Scheduling unrelated parallel machines with sequence-dependent setup times

Zeidi, Javad Rezaeian; Mohammad-Hosseini, Samir

doi:10.1007/s00170-015-7215-y

Cited by 20 publications

(14 citation statements)

References 25 publications

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“…UPMSPs with due-date related objectives are classical scheduling problems that have been comprehensively researched for decades [23]. In particular, several studies have addressed SDFST as main constraints [24]- [27]. To reduce the number of setups in a scheduling problem, batch scheduling heuristics were popularly adopted by forming batches of jobs processed on a machine without a setup [28].…”

Section: Literature Review a Upmsps With Sdfst Under Due-dates Constraintsmentioning

confidence: 99%

Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family Setups

Paeng

Park

2021

IEEE Access

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Parallel machine scheduling with sequence-dependent family setups has attracted much attention from academia and industry due to its practical applications. In a real-world manufacturing system, however, solving the scheduling problem becomes challenging since it is required to address urgent and frequent changes in demand and due-dates of products. To minimize the total tardiness of the scheduling problem, we propose a deep reinforcement learning (RL) based scheduling framework in which trained neural networks (NNs) are able to solve unseen scheduling problems without re-training even when such changes occur. Specifically, we propose state and action representations whose dimensions are independent of production requirements and due-dates of jobs while accommodating family setups. At the same time, an NN architecture with parameter sharing was utilized to improve the training efficiency. Extensive experiments demonstrate that the proposed method outperforms the recent metaheuristics, rule-based, and other RL-based methods in terms of total tardiness. Moreover, the computation time for obtaining a schedule by our framework is shorter than those of the metaheuristics and other RL-based methods.INDEX TERMS Deep reinforcement learning, unrelated parallel machine scheduling, sequence-dependent family setups, total tardiness objective, deep Q-network.

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Section: Literature Review a Upmsps With Sdfst Under Due-dates Constraintsmentioning

confidence: 99%

Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family Setups

Paeng

Park

2021

IEEE Access

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“…In this case, the setup times do not depend on the sequence. Zeidi and Mohammad Hosseini [41] formulate a new mathematical model to minimize the total cost of tardiness and earliness on unrelated parallel machines with considering sequencedependent setup times constraints. They also propose an integrated metaheuristic and evaluated it under the relevant existing benchmark test data.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimize Unrelated Parallel Machines Scheduling Problems With Multiple Limited Additional Resources, Sequence-Dependent Setup Times and Release Date Constraints

Al-Harkan

Qamhan

2019

IEEE Access

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This paper considers the problem of scheduling a set of jobs on unrelated parallel machines subject to several constraints which are non-zero arbitrary release dates, limited additional resources, and non-anticipatory sequence-dependent setup times. The objective function is to minimize the maximum completion time. In order to find an optimal solution for this problem, a new mixed-integer linear programming model (MILP) is presented. Moreover, a two-stage hybrid metaheuristic based on variable neighborhood search hybrid and simulated annealing (TVNS_SA) is proposed. In the first stage, a developed heuristic is used to find an initial solution with good quality. At the second stage, the obtained initial solution is used as the first neighborhood structures in the proposed metaheuristic, for further progress different neighborhood structures and effective resolution schemes are also presented. The computational results indicate that the proposed metaheuristic is capable of obtaining optimal solutions for most of the instances when compared to the solution obtained by the developed mixed-integer linear programming model. In addition, the metaheuristic dominated the MILP with respect to computing time. The overall evaluation of the proposed algorithm shows its efficiency and effectiveness when compared with other algorithms. Finally, in order to obtain rigorous and fair conclusions, a paired t-test has been conducted to test the significant differences between the five variants of the TVNS_SA.

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“…The unrelated parallel machine scheduling problem, or very similar variations, has been widely considered over recent decades. Some recent results can be found in Zeidi and MohammadHosseini (2015) and Chen (2015). A review of parallel machine scheduling problems can be found in Kravchenko and Werner (2011).…”

Section: Literature Reviewmentioning

confidence: 99%

Heuristic algorithms for the unrelated parallel machine scheduling problem with one scarce additional resource

Villa

Vallada

Fanjul-Peyro

2018

Expert Systems with Applications

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Villa Juliá, MF.; Vallada Regalado, E.; Fanjul Peyró, L. (2018). Heuristic algorithms for the unrelated parallel machine scheduling problem with one scarce additional resource. Expert Systems with Applications. 93:28-38. https://doi. AbstractIn this paper, we study the unrelated parallel machine scheduling problem with one scarce additional resource to minimise the maximum completion time of the jobs or makespan. Several heuristics are proposed following two strategies: the first one is based on the consideration of the resource constraint during the whole solution construction process. The second one starts from several assignment rules without considering the resource constraint, and repairs the non feasible assignments in order to obtain a feasible solution. Several computation experiments are carried out over an extensive benchmark. A comparative evaluation against previously proposed mathematical models and matheuristics (combination of mathematical models and heuristics) is carried out. From the results, we can conclude that our methods outperform the existing ones, and the second strategy performs better, especially for large instances.

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Scheduling unrelated parallel machines with sequence-dependent setup times

Cited by 20 publications

References 25 publications

Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family Setups

Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family Setups

Optimize Unrelated Parallel Machines Scheduling Problems With Multiple Limited Additional Resources, Sequence-Dependent Setup Times and Release Date Constraints

Heuristic algorithms for the unrelated parallel machine scheduling problem with one scarce additional resource

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