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

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dc.authoridSel, Cagri/0000-0002-8657-2303
dc.authoridINAL, ALI FIRAT/0000-0001-7747-0746
dc.authoridTurker, Ahmet Kursad/0000-0001-6686-9241
dc.authoridERSOZ, Suleyman/0000-0002-7534-6837
dc.contributor.authorInal, Ali Firat
dc.contributor.authorSel, Cagri
dc.contributor.authorAktepe, Adnan
dc.contributor.authorTurker, Ahmet Kursad
dc.contributor.authorErsoz, Suleyman
dc.date.accessioned2024-09-29T16:08:15Z
dc.date.available2024-09-29T16:08:15Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
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.en_US
dc.identifier.doi10.3390/su15108262
dc.identifier.issn2071-1050
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85160833480en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/su15108262
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7433
dc.identifier.volume15en_US
dc.identifier.wosWOS:000997783900001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofSustainabilityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdynamic job shop scheduling problemen_US
dc.subjectmulti-agent systemen_US
dc.subjectreinforcement learningen_US
dc.subjectIndustry 4en_US
dc.subject0en_US
dc.subjectdispatching rulesen_US
dc.titleA Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problemen_US
dc.typeArticleen_US

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