A hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems

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dc.authorscopusidReza Tavakkoli Moghaddam / 57207533714
dc.authorwosidReza Tavakkoli Moghaddam / P-1948-2015
dc.contributor.authorNessari, Saman
dc.contributor.authorTavakkoli Moghaddam, Reza
dc.contributor.authorBakhshi Khaniki, Hessam
dc.contributor.authorBozorgi Amiri, Ali
dc.date.accessioned2025-04-18T08:28:19Z
dc.date.available2025-04-18T08:28:19Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractThe flexible job shop scheduling problem (FJSSP) is a complex optimization challenge that plays a crucial role in enhancing productivity and efficiency in modern manufacturing systems, aimed at optimizing the allocation of jobs to a variable set of machines. This paper introduces an algorithm to tackle the FJSSP by minimizing makespan and total weighted earliness and tardiness under uncertainty. This hybrid algorithm effectively addresses the complexities of stochastic multi-objective optimization by integrating the equilibrium optimizer (EO) as an initial solutions generator, Non-dominated sorting genetic algorithm II (NSGA-II), and simulation techniques. The algorithm's effectiveness is validated by showcasing specific instances and delivering decision results for optimal scheduling across varying levels of uncertainty. Results reveal the algorithm's consistent superiority in managing the complexities of stochastic parameters across various problem scales, achieving lower makespan and improved Pareto front quality compared to existing methods. Particularly notable is the algorithm's faster convergence and robust performance, as validated by the statistical Wilcoxon test, which confirms its reliability and efficacy in handling dynamic scheduling situations. These findings underscore the algorithm's potential in providing flexible, robust solutions. The proposed algorithm's unique balance of exploitative and explorative capabilities within a simulation framework enables effective handling of uncertainty in the FJSSP, offering flexibility and customization that is adaptable to various scheduling environments. © 2024 The Author(s)
dc.identifier.citationNessari, S., Tavakkoli-Moghaddam, R., Bakhshi-Khaniki, H., & Bozorgi-Amiri, A. (2024). A hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems. Decision Analytics Journal, 100485.
dc.identifier.doi10.1016/j.dajour.2024.100485
dc.identifier.issn27726622
dc.identifier.scopus2-s2.0-85195096699
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.dajour.2024.100485
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6569
dc.identifier.volume11
dc.indekslendigikaynakScopus
dc.institutionauthorTavakkoli Moghaddam, Reza
dc.institutionauthoridReza Tavakkoli Moghaddam / 0000-0002-6757-926X
dc.language.isoen
dc.publisherElsevier Inc.
dc.relation.ispartofDecision Analytics Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectEquilibrium Optimizer
dc.subjectFlexible Job Shop Scheduling
dc.subjectMulti-objective Stochastic Optimization
dc.subjectNon-dominated Sorting Genetic Algorithm
dc.subjectSimheuristics
dc.titleA hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems
dc.typeArticle

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