (IAs) are used to realize decentralized cooperation, and negotiation is realized by using message passing based on the contract net model. Multiple agents, such as manager agents, workcell agents, and workstation agents, make game-like decisions based on multiple criteria evaluations. This procedure of decentralized cooperative problem solving makes local scheduling possible. And by integrating such multiple local schedules, dynamic flexible scheduling for the whole shop floor production can be realized.
Solution of the NP-hard total tardiness minimization problem in scheduling theory
NASA Astrophysics Data System (ADS)
Lazarev, A. A.
2007-06-01
The classical NP-hard (in the ordinary sense) problem of scheduling jobs in order to minimize the total tardiness for a single machine 1‖Σ T j is considered. An NP-hard instance of the problem is completely analyzed. A procedure for partitioning the initial set of jobs into subsets is proposed. Algorithms are constructed for finding an optimal schedule depending on the number of subsets. The complexity of the algorithms is O( n 2Σ p j ), where n is the number of jobs and p j is the processing time of the jth job ( j = 1, 2, …, n).
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.
Optimal recombination in genetic algorithms for flowshop scheduling problems
NASA Astrophysics Data System (ADS)
Kovalenko, Julia
2016-10-01
The optimal recombination problem consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. We prove NP-hardness of the optimal recombination for various variants of the flowshop scheduling problem with makespan criterion and criterion of maximum lateness. An algorithm for solving the optimal recombination problem for permutation flowshop problems is built, using enumeration of prefect matchings in a special bipartite graph. The algorithm is adopted for the classical flowshop scheduling problem and for the no-wait flowshop problem. It is shown that the optimal recombination problem for the permutation flowshop scheduling problem is solvable in polynomial time for almost all pairs of parent solutions as the number of jobs tends to infinity.
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
Analysis of feeder bus network design and scheduling problems.
Almasi, Mohammad Hadi; Mirzapour Mounes, Sina; Koting, Suhana; 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.
Algorithms for Scheduling and Network Problems
1991-09-01
68 4.2 Previous Work ........................................... 70 5 The Parallel Approximability of a Flow Problem 73...Algorithm A is said to have com~etitive ratio c (or is said to be c-competitive)’if Cmaa (") c. Cax(X") + 0(1) for all problem instances 1. If A is a...actually find minimum (unary) weight matchings. Since these algorithms run in time O(logk n) 70 CHAPTER 4. PARALLEL NETWORK OPTIMIZATION AN
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, 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, 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, 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....
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, 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....
1995-09-01
Cashman 11 Table of Contents Page Acknowledgements ii List of Figures vi List of Tables vii Abstract viii I: Introduction 1 General Issue 1 ...Introduction 14 Overall Research Approach 14 Appropriateness of the Data Source 16 Pilot Study 1 ? Data Selection i8 Data Identifying Schedule...Schedule Problems Observed on 22 Large Air Force System Development Efforts 85 Bibliography 106 Vita 108 List of Figures Figure Page 4- 1
An Application of Wedelin's Method to Railway Crew Scheduling Problem
NASA Astrophysics Data System (ADS)
Miura, Rei; Imaizumi, Jun; Fukumura, Naoto; Morito, Susumu
So many scheduling problems arise in railway industries. One of the typical scheduling problems is Crew Scheduling Problem. Much attention has been paid to this problem by a lot of researchers, but many studies have not been done to the problems in railway industries in Japan. In this paper, we consider a railway crew scheduling problem in Japan. The problem can be formulated into Set Covering Problem (SCP). In SCP, a row corresponds to a trip representing a minimal task and a column corresponds to a pairing representing a sequence of trips performed by a certain crew. Many algorithms have been developed and proposed for it. On the other hand, in practical use, it is important to investigate how these algorithms behave and work on a certain problem. Therefore, we focus on Wedelin's algorithm, which is based on Lagrange relaxation and is known as one of the high performance algorithms for SCP, and mainly examine the basic idea of this algorithm. Furthermore, we show effectiveness of this procedure through computational experiments on instances from Japanese railway.
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.
Solving cyclical nurse scheduling problem using preemptive goal programming
NASA Astrophysics Data System (ADS)
Sundari, V. E.; Mardiyati, S.
2017-07-01
Nurse scheduling system in a hospital is being modeled as a preemptive goal programming problem that is solved by using LINGO software with the objective function to minimize deviation variable at each goal. The scheduling is done cyclically, so every nurse is treated fairly since they have the same work shift portion with the other nurses. By paying attention to the hospital's rules regarding nursing work shift cyclically, it can be obtained that numbers of nurse needed in every ward are 18 nurses and the numbers of scheduling periods are 18 periods where every period consists of 21 days.
A bicriteria heuristic for an elective surgery scheduling problem.
Marques, Inês; Captivo, M Eugénia; Vaz Pato, Margarida
2015-09-01
Resource rationalization and reduction of waiting lists for surgery are two main guidelines for hospital units outlined in the Portuguese National Health Plan. This work is dedicated to an elective surgery scheduling problem arising in a Lisbon public hospital. In order to increase the surgical suite's efficiency and to reduce the waiting lists for surgery, two objectives are considered: maximize surgical suite occupation and maximize the number of surgeries scheduled. This elective surgery scheduling problem consists of assigning an intervention date, an operating room and a starting time for elective surgeries selected from the hospital waiting list. Accordingly, a bicriteria surgery scheduling problem arising in the hospital under study is presented. To search for efficient solutions of the bicriteria optimization problem, the minimization of a weighted Chebyshev distance to a reference point is used. A constructive and improvement heuristic procedure specially designed to address the objectives of the problem is developed and results of computational experiments obtained with empirical data from the hospital are presented. This study shows that by using the bicriteria approach presented here it is possible to build surgical plans with very good performance levels. This method can be used within an interactive approach with the decision maker. It can also be easily adapted to other hospitals with similar scheduling conditions.
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.
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)
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.
Cost-Minimizing Scheduling of Workflows on a Cloud of Memory Managed Multicore Machines
NASA Astrophysics Data System (ADS)
Grounds, Nicolas G.; Antonio, John K.; Muehring, Jeff
Workflows are modeled as hierarchically structured directed acyclic graphs in which vertices represent computational tasks, referred to as requests, and edges represent precedent constraints among requests. Associated with each workflow is a deadline that defines the time by which all computations of a workflow should be complete. Workflows are submitted by numerous clients to a scheduler that assigns workflow requests to a cloud of memory managed multicore machines for execution. A cost function is assumed to be associated with each workflow, which maps values of relative workflow tardiness to corresponding cost function values. A novel cost-minimizing scheduling framework is introduced to schedule requests of workflows so as to minimize the sum of cost function values for all workflows. The utility of the proposed scheduler is compared to another previously known scheduling policy.
NASA Astrophysics Data System (ADS)
Wang, Liuping; Gan, Lu
2013-08-01
Linear controllers with gain scheduling have been successfully used in the control of nonlinear systems for the past several decades. This paper proposes the design of gain scheduled continuous-time model predictive controller with constraints. Using induction machine as an illustrative example, the paper will show the four steps involved in the design of a gain scheduled predictive controller: (i) linearisation of a nonlinear plant according to operating conditions; (ii) the design of linear predictive controllers for the family of linear models; (iii) gain scheduled predictive control law that will optimise a multiple model objective function with constraints, which will also ensure smooth transitions (i.e. bumpless transfer) between the predictive controllers; (iv) experimental validation of the gain scheduled predictive control system with constraints.
Zhao, Chuan-Li; Hsu, Chou-Jung; Hsu, Hua-Feng
2014-01-01
This paper considers single machine scheduling and due date assignment with setup time. The setup time is proportional to the length of the already processed jobs; that is, the setup time is past-sequence-dependent (p-s-d). It is assumed that a job's processing time depends on its position in a sequence. The objective functions include total earliness, the weighted number of tardy jobs, and the cost of due date assignment. We analyze these problems with two different due date assignment methods. We first consider the model with job-dependent position effects. For each case, by converting the problem to a series of assignment problems, we proved that the problems can be solved in O(n(4)) time. For the model with job-independent position effects, we proved that the problems can be solved in O(n(3)) time by providing a dynamic programming algorithm.
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.
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...
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, 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, 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, 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...
Managing magnetic resonance imaging machines: support tools for scheduling and planning.
Carpenter, Adam P; Leemis, Lawrence M; Papir, Alan S; Phillips, David J; Phillips, Grace S
2011-06-01
We devise models and algorithms to estimate the impact of current and future patient demand for examinations on Magnetic Resonance Imaging (MRI) machines at a hospital radiology department. Our work helps improve scheduling decisions and supports MRI machine personnel and equipment planning decisions. Of particular novelty is our use of scheduling algorithms to compute the competing objectives of maximizing examination throughput and patient-magnet utilization. Using our algorithms retrospectively can help (1) assess prior scheduling decisions, (2) identify potential areas of efficiency improvement and (3) identify difficult examination types. Using a year of patient data and several years of MRI utilization data, we construct a simulation model to forecast MRI machine demand under a variety of scenarios. Under our predicted demand model, the throughput calculated by our algorithms acts as an estimate of the overtime MRI time required, and thus, can be used to help predict the impact of different trends in examination demand and to support MRI machine staffing and equipment planning.
Application of Viral Systems for Single-Machine Total Weighted Tardiness Problem
NASA Astrophysics Data System (ADS)
Santosa, Budi; Affandi, Umar
2013-06-01
In this paper, a relatively new algorithm inspired by the viral replication system called Viral Systems is used to solve the Single-Machine Total Weighted Tardiness (SMTWTP). SMTWTP is a job scheduling problem which is one of classical combinatorial problems known as np-hard problems. This algorithm makes the process of finding solutions through neighborhood and mutation mechanism. The experiment was conducted to evaluate its performance. There are seven parameters which are required to tune in to find best solution. The experiment was implemented on data sets of 40 jobs, 50 jobs, and 100 jobs. The results show that the algorithm can solve 235 optimally out of 275 problems.
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.
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.
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
Variable-time reinforcement schedules in the treatment of socially maintained problem behavior.
Van Camp, C M; Lerman, D C; Kelley, M E; Contrucci, S A; Vorndran, C M
2000-01-01
Noncontingent reinforcement (NCR) consists of delivering a reinforcer on a time-based schedule, independent of responding. Studies evaluating the effectiveness of NCR as treatment for problem behavior have used fixed-time (FT) schedules of reinforcement. In this study, the efficacy of NCR with variable-time (VT) schedules was evaluated by comparing the effects of VT and FT reinforcement schedules with 2 individuals who engaged in problem behavior maintained by positive reinforcement. Both FT and VT schedules were effective in reducing problem behavior. These findings suggest that VT schedules can be used to treat problem behavior maintained by social consequences.
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.
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.
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.
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.
Variable-Time Reinforcement Schedules in the Treatment of Socially Maintained Problem Behavior.
ERIC Educational Resources Information Center
Van Camp, Carole M.; Lerman, Dorothea C.; Kelley, Michael E.; Contrucci, Stephanie A.; Vorndran, Christina M.
2000-01-01
The efficacy of noncontingent reinforcement with variable-time (VT) schedules was evaluated by comparing the effects of VT and fixed-time (FT) reinforcement schedules with two individuals with moderate to severe mental retardation and severe behavior problems. Both VT and FT schedules were effective in reducing problem behavior. (Contains…
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.
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.
Work schedule differences in sleep problems of nursing home caregivers.
Takahashi, Masaya; Iwakiri, Kazuyuki; Sotoyama, Midori; Higuchi, Shigekazu; Kiguchi, Masako; Hirata, Mamoru; Hisanaga, Naomi; Kitahara, Teruyo; Taoda, Kazushi; Nishiyama, Katsuo
2008-09-01
Nursing home caregivers (n=775; 604 women; mean age 33.6 years) were studied to examine how work schedules affect their sleep. The shift group (n=536) worked under a rotating two-shift system (n=365), a rotating three-shift system (n=66), or other types of shifts (n=78). The non-shift group included 222 caregivers. Participants completed a questionnaire about working conditions, sleep problems, health, lifestyle, and demographic factors. The two-shift caregivers reported the highest levels of difficulty initiating sleep (DIS, 37.6%), insomnia symptoms (43.0%), and poor quality of sleep (24.9%) among the groups. Adjusted odds ratios for these problems were significantly greater for the two-shift caregivers than for non-shift counterparts: DIS (odds ratio 2.86, 95% confidence interval 1.57-5.20), insomnia symptoms (2.33, 1.36-4.02), and poor sleep quality (2.15, 1.09-4.22). Our data suggest that working under a rotating two-shift system, which has a longer night shift, is associated with an elevated risk of sleep problems for nursing home caregivers.
The school bus routing and scheduling problem with transfers
Doerner, Karl F.; Parragh, Sophie N.
2015-01-01
In this article, we study the school bus routing and scheduling problem with transfers arising in the field of nonperiodic public transportation systems. It deals with the transportation of pupils from home to their school in the morning taking the possibility that pupils may change buses into account. Allowing transfers has several consequences. On the one hand, it allows more flexibility in the bus network structure and can, therefore, help to reduce operating costs. On the other hand, transfers have an impact on the service level: the perceived service quality is lower due to the existence of transfers; however, at the same time, user ride times may be reduced and, thus, transfers may also have a positive impact on service quality. The main objective is the minimization of the total operating costs. We develop a heuristic solution framework to solve this problem and compare it with two solution concepts that do not consider transfers. The impact of transfers on the service level in terms of time loss (or user ride time) and the number of transfers is analyzed. Our results show that allowing transfers reduces total operating costs significantly while average and maximum user ride times are comparable to solutions without transfers. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 65(2), 180–203 2015 PMID:28163329
The school bus routing and scheduling problem with transfers.
Bögl, Michael; Doerner, Karl F; Parragh, Sophie N
2015-03-01
In this article, we study the school bus routing and scheduling problem with transfers arising in the field of nonperiodic public transportation systems. It deals with the transportation of pupils from home to their school in the morning taking the possibility that pupils may change buses into account. Allowing transfers has several consequences. On the one hand, it allows more flexibility in the bus network structure and can, therefore, help to reduce operating costs. On the other hand, transfers have an impact on the service level: the perceived service quality is lower due to the existence of transfers; however, at the same time, user ride times may be reduced and, thus, transfers may also have a positive impact on service quality. The main objective is the minimization of the total operating costs. We develop a heuristic solution framework to solve this problem and compare it with two solution concepts that do not consider transfers. The impact of transfers on the service level in terms of time loss (or user ride time) and the number of transfers is analyzed. Our results show that allowing transfers reduces total operating costs significantly while average and maximum user ride times are comparable to solutions without transfers. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 65(2), 180-203 2015.
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.
A coherent Ising machine for 2000-node optimization problems
NASA Astrophysics Data System (ADS)
Inagaki, Takahiro; Haribara, Yoshitaka; Igarashi, Koji; Sonobe, Tomohiro; Tamate, Shuhei; Honjo, Toshimori; Marandi, Alireza; McMahon, Peter L.; Umeki, Takeshi; Enbutsu, Koji; Tadanaga, Osamu; Takenouchi, Hirokazu; Aihara, Kazuyuki; Kawarabayashi, Ken-ichi; Inoue, Kyo; Utsunomiya, Shoko; Takesue, Hiroki
2016-11-01
The analysis and optimization of complex systems can be reduced to mathematical problems collectively known as combinatorial optimization. Many such problems can be mapped onto ground-state search problems of the Ising model, and various artificial spin systems are now emerging as promising approaches. However, physical Ising machines have suffered from limited numbers of spin-spin couplings because of implementations based on localized spins, resulting in severe scalability problems. We report a 2000-spin network with all-to-all spin-spin couplings. Using a measurement and feedback scheme, we coupled time-multiplexed degenerate optical parametric oscillators to implement maximum cut problems on arbitrary graph topologies with up to 2000 nodes. Our coherent Ising machine outperformed simulated annealing in terms of accuracy and computation time for a 2000-node complete graph.
An Analysis of Robust Workforce Scheduling Models for a Nurse Rostering Problem
2007-03-01
12 Moz and Pato ...problem and the nurse rerostering problem. The nurse rostering problem has received much attention in the staff scheduling literature (Moz and Pato , 2007...Most recently, Moz and Pato (2007) developed constructive heuristics and genetic algorithms to re-roster a schedule following a disruption. Their use
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.
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 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.
A New Lagrangian Relaxation Method Considering Previous Hour Scheduling for Unit Commitment Problem
NASA Astrophysics Data System (ADS)
Khorasani, H.; Rashidinejad, M.; Purakbari-Kasmaie, M.; Abdollahi, A.
2009-08-01
Generation scheduling is a crucial challenge in power systems especially under new environment of liberalization of electricity industry. A new Lagrangian relaxation method for unit commitment (UC) has been presented for solving generation scheduling problem. This paper focuses on the economical aspect of UC problem, while the previous hour scheduling as a very important issue is studied. In this paper generation scheduling of present hour has been conducted by considering the previous hour scheduling. The impacts of hot/cold start-up cost have been taken in to account in this paper. Case studies and numerical analysis presents significant outcomes while it demonstrates the effectiveness of the proposed method.
Problems in modeling man machine control behavior in biodynamic environments
NASA Technical Reports Server (NTRS)
Jex, H. R.
1972-01-01
Reviewed are some current problems in modeling man-machine control behavior in a biodynamic environment. It is given in two parts: (1) a review of the models which are appropriate for manual control behavior and the added elements necessary to deal with biodynamic interfaces; and (2) a review of some biodynamic interface pilot/vehicle problems which have occurred, been solved, or need to be solved.
Full glowworm swarm optimization algorithm for whole-set orders scheduling in single machine.
Yu, Zhang; Yang, Xiaomei
2013-01-01
By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency.
Research on remanufacturing scheduling problem based on critical chain management
NASA Astrophysics Data System (ADS)
Cui, Y.; Guan, Z.; He, C.; Yue, L.
2017-06-01
Remanufacturing is the recycling process of waste products as “as good as new products”, compared with materials recycling, remanufacturing represents a higher form of recycling. The typical structure of remanufacturing system consists of three parts: disassembly workshop, remanufacturing workshop and assembly workshop. However, the management of production planning and control activities can differ greatly from management activities in traditional manufacturing. Scheduling in a remanufacturing environment is more complex and the scheduler must deal with more uncertainty than in a traditional manufacturing environment. In order to properly schedule in a remanufacturing environment the schedule must be able to cope with several complicating factors which increase variability. This paper introduced and discussed seven complicating characteristics that require significant changes in production planning and control activities, in order to provide a new method for remanufacturing production scheduling system.
NASA Astrophysics Data System (ADS)
Tseng, Chao-Tang; Chen, Kuan-Han
2013-12-01
In recent years, a new type of tardiness cost, called stepwise tardiness, has received attention. To the authors' knowledge, only a few studies have investigated this type of tardiness in the scheduling problem. This study considered the single machine total stepwise tardiness problem with release dates, which is strongly NP hard. Because of the essential complexity of the problem, heuristics were first developed to quickly generate initial solutions. Subsequently, a new electromagnetism-like mechanism (EM), which is a novel metaheuristic, was proposed to improve the solution quality. The new EM includes a natural encoding scheme, a new distance measure between solutions, and effective attraction and repulsion operators. Comparisons with a current EM and other metaheuristics were performed to verify the proposed EM. The computational results show that the proposed EM exhibits good performance for the considered problem.
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 ?.
Nonlinear programming for classification problems in machine learning
NASA Astrophysics Data System (ADS)
Astorino, Annabella; Fuduli, Antonio; Gaudioso, Manlio
2016-10-01
We survey some nonlinear models for classification problems arising in machine learning. In the last years this field has become more and more relevant due to a lot of practical applications, such as text and web classification, object recognition in machine vision, gene expression profile analysis, DNA and protein analysis, medical diagnosis, customer profiling etc. Classification deals with separation of sets by means of appropriate separation surfaces, which is generally obtained by solving a numerical optimization model. While linear separability is the basis of the most popular approach to classification, the Support Vector Machine (SVM), in the recent years using nonlinear separating surfaces has received some attention. The objective of this work is to recall some of such proposals, mainly in terms of the numerical optimization models. In particular we tackle the polyhedral, ellipsoidal, spherical and conical separation approaches and, for some of them, we also consider the semisupervised versions.
Second-order schedules and the problem of conditioned reinforcement
Stubbs, D. Alan
1971-01-01
Thirteen pigeons were exposed to a variety of second-order schedules in which responding under a component schedule was reinforced according to a schedule of reinforcement. Under different conditions, completion of each component resulted in either (1) the brief presentation of a stimulus also present during reinforcement (pairing operation), (2) the brief presentation of a stimulus not present during reinforcement (nonpairing operation), or (3) no brief stimulus presentation (tandem). Brief-stimulus presentations engendered a pattern of responding within components similar to that engendered by food. Patterning was observed when fixed-interval and fixed-ratio components were maintained under fixed- and variable-ratio and fixed- and variable-interval schedules. There were no apparent differences in performance under pairing and nonpairing conditions in any study. The properties of the stimuli presented in brief-stimulus operations produced different effects on response patterning. In one study, similar effects on performance were found whether brief-stimulus presentations were response-produced or delivered independently of responding. Response patterning did not occur when the component schedule under which a nonpaired stimulus was produced occurred independently of the food schedule. The results suggest a reevaluation of the role of conditioned reinforcement in second-order schedule performance. The similarity of behavior under pairing and nonpairing operations is consistent with two hypotheses: (1) the major effect is due to the discriminative properties of the brief stimulus; (2) the scheduling operation under which the paired or nonpaired stimulus is presented can establish it as a reinforcer. PMID:16811549
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.
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
Producing Satisfactory Solutions to Scheduling Problems: An Iterative Constraint Relaxation Approach
NASA Technical Reports Server (NTRS)
Chien, S.; Gratch, J.
1994-01-01
One drawback to using constraint-propagation in planning and scheduling systems is that when a problem has an unsatisfiable set of constraints such algorithms typically only show that no solution exists. While, technically correct, in practical situations, it is desirable in these cases to produce a satisficing solution that satisfies the most important constraints (typically defined in terms of maximizing a utility function). This paper describes an iterative constraint relaxation approach in which the scheduler uses heuristics to progressively relax problem constraints until the problem becomes satisfiable. We present empirical results of applying these techniques to the problem of scheduling spacecraft communications for JPL/NASA antenna resources.
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.
Single-machine scheduling to minimize total completion time and tardiness with two competing agents.
Lee, Wen-Chiung; Shiau, Yau-Ren; Chung, Yu-Hsiang; Ding, Lawson
2014-01-01
We consider a single-machine two-agent problem where the objective is to minimize a weighted combination of the total completion time and the total tardiness of jobs from the first agent given that no tardy jobs are allowed for the second agent. A branch-and-bound algorithm is developed to derive the optimal sequence and two simulated annealing heuristic algorithms are proposed to search for the near-optimal solutions. Computational experiments are also conducted to evaluate the proposed branch-and-bound and simulated annealing algorithms.
Single-Machine Scheduling to Minimize Total Completion Time and Tardiness with Two Competing Agents
Shiau, Yau-Ren; Chung, Yu-Hsiang; Ding, Lawson
2014-01-01
We consider a single-machine two-agent problem where the objective is to minimize a weighted combination of the total completion time and the total tardiness of jobs from the first agent given that no tardy jobs are allowed for the second agent. A branch-and-bound algorithm is developed to derive the optimal sequence and two simulated annealing heuristic algorithms are proposed to search for the near-optimal solutions. Computational experiments are also conducted to evaluate the proposed branch-and-bound and simulated annealing algorithms. PMID:24574901
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.
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 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.
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.
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.
On scheduling models for the frequency interval assignment problem with cumulative interferences
NASA Astrophysics Data System (ADS)
Kiatmanaroj, Kata; Artigues, Christian; Houssin, Laurent
2016-05-01
In this article, models and methods for solving a real-life frequency assignment problem based on scheduling theory are investigated. A realistic frequency assignment problem involving cumulative interference constraints in which the aim is to maximize the number of assigned users is considered. If interferences are assumed to be binary, a multiple carrier frequency assignment problem can be treated as a disjunctive scheduling problem since a user requesting a number of contiguous frequencies can be considered as a non-preemptive task with a processing time, and two interfering users can be modelled through a disjunctive constraint on the corresponding tasks. A binary interference version of the problem is constructed and a disjunctive scheduling model is derived. Based on the binary representation, two models are proposed. The first one relies on an interference matrix and the second one considers maximal cliques. A third, cumulative, model that yields a new class of scheduling problems is also proposed. Computational experiments show that the case-study frequency assignment problem can be solved efficiently with disjunctive scheduling techniques.
Wang, Zhaocai; Ji, Zuwen; Wang, Xiaoming; Wu, Tunhua
2017-09-07
As a promising approach to solve the computationally intractable problem, the method based on DNA computing is an emerging research area including mathematics, computer science and molecular biology. The task scheduling problem, as a well-known NP-complete problem, arranges n jobs to m individuals and finds the minimum execution time of last finished individual. In this paper, we use a biologically inspired computational model and describe a new parallel algorithm to solve the task scheduling problem by basic DNA molecular operations. In turn, we skillfully design flexible length DNA strands to represent elements of the allocation matrix, take appropriate biological experiment operations and get solutions of the task scheduling problem in proper length range with less than O(n(2)) time complexity. Copyright © 2017. Published by Elsevier B.V.
Solving nonstationary classification problems with coupled support vector machines.
Grinblat, Guillermo L; Uzal, Lucas C; Ceccatto, H Alejandro; Granitto, Pablo M
2011-01-01
Many learning problems may vary slowly over time: in particular, some critical real-world applications. When facing this problem, it is desirable that the learning method could find the correct input-output function and also detect the change in the concept and adapt to it. We introduce the time-adaptive support vector machine (TA-SVM), which is a new method for generating adaptive classifiers, capable of learning concepts that change with time. The basic idea of TA-SVM is to use a sequence of classifiers, each one appropriate for a small time window but, in contrast to other proposals, learning all the hyperplanes in a global way. We show that the addition of a new term in the cost function of the set of SVMs (that penalizes the diversity between consecutive classifiers) produces a coupling of the sequence that allows TA-SVM to learn as a single adaptive classifier. We evaluate different aspects of the method using appropriate drifting problems. In particular, we analyze the regularizing effect of changing the number of classifiers in the sequence or adapting the strength of the coupling. A comparison with other methods in several problems, including the well-known STAGGER dataset and the real-world electricity pricing domain, shows the good performance of TA-SVM in all tested situations.
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…
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…
CPAP Machines: Tips for Avoiding 10 Common Problems
... for obstructive sleep apnea. It includes a small machine that supplies a constant and steady air pressure, ... this by using a "ramp" feature on the machine. This feature allows you to start with low ...
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.
Parallel job-scheduling algorithms
Rodger, S.H.
1989-01-01
In this thesis, we consider solving job scheduling problems on the CREW PRAM model. We show how to adapt Cole's pipeline merge technique to yield several efficient parallel algorithms for a number of job scheduling problems and one optimal parallel algorithm for the following job scheduling problem: Given a set of n jobs defined by release times, deadlines and processing times, find a schedule that minimizes the maximum lateness of the jobs and allows preemption when the jobs are scheduled to run on one machine. In addition, we present the first NC algorithm for the following job scheduling problem: Given a set of n jobs defined by release times, deadlines and unit processing times, determine if there is a schedule of jobs on one machine, and calculate the schedule if it exists. We identify the notion of a canonical schedule, which is the type of schedule our algorithm computes if there is a schedule. Our algorithm runs in O((log n){sup 2}) time and uses O(n{sup 2}k{sup 2}) processors, where k is the minimum number of distinct offsets of release times or deadlines.
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.
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,…
NASA Astrophysics Data System (ADS)
Noori-Darvish, Samaneh; Tavakkoli-Moghaddam, Reza
2012-10-01
We consider an open shop scheduling problem with setup and processing times separately such that not only the setup times are dependent on the machines, but also they are dependent on the sequence of jobs that should be processed on a machine. A novel bi-objective mathematical programming is designed in order to minimize the total tardiness and the makespan. Among several multi-objective decision making (MODM) methods, an interactive one, called the TH method is applied for solving small-sized instances optimally and obtaining Pareto-optimal solutions by the Lingo software. To achieve Pareto-optimal sets for medium to large-sized problems, an improved non-dominated sorting genetic algorithm II (NSGA-II) is presented that consists of a heuristic method for obtaining a good initial population. In addition, by using the design of experiments (DOE), the efficiency of the proposed improved NSGA-II is compared with the efficiency of a well-known multi-objective genetic algorithm, namely SPEA-II. Finally, the performance of the improved NSGA-II is examined in a comparison with the performance of the traditional NSGA-II.
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.
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 activities. Problem behavior decreased for both participants when extinction and DRO were introduced, regardless of whether visual schedules were also used.
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.
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.…
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.
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…
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…
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…
Dynamic Scheduling of a Multi-Class Queue I: Problem Formulation and Descriptive Results.
interest rate . The problem is to decide, at the completion of each service and given the state of the system, which class to admit next. The objective is to maximize expected net present value over an infinite planning horizon. The problem is formulated as a Markov renewal decision process. One very special type of scheduling rule, called a static policy, simply enforces a specified priority ranking. The return function under a static policy is explicity
NASA Astrophysics Data System (ADS)
Wang, Deyun; Grunder, Olivier; EL Moudni, Abdellah
2014-08-01
This paper considers an integrated lot sizing and scheduling problem for a production-distribution environment with arbitrary job volumes and distinct due dates considerations. In the problem, jobs are firstly batch processed on a batching machine at production stage and then delivered to a pre-specified customer at the subsequent delivery stage by a capacitated vehicle. Each job is associated with a distinct due date and a distinct volume, and has to be delivered to the customer before its due date, i.e. delay is not allowed. The processing time of a batch is a constant independent of the jobs it contains. In production, a constant set-up time as well as a constant set-up cost is required before the first job of this batch is processed. In delivery, a constant delivery time as well as a constant delivery cost is needed for each round-trip delivery between the factory and the customer. Moreover, it is supposed that a job that arrives at the customer before its due date will incur a customer inventory cost. The objective is to find a coordinated lot sizing and scheduling scheme such that the total cost is minimised while guaranteeing a certain customer service level. A mixed integer formulation is proposed for this problem, and then a genetic algorithm is developed to solve it. To evaluate the performance of the proposed genetic algorithm, a lower bound on the objective value is established. Computational experiments show that the proposed genetic algorithm performs well on randomly generated problem instances.
An Algorithm for the Weighted Earliness-Tardiness Unconstrained Project Scheduling Problem
NASA Astrophysics Data System (ADS)
Afshar Nadjafi, Behrouz; Shadrokh, Shahram
This research considers a project scheduling problem with the object of minimizing weighted earliness-tardiness penalty costs, taking into account a deadline for the project and precedence relations among the activities. An exact recursive method has been proposed for solving the basic form of this problem. We present a new depth-first branch and bound algorithm for extended form of the problem, which time value of money is taken into account by discounting the cash flows. The algorithm is extended with two bounding rules in order to reduce the size of the branch and bound tree. Finally, some test problems are solved and computational results are reported.
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
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.
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
Feyock, Anthony J.
This paper presents an analysis of the job performed by an offset press operator (alternate title is offset duplicating machine operator) of a Multilith 1250 W. First covered is work performed, as follows: prepares dampening unit for printing run, prepares inking unit for printing, readies printing plate for printing, sets up press for running,…
ERIC Educational Resources Information Center
Feyock, Anthony J.
This paper presents an analysis of the job performed by an offset press operator (alternate title is offset duplicating machine operator) of a Multilith 1250 W. First covered is work performed, as follows: prepares dampening unit for printing run, prepares inking unit for printing, readies printing plate for printing, sets up press for running,…
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…
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.
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.
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.
Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun
2014-01-01
A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method. PMID:24772031
Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun
2014-01-01
A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method.
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.
Effective Iterated Greedy Algorithm for Flow-Shop Scheduling Problems with Time lags
NASA Astrophysics Data System (ADS)
ZHAO, Ning; YE, Song; LI, Kaidian; CHEN, Siyu
2017-03-01
Flow shop scheduling problem with time lags is a practical scheduling problem and attracts many studies. Permutation problem(PFSP with time lags) is concentrated but non-permutation problem(non-PFSP with time lags) seems to be neglected. With the aim to minimize the makespan and satisfy time lag constraints, efficient algorithms corresponding to PFSP and non-PFSP problems are proposed, which consist of iterated greedy algorithm for permutation(IGTLP) and iterated greedy algorithm for non-permutation (IGTLNP). The proposed algorithms are verified using well-known simple and complex instances of permutation and non-permutation problems with various time lag ranges. The permutation results indicate that the proposed IGTLP can reach near optimal solution within nearly 11% computational time of traditional GA approach. The non-permutation results indicate that the proposed IG can reach nearly same solution within less than 1% computational time compared with traditional GA approach. The proposed research combines PFSP and non-PFSP together with minimal and maximal time lag consideration, which provides an interesting viewpoint for industrial implementation.
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
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.
New scheduling rules for a dynamic flexible flow line problem with sequence-dependent setup times
NASA Astrophysics Data System (ADS)
Kia, Hamidreza; Ghodsypour, Seyed Hassan; Davoudpour, Hamid
2017-01-01
In the literature, the application of multi-objective dynamic scheduling problem and simple priority rules are widely studied. Although these rules are not efficient enough due to simplicity and lack of general insight, composite dispatching rules have a very suitable performance because they result from experiments. In this paper, a dynamic flexible flow line problem with sequence-dependent setup times is studied. The objective of the problem is minimization of mean flow time and mean tardiness. A 0-1 mixed integer model of the problem is formulated. Since the problem is NP-hard, four new composite dispatching rules are proposed to solve it by applying genetic programming framework and choosing proper operators. Furthermore, a discrete-event simulation model is made to examine the performances of scheduling rules considering four new heuristic rules and the six adapted heuristic rules from the literature. It is clear from the experimental results that composite dispatching rules that are formed from genetic programming have a better performance in minimization of mean flow time and mean tardiness than others.
New scheduling rules for a dynamic flexible flow line problem with sequence-dependent setup times
NASA Astrophysics Data System (ADS)
Kia, Hamidreza; Ghodsypour, Seyed Hassan; Davoudpour, Hamid
2017-01-01
In the literature, the application of multi-objective dynamic scheduling problem and simple priority rules are widely studied. Although these rules are not efficient enough due to simplicity and lack of general insight, composite dispatching rules have a very suitable performance because they result from experiments. In this paper, a dynamic flexible flow line problem with sequence-dependent setup times is studied. The objective of the problem is minimization of mean flow time and mean tardiness. A 0-1 mixed integer model of the problem is formulated. Since the problem is NP-hard, four new composite dispatching rules are proposed to solve it by applying genetic programming framework and choosing proper operators. Furthermore, a discrete-event simulation model is made to examine the performances of scheduling rules considering four new heuristic rules and the six adapted heuristic rules from the literature. It is clear from the experimental results that composite dispatching rules that are formed from genetic programming have a better performance in minimization of mean flow time and mean tardiness than others.
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.
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.
Wingfield, Cai; Su, Li; Liu, Xunying; Zhang, Chao; Woodland, Phil; Thwaites, Andrew; Fonteneau, Elisabeth; Marslen-Wilson, William D
2017-09-01
There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.
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.
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.
Three-Stage Tabu Search for Solving Large-Scale Flow Shop Scheduling Problems
NASA Astrophysics Data System (ADS)
Xu, Yuedong; Tian, Yajie; Sannomiya, Nobuo
Tabu search is a meta-heuristic approach designed skillfully for finding a suboptimal solution of combinatorial optimization problems. In this paper the tabu search with three stages is proposed for solving large-scale flow shop scheduling problems. In order to obtain a better suboptimal solution in a short computation time, three different candidate lists are used to determine the incumbent solution in the respective search stages. The candidate lists are constructed by restricting the moving of each job. Test problems with four kinds of job data are examined. Based on analyzing the relationship between the candidate list and the suboptimal solution for each job data, a common parameter is given to construct the candidate list during the search process. Comparison of the computation result is made with the genetic algorithm and the basic tabu search, from which it is shown that the proposed tabu search outperforms two others.
A hybrid water flow algorithm for multi-objective flexible flow shop scheduling problems
NASA Astrophysics Data System (ADS)
Hieu Tran, Trung; Ng, Kien Ming
2013-04-01
In this article, the multi-objective flexible flow shop scheduling problem with limited intermediate buffers is addressed. The objectives considered in this problem consist of minimizing the completion time of jobs and minimizing the total tardiness time of jobs. A hybrid water flow algorithm for solving this problem is proposed. Landscape analysis is performed to determine the weights of objective functions, which guide the exploration of feasible regions and movement towards the optimal Pareto solution set. Local and global neighbourhood structures are integrated in the erosion process of the algorithm, while evaporation and precipitation processes are included to enhance the solution exploitation capability of the algorithm in unexplored neighbouring regions. An improvement process is used to reinforce the final Pareto solution set obtained. The performance of the proposed algorithm is tested with benchmark and randomly generated instances. The computational results and comparisons demonstrate the effectiveness and efficiency of the proposed algorithm.
An estimation of distribution algorithm (EDA) variant with QGA for Flowshop scheduling problem
NASA Astrophysics Data System (ADS)
Latif, Muhammad Shahid; Hong, Zhou; Ali, Amir
2014-04-01
In this research article, a hybrid approach is presented which based on well-known meta-heuristics algorithms. This study based on integration of Quantum Genetic Algorithm (QGA) and Estimation of Distribution Algorithm, EDA, (for simplicity we use Q-EDA) for flowshop scheduling, a well-known NP hard Problem, while focusing on the total flow time minimization criterion. A relatively new method has been adopted for the encoding of jobs sequence in flowshop known as angel rotations instead of random keys, so QGA become more efficient. Further, EDA has been integrated to update the population of QGA by making a probability model. This probabilistic model is built and used to generate new candidate solutions which comprised on best individuals, obtained after several repetitions of proposed (Q-EDA) approach. As both heuristics based on probabilistic characteristics, so exhibits excellent learning capability and have minimum chances of being trapped in local optima. The results obtained during this study are presented and compared with contemporary approaches in literature. The current hybrid Q-EDA has implemented on different benchmark problems. The experiments has showed better convergence and results. It is concluded that hybrid Q-EDA algorithm can generally produce better results while implemented for Flowshop Scheduling Problem (FSSP).
A PSO-based hybrid metaheuristic for permutation flowshop scheduling problems.
Zhang, Le; Wu, Jinnan
2014-01-01
This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.
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.
An Extreme Learning Machine Approach to Density Estimation Problems.
Cervellera, Cristiano; Maccio, Danilo
2017-01-17
In this paper, we discuss how the extreme learning machine (ELM) framework can be effectively employed in the unsupervised context of multivariate density estimation. In particular, two algorithms are introduced, one for the estimation of the cumulative distribution function underlying the observed data, and one for the estimation of the probability density function. The algorithms rely on the concept of $F$-discrepancy, which is closely related to the Kolmogorov-Smirnov criterion for goodness of fit. Both methods retain the key feature of the ELM of providing the solution through random assignment of the hidden feature map and a very light computational burden. A theoretical analysis is provided, discussing convergence under proper hypotheses on the chosen activation functions. Simulation tests show how ELMs can be successfully employed in the density estimation framework, as a possible alternative to other standard methods.
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.
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.
Processing time tolerance-based ACO algorithm for solving job-shop scheduling problem
NASA Astrophysics Data System (ADS)
Luo, Yabo; Waden, Yongo P.
2017-06-01
Ordinarily, Job Shop Scheduling Problem (JSSP) is known as NP-hard problem which has uncertainty and complexity that cannot be handled by a linear method. Thus, currently studies on JSSP are concentrated mainly on applying different methods of improving the heuristics for optimizing the JSSP. However, there still exist many problems for efficient optimization in the JSSP, namely, low efficiency and poor reliability, which can easily trap the optimization process of JSSP into local optima. Therefore, to solve this problem, a study on Ant Colony Optimization (ACO) algorithm combined with constraint handling tactics is carried out in this paper. Further, the problem is subdivided into three parts: (1) Analysis of processing time tolerance-based constraint features in the JSSP which is performed by the constraint satisfying model; (2) Satisfying the constraints by considering the consistency technology and the constraint spreading algorithm in order to improve the performance of ACO algorithm. Hence, the JSSP model based on the improved ACO algorithm is constructed; (3) The effectiveness of the proposed method based on reliability and efficiency is shown through comparative experiments which are performed on benchmark problems. Consequently, the results obtained by the proposed method are better, and the applied technique can be used in optimizing JSSP.
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 α=.87), item analysis revealed one weak item ('Objectional habits') with item-total biserial correlation of only .20. An exploratory factor analysis revealed two main factors. The first factor consisted of items relating to disruptive/distractive problems. The second factor consisted of items relating to antisocial/delinquent problems. Disruptive/distractive problems were specifically associated with low ID level. Antisocial/delinquent behaviors were specifically associated with male gender, schizophrenia, hospital admission and troubles with police. For patients who had both disruptive/distractive problems and antisocial/delinquent behaviors, personality disorders and autism were more frequent, where as anxiety and depression were less frequent. On the basis of the obtained results, two new DAS subscales for assessing challenging behavior were proposed. Both subscales had good levels of internal consistency, as well as face and criterion validity. Overall, the new DAS subscales were shown to have acceptable psychometric properties and have therefore potential for use in both research and clinical practice. Copyright © 2010 Elsevier Ltd. All rights reserved.
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 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…
Quantum algorithms for biomolecular solutions of the satisfiability problem on a quantum machine.
Chang, Weng-Long; Ren, Ting-Ting; Luo, Jun; Feng, Mang; Guo, Minyi; Weicheng Lin, Kawuu
2008-09-01
In this paper, we demonstrate that the logic computation performed by the DNA-based algorithm for solving general cases of the satisfiability problem can be implemented more efficiently by our proposed quantum algorithm on the quantum machine proposed by Deutsch. To test our theory, we carry out a three-quantum bit nuclear magnetic resonance experiment for solving the simplest satisfiability problem.
On the integrability of the motion of 3D-Swinging Atwood machine and related problems
NASA Astrophysics Data System (ADS)
Elmandouh, A. A.
2016-03-01
In the present article, we study the problem of the motion of 3D- Swinging Atwood machine. A new integrable case for this problem is announced. We point out a new integrable case describing the motion of a heavy particle on a titled cone.
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.
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.
NASA Astrophysics Data System (ADS)
Wang, Chun; Ji, Zhicheng; Wang, Yan
2017-07-01
In this paper, multi-objective flexible job shop scheduling problem (MOFJSP) was studied with the objects to minimize makespan, total workload and critical workload. A variable neighborhood evolutionary algorithm (VNEA) was proposed to obtain a set of Pareto optimal solutions. First, two novel crowded operators in terms of the decision space and object space were proposed, and they were respectively used in mating selection and environmental selection. Then, two well-designed neighborhood structures were used in local search, which consider the problem characteristics and can hold fast convergence. Finally, extensive comparison was carried out with the state-of-the-art methods specially presented for solving MOFJSP on well-known benchmark instances. The results show that the proposed VNEA is more effective than other algorithms in solving MOFJSP.
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
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.
Åkerstedt, Torbjörn; Kecklund, Göran
2017-03-01
The purpose was to investigate which detailed characteristics of shift schedules that are seen as problems to those exposed. A representative national sample of non-day workers (N = 2031) in Sweden was asked whether they had each of a number of particular work schedule characteristics and, if yes, to what extent this constituted a "big problem in life". It was also inquired whether the individual's work schedules had negative consequences for fatigue, sleep and social life. The characteristic with the highest percentage reporting a big problem was "short notice (<1 month) of a new work schedule" (30.5%), <11 h off between shifts (27.8%), and split duty (>1.5 h break at mid-shift, 27.2%). Overtime (>10 h/week), night work, morning work, day/night shifts showed lower prevalences of being a "big problem". Women indicated more problems in general. Short notice was mainly related to negative social effects, while <11 h off between shifts was related to disturbed sleep, fatigue and social difficulties. It was concluded that schedules involving unpredictable working hours (short notice), short daily rest between shifts, and split duty shifts constitute big problems. The results challenge current views of what aspects of shift work need improvement, and negative social consequences seem more important than those related to health.
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
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.
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)
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
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)
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.
Yang, Xin; Zeng, Zhenxiang; Wang, Ruidong; Sun, Xueshan
2016-01-01
This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems.
Three hybridization models based on local search scheme for job shop scheduling problem
NASA Astrophysics Data System (ADS)
Balbi Fraga, Tatiana
2015-05-01
This work presents three different hybridization models based on the general schema of Local Search Heuristics, named Hybrid Successive Application, Hybrid Neighborhood, and Hybrid Improved Neighborhood. Despite similar approaches might have already been presented in the literature in other contexts, in this work these models are applied to analyzes the solution of the job shop scheduling problem, with the heuristics Taboo Search and Particle Swarm Optimization. Besides, we investigate some aspects that must be considered in order to achieve better solutions than those obtained by the original heuristics. The results demonstrate that the algorithms derived from these three hybrid models are more robust than the original algorithms and able to get better results than those found by the single Taboo Search.
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.
Zeng, Zhenxiang; Wang, Ruidong; Sun, Xueshan
2016-01-01
This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems. PMID:27907163
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.
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.
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)
Chen, Miawjane; Yan, Shangyao; Wang, Sin-Siang; Liu, Chiu-Lan
2015-02-01
An effective project schedule is essential for enterprises to increase their efficiency of project execution, to maximize profit, and to minimize wastage of resources. Heuristic algorithms have been developed to efficiently solve the complicated multi-mode resource-constrained project scheduling problem with discounted cash flows (MRCPSPDCF) that characterize real problems. However, the solutions obtained in past studies have been approximate and are difficult to evaluate in terms of optimality. In this study, a generalized network flow model, embedded in a time-precedence network, is proposed to formulate the MRCPSPDCF with the payment at activity completion times. Mathematically, the model is formulated as an integer network flow problem with side constraints, which can be efficiently solved for optimality, using existing mathematical programming software. To evaluate the model performance, numerical tests are performed. The test results indicate that the model could be a useful planning tool for project scheduling in the real world.
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.
Li, Shanlin; Li, Maoqin
2015-01-01
We consider an integrated production and distribution scheduling problem faced by a typical make-to-order manufacturer which relies on a third-party logistics (3PL) provider for finished product delivery to customers. In the beginning of a planning horizon, the manufacturer has received a set of orders to be processed on a single production line. Completed orders are delivered to customers by a finite number of vehicles provided by the 3PL company which follows a fixed daily or weekly shipping schedule such that the vehicles have fixed departure dates which are not part of the decisions. The problem is to find a feasible schedule that minimizes one of the following objective functions when processing times and weights are oppositely ordered: (1) the total weight of late orders and (2) the number of vehicles used subject to the condition that the total weight of late orders is minimum. We show that both problems are solvable in polynomial time. PMID:25785285
Heuristic algorithms for solving of the tool routing problem for CNC cutting machines
NASA Astrophysics Data System (ADS)
Chentsov, P. A.; Petunin, A. A.; Sesekin, A. N.; Shipacheva, E. N.; Sholohov, A. E.
2015-11-01
The article is devoted to the problem of minimizing the path of the cutting tool to shape cutting machines began. This problem can be interpreted as a generalized traveling salesman problem. Earlier version of the dynamic programming method to solve this problem was developed. Unfortunately, this method allows to process an amount not exceeding thirty circuits. In this regard, the task of constructing quasi-optimal route becomes relevant. In this paper we propose options for quasi-optimal greedy algorithms. Comparison of the results of exact and approximate algorithms is given.
An Effective Evolutionary Hybrid for Solving the Permutation Flowshop Scheduling Problem.
Amirghasemi, Mehrdad; Zamani, Reza
2017-01-01
This paper presents an effective evolutionary hybrid for solving the permutation flowshop scheduling problem. Based on a memetic algorithm, the procedure uses a construction component that generates initial solutions through the use of a novel reblocking mechanism operating according to a biased random sampling technique. This component is aimed at forcing the operations having smaller processing times to appear on the critical path. The goal of the construction component is to fill an initial pool with high-quality solutions for a memetic algorithm that looks for even higher-quality solutions. In the memetic algorithm, whenever a crossover operator and possibly a mutation are performed, the offspring genome is fine-tuned by a combination of 2-exchange swap and insertion local searches. The same with the employed construction method; in these local searches, the critical path notion has been used to exploit the structure of the problem. The results of computational experiments on the benchmark instances indicate that these components have strong synergy, and their integration has created a robust and effective procedure that outperforms several state-of-the-art procedures on a number of the benchmark instances. By deactivating different components enhancing the evolutionary module of the procedure, the effects of these components have also been examined.
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.
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.
NASA Astrophysics Data System (ADS)
Motoyama, H.
1989-05-01
The present situations of superconducting electric power machines in the world and studied problems were investigated from viewpoint of the electric insulation. 50MVA generator (CRIE/Hitachi) or 120MVA generator (KWU/Siemens) where the dc superconducting technique was applied on field windings, are developed. As to Superconducting transformer, 220KVA transformer is trially manufactured and the conceptual design of 1,000MVA transformer is made by W.H. or Alstom. Future problems are the study of protecting method for the overvoltage to superconducting electric power machines and the study to prevent the quench for superconducting windings. The respective insulating characteristics of solid and liquid insulators become clear gradually under the cryogenic condition but a large part of insulating characteristics of composite insulator prepared by combination of both insulators are not clear, so that these problems must be clarified.
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.
Application of the SNoW machine learning paradigm to a set of transportation imaging problems
NASA Astrophysics Data System (ADS)
Paul, Peter; Burry, Aaron M.; Wang, Yuheng; Kozitsky, Vladimir
2012-01-01
Machine learning methods have been successfully applied to image object classification problems where there is clear distinction between classes and where a comprehensive set of training samples and ground truth are readily available. The transportation domain is an area where machine learning methods are particularly applicable, since the classification problems typically have well defined class boundaries and, due to high traffic volumes in most applications, massive roadway data is available. Though these classes tend to be well defined, the particular image noises and variations can be challenging. Another challenge is the extremely high accuracy typically required in most traffic applications. Incorrect assignment of fines or tolls due to imaging mistakes is not acceptable in most applications. For the front seat vehicle occupancy detection problem, classification amounts to determining whether one face (driver only) or two faces (driver + passenger) are detected in the front seat of a vehicle on a roadway. For automatic license plate recognition, the classification problem is a type of optical character recognition problem encompassing multiple class classification. The SNoW machine learning classifier using local SMQT features is shown to be successful in these two transportation imaging applications.
An element search ant colony technique for solving virtual machine placement problem
NASA Astrophysics Data System (ADS)
Srija, J.; Rani John, Rose; Kanaga, Grace Mary, Dr.
2017-09-01
The data centres in the cloud environment play a key role in providing infrastructure for ubiquitous computing, pervasive computing, mobile computing etc. This computing technique tries to utilize the available resources in order to provide services. Hence maintaining the resource utilization without wastage of power consumption has become a challenging task for the researchers. In this paper we propose the direct guidance ant colony system for effective mapping of virtual machines to the physical machine with maximal resource utilization and minimal power consumption. The proposed algorithm has been compared with the existing ant colony approach which is involved in solving virtual machine placement problem and thus the proposed algorithm proves to provide better result than the existing technique.
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
2016-09-23
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.
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.
Solving Administrative Problems: Student Scheduling and Tracking System for the Microcomputer.
ERIC Educational Resources Information Center
Bolton, Brenda Anthony
1982-01-01
Describes the Student Scheduling and Tracking System (SSTS), which is a computerized student record database used in Davidson High School in Mobile, Alabama. The microcomputer-based system is used in report card preparation, student scheduling, and maintaining current student records. (JJD)
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.
Anticipated and Experienced Problems in Implementing a Flexible-Modular Schedule
ERIC Educational Resources Information Center
Sturges, A. W.; Mrdjenovich, Donald
1973-01-01
Successful implementation of a modular-flexible schedule was found to facilitate subsequent school structure changes by principals; questionnaires were sent to school principals and a national jury'' to provide both practical and theoretical answers. (Editor/SP)
The nurse scheduling problem: a goal programming and nonlinear optimization approaches
NASA Astrophysics Data System (ADS)
Hakim, L.; Bakhtiar, T.; Jaharuddin
2017-01-01
Nurses scheduling is an activity of allocating nurses to conduct a set of tasks at certain room at a hospital or health centre within a certain period. One of obstacles in the nurse scheduling is the lack of resources in order to fulfil the needs of the hospital. Nurse scheduling which is undertaken manually will be at risk of not fulfilling some nursing rules set by the hospital. Therefore, this study aimed to perform scheduling models that satisfy all the specific rules set by the management of Bogor State Hospital. We have developed three models to overcome the scheduling needs. Model 1 is designed to schedule nurses who are solely assigned to a certain inpatient unit and Model 2 is constructed to manage nurses who are assigned to an inpatient room as well as at Polyclinic room as conjunct nurses. As the assignment of nurses on each shift is uneven, then we propose Model 3 to minimize the variance of the workload in order to achieve equitable assignment on every shift. The first two models are formulated in goal programming framework, while the last model is in nonlinear optimization form.
Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang
2014-01-01
We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms.
Seol, Jae-Wook; Yi, Wangjin; Choi, Jinwook; Lee, Kyung Soon
2017-02-01
Clinical narrative text includes information related to a patient's medical history such as chronological progression of medical problems and clinical treatments. A chronological view of a patient's history makes clinical audits easier and improves quality of care. In this paper, we propose a clinical Problem-Action relation extraction method, based on clinical semantic units and event causality patterns, to present a chronological view of a patient's problem and a doctor's action. Based on our observation that a clinical text describes a patient's medical problems and a doctor's treatments in chronological order, a clinical semantic unit is defined as a problem and/or an action relation. Since a clinical event is a basic unit of the problem and action relation, events are extracted from narrative texts, based on the external knowledge resources context features of the conditional random fields. A clinical semantic unit is extracted from each sentence based on time expressions and context structures of events. Then, a clinical semantic unit is classified into a problem and/or action relation based on the event causality patterns of the support vector machines. Experimental results on Korean discharge summaries show 78.8% performance in the F1-measure. This result shows that the proposed method is effectively classifies clinical Problem-Action relations.
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.
Approximate Bayesian computation for machine learning, inverse problems and big data
NASA Astrophysics Data System (ADS)
Mohammad-Djafari, Ali
2017-06-01
This paper summarizes my tutorial talk in MaxEnt 2016 workshop. Starting from the basics of the Bayesian approach and simple example of low dimensional parameter estimation where almost all the computations can be done easily, we go very fast to high dimensional case. In those real world cases, even for the sample case of linear model with Gaussian prior, where the posterior law is also Gaussian, the cost of the computation of the posterior covariance becomes important and needs approximate and fast algorithms. Different approximation methods for model comparison and model selection in machine learning problems are presented in summary. Among the existing methods, we mention Laplace approximation, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Variational Bayesian Approximation (VBA) Methods. Finally, through two examples of inverse problems in imaging systems: X ray and Diffraction wave Computed Tomography (CT), we show how to handle the real great dimensional problems.
A multi-objective scatter search for a bi-criteria no-wait flow shop scheduling problem
NASA Astrophysics Data System (ADS)
Rahimi-Vahed, A. R.; Javadi, B.; Rabbani, M.; Tavakkoli-Moghaddam, R.
2008-04-01
The flow shop problem as a typical manufacturing challenge has gained wide attention in academic fields. This article considers a bi-criteria no-wait flow shop scheduling problem (FSSP) in which weighted mean completion time and weighted mean tardiness are to be minimized simultaneously. Since a FSSP has been proved to be NP-hard in a strong sense, a new multi-objective scatter search (MOSS) is designed for finding the locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm (GA), i.e. SPEA-II. The computational results show that the proposed MOSS performs better than the above GA, especially for the large-sized problems.
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…
The Swedish Experiment with Localised Control of Time Schedules: Policy Problem Representations
ERIC Educational Resources Information Center
Ronnberg, Linda
2007-01-01
Swedish compulsory schools are the most autonomous in Europe regarding time allocation and time management. Still, the Swedish state decided to take this even further, when introducing an experiment that permits some compulsory schools to abandon the regulations of the national time schedule. The aim of this study is to explore and analyse the…
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.
Traversa, Fabio L; Di Ventra, Massimiliano
2017-02-01
We introduce a class of digital machines, we name Digital Memcomputing Machines, (DMMs) able to solve a wide range of problems including Non-deterministic Polynomial (NP) ones with polynomial resources (in time, space, and energy). An abstract DMM with this power must satisfy a set of compatible mathematical constraints underlying its practical realization. We prove this by making a connection with the dynamical systems theory. This leads us to a set of physical constraints for poly-resource resolvability. Once the mathematical requirements have been assessed, we propose a practical scheme to solve the above class of problems based on the novel concept of self-organizing logic gates and circuits (SOLCs). These are logic gates and circuits able to accept input signals from any terminal, without distinction between conventional input and output terminals. They can solve boolean problems by self-organizing into their solution. They can be fabricated either with circuit elements with memory (such as memristors) and/or standard MOS technology. Using tools of functional analysis, we prove mathematically the following constraints for the poly-resource resolvability: (i) SOLCs possess a global attractor; (ii) their only equilibrium points are the solutions of the problems to solve; (iii) the system converges exponentially fast to the solutions; (iv) the equilibrium convergence rate scales at most polynomially with input size. We finally provide arguments that periodic orbits and strange attractors cannot coexist with equilibria. As examples, we show how to solve the prime factorization and the search version of the NP-complete subset-sum problem. Since DMMs map integers into integers, they are robust against noise and hence scalable. We finally discuss the implications of the DMM realization through SOLCs to the NP = P question related to constraints of poly-resources resolvability.
NASA Astrophysics Data System (ADS)
Traversa, Fabio L.; Di Ventra, Massimiliano
2017-02-01
We introduce a class of digital machines, we name Digital Memcomputing Machines, (DMMs) able to solve a wide range of problems including Non-deterministic Polynomial (NP) ones with polynomial resources (in time, space, and energy). An abstract DMM with this power must satisfy a set of compatible mathematical constraints underlying its practical realization. We prove this by making a connection with the dynamical systems theory. This leads us to a set of physical constraints for poly-resource resolvability. Once the mathematical requirements have been assessed, we propose a practical scheme to solve the above class of problems based on the novel concept of self-organizing logic gates and circuits (SOLCs). These are logic gates and circuits able to accept input signals from any terminal, without distinction between conventional input and output terminals. They can solve boolean problems by self-organizing into their solution. They can be fabricated either with circuit elements with memory (such as memristors) and/or standard MOS technology. Using tools of functional analysis, we prove mathematically the following constraints for the poly-resource resolvability: (i) SOLCs possess a global attractor; (ii) their only equilibrium points are the solutions of the problems to solve; (iii) the system converges exponentially fast to the solutions; (iv) the equilibrium convergence rate scales at most polynomially with input size. We finally provide arguments that periodic orbits and strange attractors cannot coexist with equilibria. As examples, we show how to solve the prime factorization and the search version of the NP-complete subset-sum problem. Since DMMs map integers into integers, they are robust against noise and hence scalable. We finally discuss the implications of the DMM realization through SOLCs to the NP = P question related to constraints of poly-resources resolvability.
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.
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. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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.
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.
McKeith, Charles F A; Rock, Adam J; Clark, Gavin I
2016-09-12
In Australia, poker-machine gamblers represent a disproportionate number of problem gamblers. To cultivate a greater understanding of the psychological mechanisms involved in poker-machine gambling, a repeated measures cue-reactivity protocol was administered. A community sample of 38 poker-machine gamblers was assessed for problem-gambling severity and trait mindfulness. Participants were also assessed regarding altered state of awareness (ASA) and urge to gamble at baseline, following a neutral cue, and following a gambling cue. Results indicated that: (a) urge to gamble significantly increased from neutral cue to gambling cue, while controlling for baseline urge; (b) cue-reactive ASA did not significantly mediate the relationship between problem-gambling severity and cue-reactive urge (from neutral cue to gambling cue); (c) trait mindfulness was significantly negatively associated with both problem-gambling severity and cue-reactive urge (i.e., from neutral cue to gambling cue, while controlling for baseline urge); and (d) trait mindfulness did not significantly moderate the effect of problem-gambling severity on cue-reactive urge (from neutral cue to gambling cue). This is the first study to demonstrate a negative association between trait mindfulness and cue-reactive urge to gamble in a population of poker-machine gamblers. Thus, this association merits further evaluation both in relation to poker-machine gambling and other gambling modalities.
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.
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.
Bidirectional extreme learning machine for regression problem and its learning effectiveness.
Yang, Yimin; Wang, Yaonan; Yuan, Xiaofang
2012-09-01
It is clear that the learning effectiveness and learning speed of neural networks are in general far slower than required, which has been a major bottleneck for many applications. Recently, a simple and efficient learning method, referred to as extreme learning machine (ELM), was proposed by Huang , which has shown that, compared to some conventional methods, the training time of neural networks can be reduced by a thousand times. However, one of the open problems in ELM research is whether the number of hidden nodes can be further reduced without affecting learning effectiveness. This brief proposes a new learning algorithm, called bidirectional extreme learning machine (B-ELM), in which some hidden nodes are not randomly selected. In theory, this algorithm tends to reduce network output error to 0 at an extremely early learning stage. Furthermore, we find a relationship between the network output error and the network output weights in the proposed B-ELM. Simulation results demonstrate that the proposed method can be tens to hundreds of times faster than other incremental ELM algorithms.
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.
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.
Granja, C; Almada-Lobo, B; Janela, F; Seabra, J; Mendes, A
2014-12-01
As patient's length of stay in waiting lists increases, governments are looking for strategies to control the problem. Agreements were created with private providers to diminish the workload in the public sector. However, the growth of the private sector is not following the demand for care. Given this context, new management strategies have to be considered in order to minimize patient length of stay in waiting lists while reducing the costs and increasing (or at least maintaining) the quality of care. Appointment scheduling systems are today known to be proficient in the optimization of health care services. Their utilization is focused on increasing the usage of human resources, medical equipment and reducing the patient waiting times. In this paper, a simulation-based optimization approach to the Patient Admission Scheduling Problem is presented. Modeling tools and simulation techniques are used in the optimization of a diagnostic imaging department. The proposed techniques have demonstrated to be effective in the evaluation of diagnostic imaging workflows. A simulated annealing algorithm was used to optimize the patient admission sequence towards minimizing the total completion and total waiting of patients. The obtained results showed average reductions of 5% on the total completion and 38% on the patients' total waiting time. Copyright © 2014 Elsevier Inc. All rights reserved.
NASA Astrophysics Data System (ADS)
Kase, Sue E.; Vanni, Michelle; Caylor, Justine; Hoye, Jeff
2017-05-01
The Human-Assisted Machine Information Exploitation (HAMIE) investigation utilizes large-scale online data collection for developing models of information-based problem solving (IBPS) behavior in a simulated time-critical operational environment. These types of environments are characteristic of intelligence workflow processes conducted during human-geo-political unrest situations when the ability to make the best decision at the right time ensures strategic overmatch. The project takes a systems approach to Human Information Interaction (HII) by harnessing the expertise of crowds to model the interaction of the information consumer and the information required to solve a problem at different levels of system restrictiveness and decisional guidance. The design variables derived from Decision Support Systems (DSS) research represent the experimental conditions in this online single-player against-the-clock game where the player, acting in the role of an intelligence analyst, is tasked with a Commander's Critical Information Requirement (CCIR) in an information overload scenario. The player performs a sequence of three information processing tasks (annotation, relation identification, and link diagram formation) with the assistance of `HAMIE the robot' who offers varying levels of information understanding dependent on question complexity. We provide preliminary results from a pilot study conducted with Amazon Mechanical Turk (AMT) participants on the Volunteer Science scientific research platform.
NASA Astrophysics Data System (ADS)
Baniamerian, Ali; Bashiri, Mahdi; Zabihi, Fahime
2017-04-01
Cross-docking is a new warehousing policy in logistics which is widely used all over the world and attracts many researchers attention to study about in last decade. In the literature, economic aspects has been often studied, while one of the most significant factors for being successful in the competitive global market is improving quality of customer servicing and focusing on customer satisfaction. In this paper, we introduce a vehicle routing and scheduling problem with cross-docking and time windows in a three-echelon supply chain that considers customer satisfaction. A set of homogeneous vehicles collect products from suppliers and after consolidation process in the cross-dock, immediately deliver them to customers. A mixed integer linear programming model is presented for this problem to minimize transportation cost and early/tardy deliveries with scheduling of inbound and outbound vehicles to increase customer satisfaction. A two phase genetic algorithm (GA) is developed for the problem. For investigating the performance of the algorithm, it was compared with exact and lower bound solutions in small and large-size instances, respectively. Results show that there are at least 86.6% customer satisfaction by the proposed method, whereas customer satisfaction in the classical model is at most 33.3%. Numerical examples results show that the proposed two phase algorithm could achieve optimal solutions in small-size instances. Also in large-size instances, the proposed two phase algorithm could achieve better solutions with less gap from the lower bound in less computational time in comparison with the classic GA.
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.
Rasmussen, Karina; O'Neill, Robert E
2006-01-01
The current study assessed the effects of fixed-time reinforcement schedules on problem behavior of students with emotional-behavioral disorders in a clinical day-treatment classroom setting. Three elementary-aged students with a variety of emotional and behavioral problems participated in the study. Initial functional assessments indicated that social attention was the maintaining reinforcer for their verbally disruptive behavior. Baseline phases were alternated with phases in which attention was provided on fixed-time schedules in the context of an ABAB design. The results indicated that the provision of attention on fixed-time schedules substantially reduced the participants' rate of verbal disruptions. These decreases were maintained during initial thinning of the schedules. The results provide one of the first examples that such an intervention can be successfully implemented in a classroom setting.
NASA Astrophysics Data System (ADS)
Meysam Mousavi, S.; Tavakkoli-Moghaddam, Reza; Jolai, Fariborz
2013-10-01
This article considers the design of cross-docking systems under uncertainty in a model that consists of two phases: (1) a strategic-based decision-making process for selecting the location of cross-docks to operate, and (2) an operational-based decision-making process for vehicle routing scheduling with multiple cross-docks. This logistic system contains three echelons, namely suppliers, cross-docks and retailers, in an uncertain environment. In the first phase, a new multi-period cross-dock location model is introduced to determine the minimum number of cross-docks among a set of location sites so that each retailer demand should be met. Then, in the second phase, a new vehicle routing scheduling model with multiple cross-docks is formulated in which each vehicle is able to pickup from or deliver to more than one supplier or retailer, and the pickup and delivery routes start and end at the corresponding cross-dock. This article is the first attempt to introduce an integrated model for cross-docking systems design under a fuzzy environment. To solve the presented two-phase mixed-integer programming (MIP) model, a new fuzzy mathematical programming-based possibilistic approach is used. Furthermore, experimental tests are carried out to demonstrate the effectiveness of the presented model. The computational results reveal the applicability and suitability of the developed fuzzy possibilistic two-phase model in a variety of problems in the domain of cross-docking systems.
Parry, R Mitchell; Phan, John H; Wang, May D
2012-03-21
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. 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. 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 thoroughness of feature
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.
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 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
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.
NASA Astrophysics Data System (ADS)
Wolfe, William J.; Wood, David; Sorensen, Stephen E.
1996-12-01
This paper discusses automated scheduling as it applies to complex domains such as factories, transportation, and communications systems. The window-constrained-packing problem is introduced as an ideal model of the scheduling trade offs. Specific algorithms are compared in terms of simplicity, speed, and accuracy. In particular, dispatch, look-ahead, and genetic algorithms are statistically compared on randomly generated job sets. The conclusion is that dispatch methods are fast and fairly accurate; while modern algorithms, such as genetic and simulate annealing, have excessive run times, and are too complex to be practical.
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.
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.
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.
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.
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.
NASA Astrophysics Data System (ADS)
Jain, Madhu; Meena, Rakesh Kumar
2017-06-01
Markov model of multi-component machining system comprising two unreliable heterogeneous servers and mixed type of standby support has been studied. The repair job of broken down machines is done on the basis of bi-level threshold policy for the activation of the servers. The server returns back to render repair job when the pre-specified workload of failed machines is build up. The first (second) repairman turns on only when the work load of N1 (N2) failed machines is accumulated in the system. The both servers may go for vacation in case when all the machines are in good condition and there are no pending repair jobs for the repairmen. Runge-Kutta method is implemented to solve the set of governing equations used to formulate the Markov model. Various system metrics including the mean queue length, machine availability, throughput, etc., are derived to determine the performance of the machining system. To provide the computational tractability of the present investigation, a numerical illustration is provided. A cost function is also constructed to determine the optimal repair rate of the server by minimizing the expected cost incurred on the system. The hybrid soft computing method is considered to develop the adaptive neuro-fuzzy inference system (ANFIS). The validation of the numerical results obtained by Runge-Kutta approach is also facilitated by computational results generated by ANFIS.
NASA Astrophysics Data System (ADS)
Wei, Xiu; Zhang, Wenqiang; Weng, Wei; Fujimura, Shigeru
This paper proposed a multi-objective local search procedure (MOLS). It is combined with NSGA-II for solving bi-criteria PFSP with the objectives of minimizing makespan and maximum tardiness. Utilizing the properties of active blocks for flow shop scheduling problem, neighborhood structures MOINS (multi-objective insertion) and MOEXC (multi-objective exchange) are designed in order to improve efficiency of perturbation. Any perturbation based on MOINS and MOEXC takes effect on different criteria simultaneously. The original idea of MOLS is systematic change neighborhoods in the local search procedure. The search direction of MOLS on an individual is naturally guided by interaction of MOINS and MOEXC. Moreover, there is no need to set parameters in MOLS. The MOLS combined with popular multi-objective evolutionary algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm-II) is called as “NSGA-II-MOLS”. To illustrate the efficacy of proposed approach, four different scaled problems are used to test performance of NSGA-II-MOLS. The numerous comparisons show efficacy of NSGA-II-MOLS is better than most of algorithms even with the same number of individual evaluations and parameters setting.
Health problems among workers of iron welding machines: an effect of electromagnetic fields.
Prasad, S K; Vyas, S
2001-04-01
The possible effects of EMFs on 100 workers were studied by means of structured interview and rating of subjective symptoms. As control 41 sewing machine operators and assembly workers were chosen, interviewed and likewise tested. The present Indian ceiling value of 250 Tesla for the equivalent power density was exceeded in more than 50% of the machines. The highest leakage fields, for EMFs, were found near machines, which gave a high exposure to the hands. Eye irritation complaints were reported by 40% of the workers. The fertility outcome did not show any significant result.
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.
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…
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.
NASA Astrophysics Data System (ADS)
Sahin, M. Ö.; Krücker, D.; Melzer-Pellmann, I.-A.
2016-12-01
In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new-physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery-significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications.
Synchronizing production and air transportation scheduling using mathematical programming models
NASA Astrophysics Data System (ADS)
Zandieh, M.; Molla-Alizadeh-Zavardehi, S.
2009-08-01
Traditional scheduling problems assume that there are always infinitely many resources for delivering finished jobs to their destinations, and no time is needed for their transportation, so that finished products can be transported to customers without delay. So, for coordination of these two different activities in the implementation of a supply chain solution, we studied the problem of synchronizing production and air transportation scheduling using mathematical programming models. The overall problem is decomposed into two sub-problems, which consists of air transportation allocation problem and a single machine scheduling problem which they are considered together. We have taken into consideration different constraints and assumptions in our modeling such as special flights, delivery tardiness and no delivery tardiness. For these purposes, a variety of models have been proposed to minimize supply chain total cost which encompass transportation, makespan, delivery earliness tardiness and departure time earliness tardiness costs.
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.
Scheduler's assistant: a tool for intelligent scheduling
NASA Astrophysics Data System (ADS)
Griffin, Neal L.
1991-03-01
The objective of this project was to use expert system technology to aid in the scheduling activities performed at the White Sands Missile Range (WSMR). The WSMR range scheduling problem presents a complex interactive environment. A human factors approach was undertaken, in that, the goal was to implement a system which mimics current WSMR scheduling procedures. The results of this project have produced a prototypic scheduling tool, called Scheduler's Assistant (SA), to aid WSMR range schedulers to generate a daily schedule. The system provides resource conflict detection and resolution advice through a series of cooperating expert systems. Immediate advantages of the system are increased safety, insurance of proper schedule execution and improved speed for turnaround time of sudden schedule changes. Additional benefits of SA include: expandability as future operations grow, allows for rapid redeployment for changing resources, promotes efficient management of WSMR resources, provides a formal representation of knowledge such that years of range personnel experience is preserved and enables the flexibility of a scheduling aid as opposed to a rigid methodology. Prior development efforts by Perceptics have produced a sophisticated expert system development tool, called Knowledge Shaper, which was used to implement all of the expert systems. The development of SA included a library of routines (the SA toolbox) to permit the manipulation of internal data tables and define a data transfer protocol to and from the SA environment. The combination of Knowledge Shaper and the SA toolbox provide a powerful set of design tools for the development of future scheduling applications.
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…
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…
ERIC Educational Resources Information Center
Karabinus, Robert A.; Boris, Richard
This document describes the development and implementation of a computer based registration and scheduling information system at Northern Illinois University. Because of personnel shortages, the University sought and received help from commercial computer firms. Implementation of scheduling, registration, and billing systems was accomplished in a…
Distributed scheduling with COMPASS
NASA Technical Reports Server (NTRS)
Rufat-Latre, Jorge; Culbert, Chris
1991-01-01
COMPASS (COMPuter Aided Scheduling System) is a sophisticated, interactive scheduling tool used within NASA. Like most existing tools, however, COMPASS is a single-user application. There is a large class of scheduling problems which may be better solved by allowing several people at various locations to build separate schedules with shared resources. DISCORS (DIStributed COmputer Resource Scheduling) is a set of services which support a distributed version of COMPASS. This architecture naturally accommodates the integration of user-defined resource models without modifying COMPASS. DISCORS services include the ability to establish and manage communications, to code messages in efficient formats, to provide fault detection and recovery, and to configure schedulers across a network. In its present form, DISCORS effectively supports distributed COMPASS, but fails to run fast and to guarantee efficient schedules. Further enhancements may allow several users to simultaneously and interactively work together to create complex schedules while COMPASS detects and coordinates the resolution of conflicting requests.
Automated telescope scheduling
NASA Astrophysics Data System (ADS)
Johnston, Mark D.
1988-08-01
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.
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.
The Stanford School Scheduling System.
ERIC Educational Resources Information Center
Stanford Univ., CA. Dept. of Industrial Engineering.
This booklet gives a general overview of the computerized Stanford School Scheduling System (SSSS) which is designed to make scheduling less difficult for individualized programs in secondary education. Topics covered include new flexible scheduling and variable course structure designs in secondary education, the school scheduling problem,…
Effects on sleep-related problems and self-reported health after a change of shift schedule.
Karlson, Björn; Eek, Frida; Orbaek, Palle; Osterberg, Kai
2009-04-01
This study prospectively examined the effects of a change of shift schedule from a fast forward-rotating schedule to a slowly backward-rotating one. The initial schedule had a forward rotation from mornings to afternoons to nights over 6 consecutive days, with 2 days on each shift followed by 4 days off before the next iteration of the cycle, whereas the new schedule had a slower backward rotation from mornings to nights to afternoons, with 3 days on a given shift followed by 3 days off before the next shift. Shift workers (n = 118) were compared with a reference group of daytime workers (n = 67) from the same manufacturing plant by means of questionnaires covering subjective health, sleep and fatigue, recovery ability, satisfaction with work hours, work-family interface, and job demands, control, and support. Data were collected 6 months before implementing the new schedule and at a follow-up 15 months later. As predicted, on most dimensions measured the shift workers displayed clear improvements from initially poorer scores than daytime workers, and the daytime workers displayed no improvements.
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.
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.
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.
NASA Astrophysics Data System (ADS)
Deng, Guanlong; Gu, Xingsheng
2014-03-01
This article presents an enhanced iterated greedy (EIG) algorithm that searches both insert and swap neighbourhoods for the single-machine total weighted tardiness problem with sequence-dependent setup times. Novel elimination rules and speed-ups are proposed for the swap move to make the employment of swap neighbourhood worthwhile due to its reduced computational expense. Moreover, a perturbation operator is newly designed as a substitute for the existing destruction and construction procedures to prevent the search from being attracted to local optima. To validate the proposed algorithm, computational experiments are conducted on a benchmark set from the literature. The results show that the EIG outperforms the existing state-of-the-art algorithms for the considered problem.
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. Copyright © 2014 Society for Psychophysiological Research.
Effects of problem structure and dialogue type on the performance of the man/machine interface
Palko, J.B.
1986-01-01
This research project is intended to provide guidance for software designers who must decide on a dialogue style for interactive problem solving support for new or infrequent users. In addition, it examines the relative performance of these dialogues in environments with a varying amount of problem structure. The three dialogue styles that are recommended by various writers in the field of information systems are: (1) menu driven, (2) question/response, and (3) form filling. It is concluded that if an interface is properly designed it probably does not matter which dialogue style is used in some problem settings. It is also clear that adding problem parameters reduces the ability of the problem solver to determine the best solution.
Constraint-Based Scheduling System
NASA Technical Reports Server (NTRS)
Zweben, Monte; Eskey, Megan; Stock, Todd; Taylor, Will; Kanefsky, Bob; Drascher, Ellen; Deale, Michael; Daun, Brian; Davis, Gene
1995-01-01
Report describes continuing development of software for constraint-based scheduling system implemented eventually on massively parallel computer. Based on machine learning as means of improving scheduling. Designed to learn when to change search strategy by analyzing search progress and learning general conditions under which resource bottleneck occurs.
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.
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.
Hing, Nerilee; Russell, Alex M.; Browne, Matthew
2017-01-01
Growth of Internet gambling has fuelled concerns about its contribution to gambling problems. However, most online gamblers also gamble on land-based forms, which may be the source of problems for some. Studies therefore need to identify the problematic mode of gambling (online or offline) to identify those with an online gambling problem. Identifying most problematic form of online gambling (e.g., EGMs, race betting, sports betting) would also enable a more accurate examination of gambling problems attributable to a specific online gambling form. This study pursued this approach, aiming to: (1) determine demographic, behavioral and psychological risk factors for gambling problems on online EGMs, online sports betting and online race betting; (2) compare the characteristics of problematic online gamblers on each of these online forms. An online survey of 4,594 Australian gamblers measured gambling behavior, most problematic mode and form of gambling, gambling attitudes, psychological distress, substance use, help-seeking, demographics and problem gambling status. Problem/moderate risk gamblers nominating an online mode of gambling as their most problematic, and identifying EGMs (n = 98), race betting (n = 291) or sports betting (n = 181) as their most problematic gambling form, were compared to non-problem/low risk gamblers who had gambled online on these forms in the previous 12 months (n = 64, 1145 and 1213 respectively), using bivariate analyses and then logistic regressions. Problem/moderate risk gamblers on each of these online forms were then compared. Risk factors for online EGM gambling were: more frequent play on online EGMs, substance use when gambling, and higher psychological distress. Risk factors for online sports betting were being male, younger, lower income, born outside of Australia, speaking a language other than English, more frequent sports betting, higher psychological distress, and more negative attitudes toward gambling. Risk factors for
Hing, Nerilee; Russell, Alex M; Browne, Matthew
2017-01-01
Growth of Internet gambling has fuelled concerns about its contribution to gambling problems. However, most online gamblers also gamble on land-based forms, which may be the source of problems for some. Studies therefore need to identify the problematic mode of gambling (online or offline) to identify those with an online gambling problem. Identifying most problematic form of online gambling (e.g., EGMs, race betting, sports betting) would also enable a more accurate examination of gambling problems attributable to a specific online gambling form. This study pursued this approach, aiming to: (1) determine demographic, behavioral and psychological risk factors for gambling problems on online EGMs, online sports betting and online race betting; (2) compare the characteristics of problematic online gamblers on each of these online forms. An online survey of 4,594 Australian gamblers measured gambling behavior, most problematic mode and form of gambling, gambling attitudes, psychological distress, substance use, help-seeking, demographics and problem gambling status. Problem/moderate risk gamblers nominating an online mode of gambling as their most problematic, and identifying EGMs (n = 98), race betting (n = 291) or sports betting (n = 181) as their most problematic gambling form, were compared to non-problem/low risk gamblers who had gambled online on these forms in the previous 12 months (n = 64, 1145 and 1213 respectively), using bivariate analyses and then logistic regressions. Problem/moderate risk gamblers on each of these online forms were then compared. Risk factors for online EGM gambling were: more frequent play on online EGMs, substance use when gambling, and higher psychological distress. Risk factors for online sports betting were being male, younger, lower income, born outside of Australia, speaking a language other than English, more frequent sports betting, higher psychological distress, and more negative attitudes toward gambling. Risk factors for
Polynomial algorithms for multiprocessor scheduling with a small number of job lengths
McCormick, S.T.; Smallwood, S.R.; Spieksma, F.C.R.
1997-06-01
The following problem was originally motivated by a question arising in scheduling maintenance periods for aircraft. Each maintenance period is a job, and the maintenance facilities are machines. In this context, there are very few different types of maintenances performed, so it is natural to consider the problem with only a small, fixed number C of different types of jobs. Each job type has a processing time, and each machine is available for the same length of time. A machine can handle at most one job at a time, all jobs are released at time zero, there are no due dates or precedence constraints, and preemption is not allowed. The question is whether it is possible to finish all jobs. We call this problem the Multiprocessor Scheduling Problem with C job lengths (MSPC). Scheduling problems such as MSPC where we can partition the jobs into a relatively few types such that all jobs of each type are identical are often called high-multiplicity problems. High-multiplicity problems are interesting because their input is very compact: the input to MSPC consists of only 2C + 2 numbers. For the case C = 2 we present a polynomial-time algorithm. We show that this algorithm produces a schedule that uses at most three different one-machine schedules, the minimum possible number. Further, we extend this algorithm to the case of machine-dependent deadlines and to a multi-parametric case. Finally, we discuss why our approach appears not to extend to the case C > 2.
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.
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…
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…
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…
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.
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.
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.
Heuristic for Critical Machine Based a Lot Streaming for Two-Stage Hybrid Production Environment
NASA Astrophysics Data System (ADS)
Vivek, P.; Saravanan, R.; Chandrasekaran, M.; Pugazhenthi, R.
2017-03-01
Lot streaming in Hybrid flowshop [HFS] is encountered in many real world problems. This paper deals with a heuristic approach for Lot streaming based on critical machine consideration for a two stage Hybrid Flowshop. The first stage has two identical parallel machines and the second stage has only one machine. In the second stage machine is considered as a critical by valid reasons these kind of problems is known as NP hard. A mathematical model developed for the selected problem. The simulation modelling and analysis were carried out in Extend V6 software. The heuristic developed for obtaining optimal lot streaming schedule. The eleven cases of lot streaming were considered. The proposed heuristic was verified and validated by real time simulation experiments. All possible lot streaming strategies and possible sequence under each lot streaming strategy were simulated and examined. The heuristic consistently yielded optimal schedule consistently in all eleven cases. The identification procedure for select best lot streaming strategy was suggested.
1990-04-01
DTIC i.LE COPY RADC-TR-90-25 Final Technical Report April 1990 MACHINE LEARNING The MITRE Corporation Melissa P. Chase Cs) CTIC ’- CT E 71 IN 2 11990...S. FUNDING NUMBERS MACHINE LEARNING C - F19628-89-C-0001 PE - 62702F PR - MOlE S. AUTHO(S) TA - 79 Melissa P. Chase WUT - 80 S. PERFORMING...341.280.5500 pm I " Aw Sig rill Ia 2110-01 SECTION 1 INTRODUCTION 1.1 BACKGROUND Research in machine learning has taken two directions in the problem of
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.,
Relation of the One-Phase Stefan Problem to the Seepage of Liquids and Electrochemical Machining,
1979-12-01
Haim Brezis , Alan E. Berger, and Joel C. W. Rogers, "A Numerical Method for Solving the Problem ut - Af(u) = 0," to appear. 6. Haim Brezis , Alan E...D , (1.lc) where L is an appropriate linear operator and the function f is given by f(u) = max(u - 1,0) . (1.2) Under most conditions of interest, the... function t v(x,t) - f(u(x,t’)dt’ (1.5) when uo(x) > 1 for x e n(O) D , (1.6a) u0 (x) 0 for x c D - Q(0) (1.6b) vt &V + U0 - 1 , X C (t) , 0 < t < , (1.7a
State-based scheduling: An architecture for telescope observation scheduling
NASA Technical Reports Server (NTRS)
Muscettola, Nicola; Smith, Stephen F.
1989-01-01
The applicability of constraint-based scheduling, a methodology previously developed and validated in the domain of factory scheduling, is extended to problem domains that require attendance to a wider range of state-dependent constraints. The problem of constructing and maintaining a short-term observation schedule for the Hubble Space Telescope (HST), which typifies this type of domain is the focus of interest. The nature of the constraints encountered in the HST domain is examined, system requirements are discussed with respect to utilization of a constraint-based scheduling methodology in such domains, and a general framework for state-based scheduling is presented.
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; Ji, Ping
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.
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
Better approximation guarantees for job-shop scheduling
Goldberg, L.A.; Paterson, M.; Srinivasan, A.
1997-06-01
Job-shop scheduling is a classical NP-hard problem. Shmoys, Stein & Wein presented the first polynomial-time approximation algorithm for this problem that has a good (polylogarithmic) approximation guarantee. We improve the approximation guarantee of their work, and present further improvements for some important NP-hard special cases of this problem (e.g., in the preemptive case where machines can suspend work on operations and later resume). We also present NC algorithms with improved approximation guarantees for some NP-hard special cases.
Conway, L.
1984-01-01
To meet the challenge of certain critical problems in defense, the Defense Advanced Research Projects Agency (DARPA) is initiating an important new program in strategic computing. By seizing an opportunity to leverage recent advances in artificial intelligence, computer science, and microelectronics, the agency plans to create a new generation of machine-intelligence technology.
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.
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.
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.
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
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.
Human-machine analytics for closed-loop sense-making in time-dominant cyber defense problems
NASA Astrophysics Data System (ADS)
Henry, Matthew H.
2017-05-01
Many defense problems are time-dominant: attacks progress at speeds that outpace human-centric systems designed for monitoring and response. Despite this shortcoming, these well-honed and ostensibly reliable systems pervade most domains, including cyberspace. The argument that often prevails when considering the automation of defense is that while technological systems are suitable for simple, well-defined tasks, only humans possess sufficiently nuanced understanding of problems to act appropriately under complicated circumstances. While this perspective is founded in verifiable truths, it does not account for a middle ground in which human-managed technological capabilities extend well into the territory of complex reasoning, thereby automating more nuanced sense-making and dramatically increasing the speed at which it can be applied. Snort1 and platforms like it enable humans to build, refine, and deploy sense-making tools for network defense. Shortcomings of these platforms include a reliance on rule-based logic, which confounds analyst knowledge of how bad actors behave with the means by which bad behaviors can be detected, and a lack of feedback-informed automation of sensor deployment. We propose an approach in which human-specified computational models hypothesize bad behaviors independent of indicators and then allocate sensors to estimate and forecast the state of an intrusion. State estimates and forecasts inform the proactive deployment of additional sensors and detection logic, thereby closing the sense-making loop. All the while, humans are on the loop, rather than in it, permitting nuanced management of fast-acting automated measurement, detection, and inference engines. This paper motivates and conceptualizes analytics to facilitate this human-machine partnership.
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
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.
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.
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.
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.
Tricker, Christopher; Rock, Adam J; Clark, Gavin I
2016-06-01
In order to enhance our understanding of the nature of poker-machine problem-gambling, a community sample of 37 poker-machine gamblers (M age = 32 years, M PGSI = 5; PGSI = Problem Gambling Severity Index) were assessed for urge to gamble (responses on a visual analogue scale) and altered state of consciousness (assessed by the Altered State of Awareness dimension of the Phenomenology of Consciousness Inventory) at baseline, after a neutral cue, and after a gambling cue. It was found that (a) problem-gambling severity (PGSI score) predicted increase in urge (from neutral cue to gambling cue, controlling for baseline; sr (2) = .19, p = .006) and increase in altered state of consciousness (from neutral cue to gambling cue, controlling for baseline; sr (2) = .57, p < .001), and (b) increase in altered state of consciousness (from neutral cue to gambling cue) mediated the relationship between problem-gambling severity and increase in urge (from neutral cue to gambling cue; κ(2) = .40, 99 % CI [.08, .71]). These findings suggest that cue-reactive altered state of consciousness is an important component of cue-reactive urge in poker-machine problem-gamblers.
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.
29 CFR 1910.218 - Forging machines.
Code of Federal Regulations, 2010 CFR
2010-07-01
... 29 Labor 5 2010-07-01 2010-07-01 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 the...
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...
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, 2011 CFR
2011-07-01
... 29 Labor 5 2011-07-01 2011-07-01 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...
1983-02-01
no task is scheduled with overlap. Let numpi be the total number of preemptions and idle slots of size at most to that are introduced. We see that if...no usable block remains on Qm-*, then numpi < m-k. Otherwise, numpi ! m-k-1. If j>n when this procedure terminates, then all tasks have been scheduled
ERIC Educational Resources Information Center
Childers, Gary L.; Ireland, Rebecca Weeks
2005-01-01
In education, there is no one best way to do anything. There are compelling reasons why some courses should be taught in longer segments of time, which the block schedule provides. There are also compelling reasons why some classes should be taught in shorter segments. At Watauga High School in Boone, North Carolina, an alternative schedule that…
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.
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
2015-06-11
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.
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
29 CFR 1952.173 - Developmental schedule.
Code of Federal Regulations, 2011 CFR
2011-07-01
... radiation machines and other non-Atomic Energy Act sources of radiation. The standards and enforcement... Inspection Scheduling System will be fully implemented and in operation March 31, 1975....
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.
McCormick, Jessica; Delfabbro, Paul; Denson, Linley A
2012-12-01
The aim of this study was to conduct an empirical investigation of the validity of Jacobs' (in J Gambl Behav 2:15-31, 1986) general theory of addictions in relation to gambling problems associated with electronic gaming machines (EGM). Regular EGM gamblers (n = 190) completed a series of standardised measures relating to psychological and physiological vulnerability, substance use, dissociative experiences, early childhood trauma and abuse and problem gambling (the Problem Gambling Severity Index). Statistical analysis using structural equation modelling revealed clear relationships between childhood trauma and life stressors and psychological vulnerability, dissociative-like experiences and problem gambling. These findings confirm and extend a previous model validated by Gupta and Derevensky (in J Gambl Stud 14: 17-49, 1998) using an adolescent population. The significance of these findings are discussed for existing pathway models of problem gambling, for Jacobs' theory, and for clinicians engaged in assessment and intervention.
Semi-online patient scheduling in pathology laboratories.
Azadeh, Ali; Baghersad, Milad; Farahani, Mehdi Hosseinabadi; Zarrin, Mansour
2015-07-01
Nowadays, effective scheduling of patients in clinics, laboratories, and emergency rooms is becoming increasingly important. Hospitals are required to maximize the level of patient satisfaction, while they are faced with lack of space and facilities. An effective scheduling of patients in existing conditions is vital for improving healthcare delivery. The shorter waiting time of patients improves healthcare service quality and efficiency. Focusing on real settings, this paper addresses a semi-online patient scheduling problem in a pathology laboratory located in Tehran, Iran, as a case study. Due to partial precedence constraints of laboratory tests, the problem is formulated as a semi-online hybrid shop scheduling problem and a mixed integer linear programming model is proposed. A genetic algorithm (GA) is developed for solving the problem and response surface methodology is used for setting GA parameters. A lower bound is also calculated for the problem, and several experiments are conducted to estimate the validity of the proposed algorithm. Based on the empirical data collected from the pathology laboratory, comparison between the current condition of the laboratory and the results obtained by the proposed approach is performed through simulation experiments. The results indicate that the proposed approach can significantly reduce waiting time of the patients and improve operations efficiency. The proposed approach has been successfully applied to scheduling patients in a pathology laboratory considering the real-world settings including precedence constraints of tests, constraint on the number of sites or operators for taking tests (i.e. multi-machine problem), and semi-online nature of the problem. Copyright © 2015 Elsevier B.V. All rights reserved.
ERIC Educational Resources Information Center
Riehl, Carolyn; Pallas, Aaron M.; Natriello, Gary
1999-01-01
Studied course scheduling in five urban high schools, exploring reasons scheduling problems can persist year after year. Identified disruptions to the scheduling process. Contains 73 references. (SLD)
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.
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.
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.
Optimizing Observation Scheduling Objectives
NASA Technical Reports Server (NTRS)
Bresina, John L.; Morris, Robert A.; Edgington, William R.
1997-01-01
In this paper, we present an approach that enables the automatic generation of high quality schedules, with respect to a given objective function. The approach involves the combination of two techniques: GenH, which automatically generates a search heuristic specialized to the given problem instance, and HBSS, which employs the generated heuristic as a bias within a stochastic sampling method.
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.
Interactive computer aided shift scheduling.
Gaertner, J
2001-12-01
This paper starts with a discussion of computer aided shift scheduling. After a brief review of earlier approaches, two conceptualizations of this field are introduced: First, shift scheduling as a field that ranges from extremely stable rosters at one pole to rather market-like approaches on the other pole. Unfortunately, already small alterations of a scheduling problem (e.g., the number of groups, the number of shifts) may call for rather different approaches and tools. Second, their environment shapes scheduling problems and scheduling has to be done within idiosyncratic organizational settings. This calls for the amalgamation of scheduling with other tasks (e.g., accounting) and for reflections whether better solutions might become possible by changes in the problem definition (e.g., other service levels, organizational changes). Therefore shift scheduling should be understood as a highly connected problem. Building upon these two conceptualizations, a few examples of software that ease scheduling in some areas of this field are given and future research questions are outlined.
Reactive Scheduling in Multipurpose Batch Plants
NASA Astrophysics Data System (ADS)
Narayani, A.; Shaik, Munawar A.
2010-10-01
Scheduling is an important operation in process industries for improving resource utilization resulting in direct economic benefits. It has a two-fold objective of fulfilling customer orders within the specified time as well as maximizing the plant profit. Unexpected disturbances such as machine breakdown, arrival of rush orders and cancellation of orders affect the schedule of the plant. Reactive scheduling is generation of a new schedule which has minimum deviation from the original schedule in spite of the occurrence of unexpected events in the plant operation. Recently, Shaik & Floudas (2009) proposed a novel unified model for short-term scheduling of multipurpose batch plants using unit-specific event-based continuous time representation. In this paper, we extend the model of Shaik & Floudas (2009) to handle reactive scheduling.
Testing Task Schedulers on Linux System
NASA Astrophysics Data System (ADS)
Jelenković, Leonardo; Groš, Stjepan; Jakobović, Domagoj
Testing task schedulers on Linux operating system proves to be a challenging task. There are two main problems. The first one is to identify which properties of the scheduler to test. The second problem is how to perform it, e.g., which API to use that is sufficiently precise and in the same time supported on most platforms. This paper discusses the problems in realizing test framework for testing task schedulers and presents one potential solution. Observed behavior of the scheduler is the one used for “normal” task scheduling (SCHED_OTHER), unlike one used for real-time tasks (SCHED_FIFO, SCHED_RR).
Zhu, Yahui.
1990-01-01
The author studies the scheduling of independent jobs on hypercube multiprocessors. He assumes that the hypercube system supports space-sharing for multiprogramming, i.e., a hypercube is partitioned into subcubes and each job is assigned to a dedicated subcube and many jobs can be running simultaneously without interfering with each other. Then the problem of how to schedule a set of jobs so that they can be finished as early as possible becomes important. He investigates two kinds of scheduling algorithms for the problem. The first one is nonpreemptive scheduling, i.e., no job is allowed to be interrupted during its execution. In this case, the problem is NP-Complete. He proposes an approximation algorithm called LDF, which generates a schedule with a finish time less than twice that of an optimal schedule. Compared with the earlier proposed algorithm, his algorithm is simpler and has almost the same performance. More importantly, his LDF algorithm can achieve this performance without knowing the job processing times, which may be hard to obtain in practice. Also he proves a lower bound result which implies that it is unlikely to find simple heuristic algorithms that can perform much better than the existing algorithms including LDF. The second kind is preemptive scheduling, i.e., a job can be preempted during its execution and rescheduled later. He develops a feasibility algorithm that runs in O (n log n) time and generates a schedule with at most min{l brace}n-2, 2{sup m}-1{r brace} preemptions. It can generate a feasible schedule for the given job set if there exists one. This improvement is important because many scheduling algorithms depend on a feasibility algorithm as a building block. Furthermore, based on an advanced search technique, he presents an algorithm that can find the optimal schedule in O(n{sup 2} log {sup 2}n) time.
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.
Evaluation of scheduling techniques for payload activity planning
NASA Technical Reports Server (NTRS)
Bullington, Stanley F.
1991-01-01
Two tasks related to payload activity planning and scheduling were performed. The first task involved making a comparison of space mission activity scheduling problems with production scheduling problems. The second task consisted of a statistical analysis of the output of runs of the Experiment Scheduling Program (ESP). Details of the work which was performed on these two tasks are presented.
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.
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.
ERIC Educational Resources Information Center
Pactor, Paul
1970-01-01
The U.S. Department of Labor has projected a 106 percent increase in the demand for office machine operators over the next 10 years. Machines with a high frequency of use include printing calculators, 10-key adding machines, and key punch machines. The 12th grade is the logical time for teaching business machines. (CH)
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.
ASTER Scheduling Prioritization Function
NASA Technical Reports Server (NTRS)
Cohen, Ron
1996-01-01
ASTER schedules are generated by an automated scheduling system. This scheduler will generate psuedo-optimal schedules based on a priority scheme. This priority scheme is controlled by the Science Team.
NASA Technical Reports Server (NTRS)
Malik, Waqar
2016-01-01
Provide an overview of algorithms used in SARDA (Spot and Runway Departure Advisor) HITL (Human-in-the-Loop) simulation for Dallas Fort-Worth International Airport and Charlotte Douglas International airport. Outline a multi-objective dynamic programming (DP) based algorithm that finds the exact solution to the single runway scheduling (SRS) problem, and discuss heuristics to restrict the search space for the DP based algorithm and provide improvements.
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.
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
Scheduling Software for Complex Scenarios
NASA Technical Reports Server (NTRS)
2006-01-01
Preparing a vehicle and its payload for a single launch is a complex process that involves thousands of operations. Because the equipment and facilities required to carry out these operations are extremely expensive and limited in number, optimal assignment and efficient use are critically important. Overlapping missions that compete for the same resources, ground rules, safety requirements, and the unique needs of processing vehicles and payloads destined for space impose numerous constraints that, when combined, require advanced scheduling. Traditional scheduling systems use simple algorithms and criteria when selecting activities and assigning resources and times to each activity. Schedules generated by these simple decision rules are, however, frequently far from optimal. To resolve mission-critical scheduling issues and predict possible problem areas, NASA historically relied upon expert human schedulers who used their judgment and experience to determine where things should happen, whether they will happen on time, and whether the requested resources are truly necessary.
Mourão-Miranda, Janaina; Hardoon, David R.; Hahn, Tim; Marquand, Andre F.; Williams, Steve C.R.; Shawe-Taylor, John; Brammer, Michael
2011-01-01
Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers. PMID:21723950
Mourão-Miranda, Janaina; Hardoon, David R; Hahn, Tim; Marquand, Andre F; Williams, Steve C R; Shawe-Taylor, John; Brammer, Michael
2011-10-01
Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers.
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.
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.
Oakes, J; Pols, R; Battersby, M; Lawn, S; Pulvirenti, M; Smith, D
2012-09-01
This study aimed to develop an empirically based description of relapse in Electronic Gaming Machine (EGM) problem gambling (PG) by describing the processes and factors that 'pull' the problem gambler away from relapse contrasted with the 'push' towards relapse. These conceptualisations describe two opposing, interacting emotional processes occurring within the problem gambler during any relapse episode. Each relapse episode comprises a complex set of psychological and social behaviours where many factors interact sequentially and simultaneously within the problem gambler to produce a series of mental and behaviour events that end (1) with relapse where 'push' overcomes 'pull' or (2) continued abstinence where 'pull' overcomes 'push'. Four focus groups comprising thirty participants who were EGM problem gamblers, gamblers' significant others, therapists and counsellors described their experiences and understanding of relapse. The groups were recorded, recordings were then transcribed and analysed using thematic textual analysis. It was established that vigilance, motivation to commit to change, positive social support, cognitive strategies such as remembering past gambling harms or distraction techniques to avoid thinking about gambling to enable gamblers to manage the urge to gamble and urge extinction were key factors that protected against relapse. Three complementary theories emerged from the analysis. Firstly, a process of reappraisal of personal gambling behaviour pulls the gambler away from relapse. This results in a commitment to change that develops over time and affects but is independent of each episode of relapse. Secondly, relapse may be halted by interacting factors that 'pull' the problem gambler away from the sequence of mental and behavioural events, which follow the triggering of the urge and cognitions to gamble. Thirdly, urge extinction and apparent 'cure' is possible for EGM gambling. This study provides a qualitative, empirical model for
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 survey of planning and scheduling research at the NASA Ames Research Center
NASA Technical Reports Server (NTRS)
Zweben, Monte
1988-01-01
NASA Ames Research Center has a diverse program in planning and scheduling. This paper highlights some of our research projects as well as some of our applications. Topics addressed include machine learning techniques, action representations and constraint-based scheduling systems. The applications discussed are planetary rovers, Hubble Space Telescope scheduling, and Pioneer Venus orbit scheduling.
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…
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/.
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Scheduling from the perspective of the application
Berman, F.; Wolski, R.
1996-12-31
Metacomputing is the aggregation of distributed and high-performance resources on coordinated networks. With careful scheduling, resource-intensive applications can be implemented efficiently on metacomputing systems at the sizes of interest to developers and users. In this paper we focus on the problem of scheduling applications on metacomputing systems. We introduce the concept of application-centric scheduling in which everything about the system is evaluated in terms of its impact on the application. Application-centric scheduling is used by virtually all metacomputer programmers to achieve performance on metacomputing systems. We describe two successful metacomputing applications to illustrate this approach, and describe AppLeS scheduling agents which generalize the application-centric scheduling approach. Finally, we show preliminary results which compare AppLeS-derived schedules with conventional strip and blocked schedules for a two-dimensional Jacobi code.
Xiao, Yunhai; Wang, Qiuyu; Liu, Lihong
2016-01-01
The main purpose of this study is to propose, then analyze, and later test a spectral gradient algorithm for solving a convex minimization problem. The considered problem covers the matrix ℓ2,1-norm regularized least squares which is widely used in multi-task learning for capturing the joint feature among each task. To solve the problem, we firstly minimize a quadratic approximated model of the objective function to derive a search direction at current iteration. We show that this direction descends automatically and reduces to the original spectral gradient direction if the regularized term is removed. Secondly, we incorporate a nonmonotone line search along this direction to improve the algorithm’s numerical performance. Furthermore, we show that the proposed algorithm converges to a critical point under some mild conditions. The attractive feature of the proposed algorithm is that it is easily performable and only requires the gradient of the smooth function and the objective function’s values at each and every step. Finally, we operate some experiments on synthetic data, which verifies that the proposed algorithm works quite well and performs better than the compared ones. PMID:27861526
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.
Isomap based supporting vector machine
NASA Astrophysics Data System (ADS)
Liang, W. N.
2015-12-01
This research presents a new isomap based supporting vector machine method. Isomap is a dimension reduction method which is able to analyze nonlinear relationship of data on manifolds. Accordingly, support vector machine is established on the isomap manifold to classify given and predict unknown data. A case study of the isomap based supporting vector machine for environmental planning problems is conducted.
NASA Technical Reports Server (NTRS)
Chien, Steve A.; Tran, Daniel Q.; Rabideau, Gregg R.; Schaffer, Steven R.
2011-01-01
Software has been designed to schedule remote sensing with the Earth Observing One spacecraft. The software attempts to satisfy as many observation requests as possible considering each against spacecraft operation constraints such as data volume, thermal, pointing maneuvers, and others. More complex constraints such as temperature are approximated to enable efficient reasoning while keeping the spacecraft within safe limits. Other constraints are checked using an external software library. For example, an attitude control library is used to determine the feasibility of maneuvering between pairs of observations. This innovation can deal with a wide range of spacecraft constraints and solve large scale scheduling problems like hundreds of observations and thousands of combinations of observation sequences.
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.
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
NASA Technical Reports Server (NTRS)
Flarity, L. D.; Hanson, R. J.; Thom, E. H.
1971-01-01
System manages Deep Space Instrumentation Facilities /DSIF/ equipment construction and modification planning. Versatile program applies to such tasks as employee time and task schedules, pay schedules, operations schedules, and plant and equipment procurement, construction, modification or service.
The Hybrid Schedule: Scheduling to the Curriculum.
ERIC Educational Resources Information Center
Boarman, Gerald L.; Kirkpatrick, Barbara S.
1995-01-01
A series of experiments with single and double mod scheduling at a large suburban Maryland high school has led to a highly flexible schedule that meets teachers' and students' needs. This schedule allows courses to be offered in the most suitable format, creates more time for students and teachers, streamlines hallway traffic, and fosters a team…
A Framework for Scheduling Professional Sports Leagues
NASA Astrophysics Data System (ADS)
Nurmi, Kimmo; Goossens, Dries; Bartsch, Thomas; Bonomo, Flavia; Briskorn, Dirk; Duran, Guillermo; Kyngäs, Jari; Marenco, Javier; Ribeiro, Celso C.; Spieksma, Frits; Urrutia, Sebastián; Wolf-Yadlin, Rodrigo
2010-10-01
This paper introduces a framework for a highly constrained sports scheduling problem which is modeled from the requirements of various professional sports leagues. We define a sports scheduling problem, introduce the necessary terminology and detail the constraints of the problem. A set of artificial and real-world instances derived from the actual problems solved for the professional sports league owners are proposed. We publish the best solutions we have found, and invite the sports scheduling community to find solutions to the unsolved instances. We believe that the instances will help researchers to test the value of their solution methods. The instances are available online.
Minimising makespan for two batch-processing machines with non-identical job sizes in job shop
NASA Astrophysics Data System (ADS)
Cheng, Bayi; Yang, Shanlin; Ma, Ying
2012-12-01
In this article, the job shop scheduling problem with two batch-processing machines is considered. The machines have limited capacity and the jobs have non-identical job sizes. The jobs are processed in batches and the total size of each batch cannot exceed the machine capacity. The processing times of a job on the two machines are proportional. We show the problem of minimising makespan is NP-hard in the strong sense. Then we provide an approximation algorithm with worst-case ratio no more than 4, and the running time of the algorithm is O(n log n). Finally, the performance of the proposed algorithm is tested by different levels of instances. Computational results demonstrate the effectiveness of the algorithm for all the instances.
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.
ERIC Educational Resources Information Center
Overturf, Leonard O.; Fastman, Jerry
This document describes a system of computerized advance registration at Colorado State University. The main objective is to generate a schedule that will satisfy student requests and needs, given constraints of faculty, time, and space. The system consists of a series of computer programs and a set of well-documented manual procedures in the…
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.
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.
Noncontingent reinforcement: a further examination of schedule effects during treatment.
Wallace, Michele 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 of 2 NCR schedules in multielement designs, one with and the other without an extinction (EXT) component, while both schedules were thinned. Problem behavior remained low as the NCR schedule with EXT was thinned, but either (a) did not decrease initially or (b) subsequently increased as the NCR schedule without EXT was thinned. These results suggest that dense schedules of NCR decrease behavior by altering its motivating operation but that extinction occurs as the NCR schedule is thinned. The benefits and limitations of using dense or thin NCR schedules are discussed.
NASA Technical Reports Server (NTRS)
Stiefel, M. L.
1983-01-01
The functions and performance characteristics of data base machines (DBM), including machines currently being studied in research laboratories and those currently offered on a commerical basis are discussed. The cost/benefit considerations that must be recognized in selecting a DBM are discussed, as well as the future outlook for such machines.
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.
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.
A Review of Production Scheduling: Theory and Practice
1979-11-01
Cornell University, 1964. 74. S. M. Johnson, "Optimal Two- and Three-Stage Production Schedules with Setup Times Included ", Nav. Res. Log. Quart. 1...on reverae aide If neceaaary and Identify by block number; Production Scheduling Job Shop Scheduling Production Lot Sizing 20. ABSTRACT...decisions. The production scheduling problem is quite different depending on the requirements generation. For the open shop , production scheduling
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.
NASA Astrophysics Data System (ADS)
Shahbaz, Muzammil; Groz, Roland
Automata learning techniques are getting significant importance for their applications in a wide variety of software engineering problems, especially in the analysis and testing of complex systems. In recent studies, a previous learning approach [1] has been extended to synthesize Mealy machine models which are specifically tailored for I/O based systems. In this paper, we discuss the inference of Mealy machines and propose improvements that reduces the worst-time learning complexity of the existing algorithm. The gain over the complexity of the proposed algorithm has also been confirmed by experimentation on a large set of finite state machines.
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.
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
Dypas: A dynamic payload scheduler for shuttle missions
NASA Technical Reports Server (NTRS)
Davis, Stephen
1988-01-01
Decision and analysis systems have had broad and very practical application areas in the human decision making process. These software systems range from the help sections in simple accounting packages, to the more complex computer configuration programs. Dypas is a decision and analysis system that aids prelaunch shutlle scheduling, and has added functionality to aid the rescheduling done in flight. Dypas is written in Common Lisp on a Symbolics Lisp machine. Dypas differs from other scheduling programs in that it can draw its knowledge from different rule bases and apply them to different rule interpretation schemes. The system has been coded with Flavors, an object oriented extension to Common Lisp on the Symbolics hardware. This allows implementation of objects (experiments) to better match the problem definition, and allows a more coherent solution space to be developed. Dypas was originally developed to test a programmer's aptitude toward Common Lisp and the Symbolics software environment. Since then the system has grown into a large software effort with several programmers and researchers thrown into the effort. Dypas is currently using two expert systems and three inferencing procedures to generate a many object schedule. The paper will review the abilities of Dypas and comment on its functionality.
Flexible Modular Scheduling: Results of Evaluations in its Second Decade.
ERIC Educational Resources Information Center
Goldman, Jeri J.
1983-01-01
Reviews the literature on flexible scheduling (FS), with particular emphasis on flexible modular schedules (FMS), in secondary schools. Analyzes problems with FMS that might have contributed to its declining popularity in the 1970s. An extensive bibliography is appended. (AOS)
Perspex machine: VII. The universal perspex machine
NASA Astrophysics Data System (ADS)
Anderson, James A. D. W.
2006-01-01
The perspex machine arose from the unification of projective geometry with the Turing machine. It uses a total arithmetic, called transreal arithmetic, that contains real arithmetic and allows division by zero. Transreal arithmetic is redefined here. The new arithmetic has both a positive and a negative infinity which lie at the extremes of the number line, and a number nullity that lies off the number line. We prove that nullity, 0/0, is a number. Hence a number may have one of four signs: negative, zero, positive, or nullity. It is, therefore, impossible to encode the sign of a number in one bit, as floating-point arithmetic attempts to do, resulting in the difficulty of having both positive and negative zeros and NaNs. Transrational arithmetic is consistent with Cantor arithmetic. In an extension to real arithmetic, the product of zero, an infinity, or nullity with its reciprocal is nullity, not unity. This avoids the usual contradictions that follow from allowing division by zero. Transreal arithmetic has a fixed algebraic structure and does not admit options as IEEE, floating-point arithmetic does. Most significantly, nullity has a simple semantics that is related to zero. Zero means "no value" and nullity means "no information." We argue that nullity is as useful to a manufactured computer as zero is to a human computer. The perspex machine is intended to offer one solution to the mind-body problem by showing how the computable aspects of mind and, perhaps, the whole of mind relates to the geometrical aspects of body and, perhaps, the whole of body. We review some of Turing's writings and show that he held the view that his machine has spatial properties. In particular, that it has the property of being a 7D lattice of compact spaces. Thus, we read Turing as believing that his machine relates computation to geometrical bodies. We simplify the perspex machine by substituting an augmented Euclidean geometry for projective geometry. This leads to a general
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
Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model
NASA Astrophysics Data System (ADS)
Nouri, Houssem Eddine; Belkahla Driss, Olfa; Ghédira, Khaled
2017-05-01
The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic multiagent model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.
Ada task scheduling: A focused Ada investigation
NASA Technical Reports Server (NTRS)
Legrand, Sue
1988-01-01
The types of control that are important for real time task scheduling are discussed. Some closely related real time issues are mentioned and major committee and research activities in this area are delineated. Although there are some problems with Ada and its real time task scheduling, Ada presents fewer than any known alternative. Ada was designed for the domain of real time embedded systems, but Ada compilers may not contain a level of task scheduling support that is adequate for all real time applications. The question addressed is which implementations of Ada's task scheduling are adequate for effective real time systems for NASA applications.
Sanotra, G S; Lund, J Damkjer; Vestergaard, K S
2002-07-01
1. The aims of this study were to determine (1) the effect of light-dark schedules on the walking ability, the risk of tibial dyschondroplasia (TD) as well as the duration of tonic immobility (TI) reactions in commercial broiler flocks and (2) the effect of a daily dark period and reduced density on the behaviour of broiler chickens. 2. Experiment 1. Group 1 had a 2 to 8 h daily dark period from 2 to 26 d of age (light-dark programme A) at a stocking density of 28.4 chicks/m2. Group 2 had 8 h of darkness daily from 2 to 38 d of age (light-dark programme B) at 24 chicks/m2. The control group had 24 h continuous light at 28.4 chicks/m2. 3. Experiment 2. Behaviour was studied with and without a daily 8 h dark period and at high (30 chicks/m2) and low (18 chicks/m2) stocking densities. 4. Programme B reduced the prevalence of impaired walking ability, corresponding to gait score > 2, when compared with controls. The effect on walking ability corresponding to gait score > 0 approached significance. 5. Both light-dark programmes reduced the occurrence of TD. Programme B (combined with reduced stocking density), however, had the greater effect. 6. Both light-dark programmes reduced the duration of TI, compared with controls (mean = 426 s) Programme B resulted in a larger reduction (alpha = -156.9 s) than programme A (alpha = -117.0). 7. The proportions of chicks drinking, eating, pecking, scratching, standing and performing vertical wing-shakes increased--both when the 8 h dark period and the reduced stocking density were applied separately and in combination (experiment 2). 8. For all behaviours, except standing, the effect of the dark period was largest in broilers kept at the high stocking density (d 40).
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
NASA Astrophysics Data System (ADS)
Vincent, Graylan
2003-01-01
A CNC mill was flown aboard NASA's KC-135 ``Weightless Wonder'' microgravity research aircraft to investigate the effect of gravity on the machining process and to demonstrate the feasibility and functionality of a CNC mill in a weightless environment, such as aboard the International Space Station. The experiment hypothesis was that the surface roughness of milling cuts made in microgravity would be of higher quality than cuts made in a gravitational environment due to increased chip removal. The technical problems associated with microgravity machining (such as the chip removal and collection process), and the engineering solutions to these problems were also evaluated in this experiment.
Workshop on Fielded Applications of Machine Learning
1994-05-11
This report summaries the talks presented at the Workshop on Fielded Applications of Machine Learning , and draws some initial conclusions about the state of machine learning and its potential for solving real-world problems.
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...
Robust stochastic mine production scheduling
NASA Astrophysics Data System (ADS)
Kumral, Mustafa
2010-06-01
The production scheduling of open pit mines aims to determine the extraction sequence of blocks such that the net present value (NPV) of a mining project is maximized under capacity and access constraints. This sequencing has significant effect on the profitability of the mining venture. However, given that the values of coefficients in the optimization procedure are obtained in a medium of sparse data and unknown future events, implementations based on deterministic models may lead to destructive consequences to the company. In this article, a robust stochastic optimization (RSO) approach is used to deal with mine production scheduling in a manner such that the solution is insensitive to changes in input data. The approach seeks a trade off between optimality and feasibility. The model is demonstrated on a case study. The findings showed that the approach can be used in mine production scheduling problems efficiently.
Scheduling Linearly Indexed Assignment Codes
NASA Astrophysics Data System (ADS)
Kailath, Thomas; Roychowdhury, Vwani P.
1989-05-01
It has been recently shown that linearly indexed Assignment Codes can be efficiently used for coding several problems especially in signal processing and matrix algebra. In fact, mathematical expressions for many algorithms are directly in the form of linearly indexed codes, and examples include the formulas for matrix multiplication, any m-dimensional convolution/correlation, matrix transposition, and solving matrix Lyapunov's equation. Systematic procedures for converting linearly indexed Assignment Codes to localized algorithms that are closely related to Regular Iterative Algorithms (RIAs) have also been developed. These localized algorithms can be often efficiently scheduled by modeling them as RIAs; however, it is not always efficient to do so. In this paper we shall analyze and develop systematic procedures for determining efficient schedules directly for the linearly indexed ACs and the localized algorithms. We shall also illustrate our procedures by determining schedules for examples such as matrix transposition and Gauss-Jordan elimination algorithm.
Feature-based telescope scheduler
NASA Astrophysics Data System (ADS)
Naghib, Elahesadat; Vanderbei, Robert J.; Stubbs, Christopher
2016-07-01
Feature-based Scheduler offers a sequencing strategy for ground-based telescopes. This scheduler is designed in the framework of Markovian Decision Process (MDP), and consists of a sub-linear online controller, and an offline supervisory control-optimizer. Online control law is computed at the moment of decision for the next visit, and the supervisory optimizer trains the controller by simulation data. Choice of the Differential Evolution (DE) optimizer, and introducing a reduced state space of the telescope system, offer an efficient and parallelizable optimization algorithm. In this study, we applied the proposed scheduler to the problem of Large Synoptic Survey Telescope (LSST). Preliminary results for a simplified model of LSST is promising in terms of both optimality, and computational cost.
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.
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.
A Decentralized Scheduling Policy for a Dynamically Reconfigurable Production System
NASA Astrophysics Data System (ADS)
Giordani, Stefano; Lujak, Marin; Martinelli, Francesco
In this paper, the static layout of a traditional multi-machine factory producing a set of distinct goods is integrated with a set of mobile production units - robots. The robots dynamically change their work position to increment the product rate of the different typologies of products in respect to the fluctuations of the demands and production costs during a given time horizon. Assuming that the planning time horizon is subdivided into a finite number of time periods, this particularly flexible layout requires the definition and the solution of a complex scheduling problem, involving for each period of the planning time horizon, the determination of the position of the robots, i.e., the assignment to the respective tasks in order to minimize production costs given the product demand rates during the planning time horizon.
Interference Cognizant Network Scheduling
NASA Technical Reports Server (NTRS)
Varadarajan, Srivatsan (Inventor); Hall, Brendan (Inventor); Smithgall, William Todd (Inventor); Bonk, Ted (Inventor); DeLay, Benjamin F. (Inventor)
2017-01-01
Systems and methods for interference cognizant network scheduling are provided. In certain embodiments, a method of scheduling communications in a network comprises identifying a bin of a global timeline for scheduling an unscheduled virtual link, wherein a bin is a segment of the timeline; identifying a pre-scheduled virtual link in the bin; and determining if the pre-scheduled and unscheduled virtual links share a port. In certain embodiments, if the unscheduled and pre-scheduled virtual links don't share a port, scheduling transmission of the unscheduled virtual link to overlap with the scheduled transmission of the pre-scheduled virtual link; and if the unscheduled and pre-scheduled virtual links share a port: determining a start time delay for the unscheduled virtual link based on the port; and scheduling transmission of the unscheduled virtual link in the bin based on the start time delay to overlap part of the scheduled transmission of the pre-scheduled virtual link.
Process Development and Micro-Machining of MARBLE Foam-Cored Rexolite Hemi-Shell Ablator Capsules
Randolph, Randall Blaine; Oertel, John A.; Schmidt, Derek William; ...
2016-06-30
For this study, machined CH hemi-shell ablator capsules have been successfully produced by the MST-7 Target Fabrication Team at Los Alamos National Laboratory. Process development and micro-machining techniques have been developed to produce capsules for both the Omega and National Ignition Facility (NIF) campaigns. These capsules are gas filled up to 10 atm and consist of a machined plastic hemi-shell outer layer that accommodates various specially engineered low-density polystyrene foam cores. Machining and assembly of the two-part, step-jointed plastic hemi-shell outer layer required development of new techniques, processes, and tooling while still meeting very aggressive shot schedules for both campaigns.more » Finally, problems encountered and process improvements will be discussed that describe this very unique, complex capsule design approach through the first Omega proof-of-concept version to the larger NIF version.« less
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. 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.
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. 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.
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
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
Techniques for cash management in scheduling manufacturing operations
NASA Astrophysics Data System (ADS)
Morady Gohareh, Mehdy; Shams Gharneh, Naser; Ghasemy Yaghin, Reza
2017-10-01
The objective in traditional scheduling is usually time based. Minimizing the makespan, total flow times, total tardi costs, etc. are instances of these objectives. In manufacturing, processing each job entails a cost paying and price receiving. Thus, the objective should include some notion of managing the flow of cash. We have defined two new objectives: maximization of average and minimum available cash. For single machine scheduling, it is demonstrated that scheduling jobs in decreasing order of profit ratios maximizes the former and improves productivity. Moreover, scheduling jobs in increasing order of costs and breaking ties in decreasing order of prices maximizes the latter and creates protection against financial instability.
Techniques for cash management in scheduling manufacturing operations
NASA Astrophysics Data System (ADS)
Morady Gohareh, Mehdy; Shams Gharneh, Naser; Ghasemy Yaghin, Reza
2016-10-01
The objective in traditional scheduling is usually time based. Minimizing the makespan, total flow times, total tardi costs, etc. are instances of these objectives. In manufacturing, processing each job entails a cost paying and price receiving. Thus, the objective should include some notion of managing the flow of cash. We have defined two new objectives: maximization of average and minimum available cash. For single machine scheduling, it is demonstrated that scheduling jobs in decreasing order of profit ratios maximizes the former and improves productivity. Moreover, scheduling jobs in increasing order of costs and breaking ties in decreasing order of prices maximizes the latter and creates protection against financial instability.
El-Refaie, Ayman Mohamed Fawzi [Niskayuna, NY; Reddy, Patel Bhageerath [Madison, WI
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.
Electrical machines with superconducting windings. Part 3: Homopolar dc machines
NASA Astrophysics Data System (ADS)
Kullman, D.; Henninger, P.
1981-01-01
The losses in rotating liquid metal contacts and the problems in including liquid metals were theoretically and experimentally studied. These machines are shown realiable. For electric ship propulsion, they are a more efficient method of power transmission than mechanical gearboxes. However, weight reduction as compared to mechanical gearboxes can hardly be achieved with machines fully shielded by magnetic iron.
ERIC Educational Resources Information Center
Monfette, Ronald J.
1986-01-01
Argues that college publications, including class schedules, must be accurate, timely, and easy to read and follow. Describes Schoolcraft College's unified format approach to publications marketing. Offers suggestions on the design, format, and distribution of class schedules. (DMM)
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 ...
Instant 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 ...
School Construction Scheduling.
ERIC Educational Resources Information Center
Delaney, J. B.
1983-01-01
Explains that favorable market and working conditions influence the scheduling of school construction projects. Facility planners, architects, and contractors are advised to develop a realistic time schedule for the entire project. (MLF)
NASA Technical Reports Server (NTRS)
Smith, Greg
2003-01-01
Schedule Risk Assessment needs to determine the probability of finishing on or before a given point in time. Task in a schedule should reflect the "most likely" duration for each task. IN reality, each task is different and has a varying degree of probability of finishing within or after the duration specified. Schedule risk assessment attempt to quantify these probabilities by assigning values to each task. Bridges the gap between CPM scheduling and the project's need to know the likelihood of "when".
37 CFR 6.1 - International schedule of classes of goods and services.
Code of Federal Regulations, 2014 CFR
2014-07-01
... classes of goods and services. 6.1 Section 6.1 Patents, Trademarks, and Copyrights UNITED STATES PATENT... ACT § 6.1 International schedule of classes of goods and services. Goods 1. Chemicals used in industry... classes; ores. 7. Machines and machine tools; motors and engines (except for land vehicles); machine...
37 CFR 6.1 - International schedule of classes of goods and services.
Code of Federal Regulations, 2013 CFR
2013-07-01
... classes of goods and services. 6.1 Section 6.1 Patents, Trademarks, and Copyrights UNITED STATES PATENT... ACT § 6.1 International schedule of classes of goods and services. Goods 1. Chemicals used in industry... classes; ores. 7. Machines and machine tools; motors and engines (except for land vehicles); machine...
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....
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, 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, 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....
Section 207(f)(2) of the E-Gov Act requires federal agencies to develop an inventory and establish a schedule of information to be published on their Web sites, make those schedules available for public comment. To post the schedules on the web site.
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.
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.
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…
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…
"When Can I Have Your Kids?" Scheduling Specialist Teachers.
ERIC Educational Resources Information Center
Rettig, Michael D.; Canady, Robert Lynn
1995-01-01
This information brief describes problems involved in scheduling elementary-school specialist teachers and offers suggestions for resolving them. Poor scheduling results in fragmented classes, unequal distribution of instructional time, and lack of common planning time. Poor scheduling is usually due to lack of congruence between school mission…
Introduction to Exploring Machines
ERIC Educational Resources Information Center
Early Childhood Today, 2006
2006-01-01
Young children are fascinated by how things "work." They are at a stage of development where they want to experiment with the many ways to use an object or take things apart and put them back together. In the process of exploring tools and machines, children use the scientific method and problem-solving skills. They observe how things work, wonder…
Introduction to Exploring Machines
ERIC Educational Resources Information Center
Early Childhood Today, 2006
2006-01-01
Young children are fascinated by how things "work." They are at a stage of development where they want to experiment with the many ways to use an object or take things apart and put them back together. In the process of exploring tools and machines, children use the scientific method and problem-solving skills. They observe how things work, wonder…
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.
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.
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.
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.
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)
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.
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.
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.
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…
Schedule-Organizer Computer Program
NASA Technical Reports Server (NTRS)
Collazo, Fernando F.
1990-01-01
Schedule Organizer provides simple method for generating distribution lists. Contains readers' names for each task schedule defined by input files. Schedule Organizer (SO), Schedule Tracker (ST) (COSMIC program MSC-21526), and Schedule Report Generator (SRG) (COSMIC program MSC-21527) computer programs manipulating data-base files in ways advantageous in scheduling. Written in PL/1 and DEC Command Language (DCL).
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.
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.
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.
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.
Brown coal preparation machines
Bleckmann, H.; Sitte, W.; Kellerwessel, H.
1981-05-01
Lignite usually requires comminuting and screening before being used as a fuel in power plants. Reduction machines normally used for coarse crushing bituminous coal, such as jaw crushers, roll crushers, and impact crushers, are not generally suitable for lignite as they require a brittle feed and large grain size. In contrast to these requirements, lignite can be easily compressed and has a small grain size. Therefore, special crusher types have been developed for the coarse reduction of lignite. These machines resemble roll crushers but subject the feed to shearing and tearing forces rather than to compressive stress. It is often necessary to screen the lignite to remove the undersize or to limit the maximum particle size before the next comminution process. Screening the lignite is a particularly difficult operation due to the high water content and the presence of clay minerals which tend to clog the screening machines. These problems can be overcome with multi-roll sizers.
Optimal outpatient appointment scheduling.
Kaandorp, Guido C; Koole, Ger
2007-09-01
In this paper optimal outpatient appointment scheduling is studied. A local search procedure is derived that converges to the optimal schedule with a weighted average of expected waiting times of patients, idle time of the doctor and tardiness (lateness) as objective. No-shows are allowed to happen. For certain combinations of parameters the well-known Bailey-Welch rule is found to be the optimal appointment schedule.
Deng, Qianwang; Gong, Xuran; Zhang, Like; Liu, Wei; Ren, Qinghua
2017-01-01
Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed. PMID:28458687
Deng, Qianwang; Gong, Guiliang; Gong, Xuran; Zhang, Like; Liu, Wei; Ren, Qinghua
2017-01-01
Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.
NASA Astrophysics Data System (ADS)
Searle, Anthony; Petrachenko, Bill
2016-12-01
The VLBI Global Observing System (VGOS) has been designed to take advantage of advances in data recording speeds and storage capacity, allowing for smaller and faster antennas, wider bandwidths, and shorter observation durations. Here, schedules for a ``realistic" VGOS network, frequency sequences, and expanded source lists are presented using a new source-based scheduling algorithm. The VGOS aim for continuous observations presents new operational challenges. As the source-based strategy is independent of the observing network, there are operational advantages which allow for more flexible scheduling of continuous VLBI observations. Using VieVS, simulations of several schedules are presented and compared with previous VGOS studies.
A unified model for scheduling elective admissions.
Barber, R W
1977-01-01
A model is presented that deals with two problems not previously solved: it handles the random arrival of requests for admission and permits continuous updating of scheduling decisions in a dynamic process, and it provides global long-term optimization of patient census rather than a series of suboptimal short-term solutions. The model can be used either for continuous dynamic scheduling or for periodic static scheduling. It is usable with many different system objectives and levels of computer resources, although an on-line computer is needed for its most effective use in the dynamic mode. PMID:591351
A Flexible Nurse Scheduling Support System
Ozkarahan, Irem
1987-01-01
Scheduling nursing personnel in hospitals is very complex because of the variety of conflicting interests and objectives. Also, demand varies 24-hour 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.
Scheduling a Medium-Sized Manufacturing Shop: A Simulation Study
1993-09-01
The original aim of this research was to ex=aine the hybrid of management techniques used by the CNC machine shop to accomplish its metamorphosis...examine the management techniques in March 1993, 1 was somewhat surprised by the lack of a well-defined scheduling system to handle the manufacturing...see in an actual manufacturing environment *hose scheduling and shop floor control techniques taught in production oriented courses. In fact, I was
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.
Quantum-Enhanced Machine Learning.
Dunjko, Vedran; Taylor, Jacob M; Briegel, Hans J
2016-09-23
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.
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.
Nicholson, Daniel J
2013-12-01
The machine conception of the organism (MCO) is one of the most pervasive notions in modern biology. However, it has not yet received much attention by philosophers of biology. The MCO has its origins in Cartesian natural philosophy, and it is based on the metaphorical redescription of the organism as a machine. In this paper I argue that although organisms and machines resemble each other in some basic respects, they are actually very different kinds of systems. I submit that the most significant difference between organisms and machines is that the former are intrinsically purposive whereas the latter are extrinsically purposive. Using this distinction as a starting point, I discuss a wide range of dissimilarities between organisms and machines that collectively lay bare the inadequacy of the MCO as a general theory of living systems. To account for the MCO's prevalence in biology, I distinguish between its theoretical, heuristic, and rhetorical functions. I explain why the MCO is valuable when it is employed heuristically but not theoretically, and finally I illustrate the serious problems that arise from the rhetorical appeal to the MCO. Copyright © 2013 Elsevier Ltd. All rights reserved.
Optimal factory scheduling using stochastic dominance A
Wurman, P.R.
1996-12-31
Generating optimal production schedules for manufacturing facilities an area of great theoretical and practical importance. During the last decade, an effort has been made to reconcile the techniques developed by the AI and OR communities. The work described here aims to continue in this vein by showing how a class of well-defined stochastic scheduling problems can be mapped into a general search procedure. This approach improves upon other methods by handling the general case of multidimensional stochastic costs.
Nonlinear neural network for deterministic scheduling
Gulati, S.; Iyengar, S.S.; Toomarian, N.; Protopopescu, V.; Barhen, J.
1988-01-01
This paper addresses the NP-complete, deterministic scheduling problem for a single server system. Given a set of n tasks along with the precedence-constraints among them, their timing requirements, setup costs and their completion deadlines, a neuromorphic model is used to construct a non-preemptive optimal processing schedule such that the total completion time, total tarediness and the number of tardy jobs is minimized. This model exhibits faster convergence than techniques based on gradient projection methods.
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
Anaesthesia Machine: Checklist, Hazards, Scavenging
Goneppanavar, Umesh; Prabhu, Manjunath
2013-01-01
From a simple pneumatic device of the early 20th century, the anaesthesia machine has evolved to incorporate various mechanical, electrical and electronic components to be more appropriately called anaesthesia workstation. Modern machines have overcome many drawbacks associated with the older machines. However, addition of several mechanical, electronic and electric components has contributed to recurrence of some of the older problems such as leak or obstruction attributable to newer gadgets and development of newer problems. No single checklist can satisfactorily test the integrity and safety of all existing anaesthesia machines due to their complex nature as well as variations in design among manufacturers. Human factors have contributed to greater complications than machine faults. Therefore, better understanding of the basics of anaesthesia machine and checking each component of the machine for proper functioning prior to use is essential to minimise these hazards. Clear documentation of regular and appropriate servicing of the anaesthesia machine, its components and their satisfactory functioning following servicing and repair is also equally important. Trace anaesthetic gases polluting the theatre atmosphere can have several adverse effects on the health of theatre personnel. Therefore, safe disposal of these gases away from the workplace with efficiently functioning scavenging system is necessary. Other ways of minimising atmospheric pollution such as gas delivery equipment with negligible leaks, low flow anaesthesia, minimal leak around the airway equipment (facemask, tracheal tube, laryngeal mask airway, etc.) more than 15 air changes/hour and total intravenous anaesthesia should also be considered. PMID:24249887
Anaesthesia machine: checklist, hazards, scavenging.
Goneppanavar, Umesh; Prabhu, Manjunath
2013-09-01
From a simple pneumatic device of the early 20(th) century, the anaesthesia machine has evolved to incorporate various mechanical, electrical and electronic components to be more appropriately called anaesthesia workstation. Modern machines have overcome many drawbacks associated with the older machines. However, addition of several mechanical, electronic and electric components has contributed to recurrence of some of the older problems such as leak or obstruction attributable to newer gadgets and development of newer problems. No single checklist can satisfactorily test the integrity and safety of all existing anaesthesia machines due to their complex nature as well as variations in design among manufacturers. Human factors have contributed to greater complications than machine faults. Therefore, better understanding of the basics of anaesthesia machine and checking each component of the machine for proper functioning prior to use is essential to minimise these hazards. Clear documentation of regular and appropriate servicing of the anaesthesia machine, its components and their satisfactory functioning following servicing and repair is also equally important. Trace anaesthetic gases polluting the theatre atmosphere can have several adverse effects on the health of theatre personnel. Therefore, safe disposal of these gases away from the workplace with efficiently functioning scavenging system is necessary. Other ways of minimising atmospheric pollution such as gas delivery equipment with negligible leaks, low flow anaesthesia, minimal leak around the airway equipment (facemask, tracheal tube, laryngeal mask airway, etc.) more than 15 air changes/hour and total intravenous anaesthesia should also be considered.
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.
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.
Empirical results on scheduling and dynamic backtracking
NASA Technical Reports Server (NTRS)
Boddy, Mark S.; Goldman, Robert P.
1994-01-01
At the Honeywell Technology Center (HTC), we have been working on a scheduling problem related to commercial avionics. This application is large, complex, and hard to solve. To be a little more concrete: 'large' means almost 20,000 activities, 'complex' means several activity types, periodic behavior, and assorted types of temporal constraints, and 'hard to solve' means that we have been unable to eliminate backtracking through the use of search heuristics. At this point, we can generate solutions, where solutions exist, or report failure and sometimes why the system failed. To the best of our knowledge, this is among the largest and most complex scheduling problems to have been solved as a constraint satisfaction problem, at least that has appeared in the published literature. This abstract is a preliminary report on what we have done and how. In the next section, we present our approach to treating scheduling as a constraint satisfaction problem. The following sections present the application in more detail and describe how we solve scheduling problems in the application domain. The implemented system makes use of Ginsberg's Dynamic Backtracking algorithm, with some minor extensions to improve its utility for scheduling. We describe those extensions and the performance of the resulting system. The paper concludes with some general remarks, open questions and plans for future work.