A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem

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dc.authoridSel, Çağrı/0000-0002-8657-2303
dc.authoridTürker, Ahmet Kürşad/0000-0001-6686-9241
dc.authoridİnal, Ali Fırat/0000-0001-7747-0746
dc.authoridERSOZ, Suleyman/0000-0002-7534-6837
dc.contributor.authorİnal, Ali Fırat
dc.contributor.authorSel, Çağrı
dc.contributor.authorAktepe, Adnan
dc.contributor.authorTürker, Ahmet Kürşad
dc.contributor.authorErsöz, Süleyman
dc.date.accessioned2025-01-21T16:35:01Z
dc.date.available2025-01-21T16:35:01Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
dc.description.abstractIn a production environment, scheduling decides job and machine allocations and the operation sequence. In a job shop production system, the wide variety of jobs, complex routes, and real-life events becomes challenging for scheduling activities. New, unexpected events disrupt the production schedule and require dynamic scheduling updates to the production schedule on an event-based basis. To solve the dynamic scheduling problem, we propose a multi-agent system with reinforcement learning aimed at the minimization of tardiness and flow time to improve the dynamic scheduling techniques. The performance of the proposed multi-agent system is compared with the first-in-first-out, shortest processing time, and earliest due date dispatching rules in terms of the minimization of tardy jobs, mean tardiness, maximum tardiness, mean earliness, maximum earliness, mean flow time, maximum flow time, work in process, and makespan. Five scenarios are generated with different arrival intervals of the jobs to the job shop production system. The results of the experiments, performed for the 3 x 3, 5 x 5, and 10 x 10 problem sizes, show that our multi-agent system overperforms compared to the dispatching rules as the workload of the job shop increases. Under a heavy workload, the proposed multi-agent system gives the best results for five performance criteria, which are the proportion of tardy jobs, mean tardiness, maximum tardiness, mean flow time, and maximum flow time.
dc.identifier.doi10.3390/su15108262
dc.identifier.issn2071-1050
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85160833480
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/su15108262
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24059
dc.identifier.volume15
dc.identifier.wosWOS:000997783900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectdynamic job shop scheduling problem; multi-agent system; reinforcement learning; Industry 4; 0; dispatching rules
dc.titleA Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem
dc.typeArticle

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