Multi-objective energy-efficient scheduling in two-stage hybrid flowshop under consideration of no-wait

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dc.check.date2026-07-12en
dc.check.infoAccess to this article is restricted until 12 months after publication by request of the publisheren
dc.contributor.authorMissaoui, Ahmeden
dc.contributor.authorO'Sullivan, Barryen
dc.contributor.editorFujita, Hamidoen
dc.contributor.editorWatanobe, Yutakaen
dc.contributor.editorAli, Moonisen
dc.contributor.editorWang, Yinglinen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-09-15T10:55:00Z
dc.date.available2025-09-15T10:55:00Z
dc.date.issued2025-07-12en
dc.description.abstractThe industrial sector is one of the world’s largest energy consumers. Moreover, the fluctuating energy costs make effective energy management a critical challenge for the manufacturing industry. To address these challenges, companies are increasingly focusing on optimizing energy-efficient scheduling practices. In the current work, we addressed the two-stage no-wait hybrid flowshop problem, aiming to minimize the total completion time and energy consumption. We initially introduced a mixed integer linear programming (MILP) model when the augmented epsilon-constraint is employed to generate the optimal Pareto front for small instances. In the second step, an efficient bi-objective iterated local search algorithm is introduced to solve a benchmark of 100 instances. The results obtained are compared against those from the Non-dominated Sorting Genetic Algorithm. The comparative analysis demonstrates our proposal’s superior effectiveness.en
dc.description.sponsorshipScience Foundation Ireland (Grant number 12/RC/2289-P2)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMissaoui, A. and O'Sullivan, B. (2025) 'Multi-objective energy-efficient scheduling in two-stage hybrid flowshop under consideration of no-wait', in Fujita, H., Watanobe, Y., Ali, M. and Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2025. Lecture Notes in Computer Science,15707, pp. 464-475. Springer, Singapore. https://doi.org/10.1007/978-981-96-8892-0_39en
dc.identifier.doi10.1007/978-981-96-8892-0_39en
dc.identifier.endpage475en
dc.identifier.isbn9789819688913en
dc.identifier.isbn9789819688920en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.journaltitleLecture Notes in Computer Scienceen
dc.identifier.startpage464en
dc.identifier.urihttps://hdl.handle.net/10468/17873
dc.identifier.volume15707en
dc.language.isoenen
dc.publisherSpringer Natureen
dc.relation.ispartofLecture Notes in Computer Scienceen
dc.relation.ispartofAdvances and Trends in Artificial Intelligence. Theory and Applicationsen
dc.relation.ispartofIEA/AIE 2025, Kitakyushu, Japan, July 1-4, 2025en
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, under exclusive license to Springer Nature Singapore Pte Ltd. This version of the paper has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-981-96-8892-0_39en
dc.subjectHybrid flowshopen
dc.subjectNo-waiten
dc.subjectIterated local searchen
dc.subjectMulti-objective optimizationen
dc.subjectMakespanen
dc.subjectEnergy consumptionen
dc.titleMulti-objective energy-efficient scheduling in two-stage hybrid flowshop under consideration of no-waiten
dc.typeArticle (peer-reviewed)en
dc.typebook-chapteren
dc.typeConference itemen
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