Multi-agent scheduling for shared manufacturing systems

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dc.contributor.authorDuran, Egeen
dc.contributor.authorOzturk, Cemalettinen
dc.contributor.authorO'Sullivan, Barryen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-04-23T11:12:32Z
dc.date.available2025-04-23T11:12:32Z
dc.date.issued2025en
dc.description.abstractAdvances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDuran, E., Ozturk, C. and O'Sullivan, B. (2025) ‘Multi-agent scheduling for shared manufacturing systems’, Procedia Computer Science, 253, pp. 727–736. https://doi.org/10.1016/j.procs.2025.01.134en
dc.identifier.doi10.1016/j.procs.2025.01.134en
dc.identifier.endpage736en
dc.identifier.journaltitleProcedia Computer Scienceen
dc.identifier.startpage727en
dc.identifier.urihttps://hdl.handle.net/10468/17294
dc.identifier.volume253en
dc.language.isoenen
dc.publisherElsevier B.V.en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Centres for Research Training (CRT) Programme/18/CRT/6223/IE/SFI Centre for Research Training in Artificial Intelligence/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/National Challenge Fund::Digital for Resilience Challenge/22/NCF/DR/11264/IE/Deep Learning based Transferrable Supply Chain Stress Test/en
dc.rights© 2025, the Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 6th International Conference on Industry 4.0 and Smart Manufacturingen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0en
dc.subjectMultiagent systemsen
dc.subjectSchedulingen
dc.subjectPlanningen
dc.subjectParallel machineen
dc.subject~Computer Science - Journal Articles~en
dc.titleMulti-agent scheduling for shared manufacturing systemsen
dc.typeArticle (peer-reviewed)en
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