A Multi-objective Reinforcement Learning Algorithm for JSSP

Méndez-Hernández, Beatriz M.; Rodríguez-Bazan, Erick D.; Martínez-Jiménez, Yailen; Libin, Pieter; Nowé, Ann

doi:10.1007/978-3-030-30487-4_44

Cited by 9 publications

(14 citation statements)

References 36 publications

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“…We also apply an analytical tuning parameter method to these algorithms. Finally, we compare the achieved results with those obtained with the JSSP algorithm cited below in [8,14].…”

Section: Introductionmentioning

confidence: 99%

“…However, it is common for the industrial scheduling requirements to have several objectives, and then the Multi-Objective JSSP (MOJSSP) becomes an even more significant challenge. Thus, many industrial production areas require the multi-objective approach [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, as we mention below, few works in the literature solve JSSP instances with more than two objectives and applying more than two metrics to evaluate their performance. Nevertheless, for MOJSSP, the number of objectives and performance metrics remains too small [8,[13][14][15]. The works of Zhao [14] and Mendez [8] are exceptions because the authors have presented implementations with two or three significant objective functions and two performance metrics.…”

Section: Introductionmentioning

confidence: 99%

“…MOJSSP has been commonly solved using IMOEA/D [14], NSGA-II [18], SPEA [19], MOPSO [20], and CMOEA [21]; the latter was renamed CMEA in [8]. Nevertheless, the number of objectives and performance metrics of these algorithms remains too small.…”

Section: Introductionmentioning

confidence: 99%

“…The Multi-Objective Q-Learning algorithm (MOQL) for JSSP was published in 2017 [23]; this approach uses several agents to solve JSSP. An extension of MOQL is MOMARLA, which was proposed in 2019 by Mendez [8]. This MOJSSP algorithm uses two objective functions: makespan and total tardiness.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Chaotic Multi-Objective Simulated Annealing and Threshold Accepting for Job Shop Scheduling Problem

Frausto–Solís

Hernández-Ramírez

Castilla-Valdez

et al. 2021

MCA

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The Job Shop Scheduling Problem (JSSP) has enormous industrial applicability. This problem refers to a set of jobs that should be processed in a specific order using a set of machines. For the single-objective optimization JSSP problem, Simulated Annealing is among the best algorithms. However, in Multi-Objective JSSP (MOJSSP), these algorithms have barely been analyzed, and the Threshold Accepting Algorithm has not been published for this problem. It is worth mentioning that the researchers in this area have not reported studies with more than three objectives, and the number of metrics they used to measure their performance is less than two or three. In this paper, we present two MOJSSP metaheuristics based on Simulated Annealing: Chaotic Multi-Objective Simulated Annealing (CMOSA) and Chaotic Multi-Objective Threshold Accepting (CMOTA). We developed these algorithms to minimize three objective functions and compared them using the HV metric with the recently published algorithms, MOMARLA, MOPSO, CMOEA, and SPEA. The best algorithm is CMOSA (HV of 0.76), followed by MOMARLA and CMOTA (with HV of 0.68), and MOPSO (with HV of 0.54). In addition, we show a complexity comparison of these algorithms, showing that CMOSA, CMOTA, and MOMARLA have a similar complexity class, followed by MOPSO.

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“…We also apply an analytical tuning parameter method to these algorithms. Finally, we compare the achieved results with those obtained with the JSSP algorithm cited below in [8,14].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Chaotic Multi-Objective Simulated Annealing and Threshold Accepting for Job Shop Scheduling Problem

Frausto–Solís

Hernández-Ramírez

Castilla-Valdez

et al. 2021

MCA

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A practical guide to multi-objective reinforcement learning and planning

Hayes

Rădulescu

Bargiacchi

et al. 2022

Auton Agent Multi-Agent Syst

Self Cite

121

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Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems.

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