A parallel hybrid PSO-GA algorithm for the flexible flow-shop scheduling with transportation

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dc.authoridErfan Babaee Tirkolaee / 0000-0003-1664-9210en_US
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874en_US
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017en_US
dc.contributor.authorAmirteimoori, Arash
dc.contributor.authorMahdavi, Iraj
dc.contributor.authorSolimanpur, Maghsud
dc.contributor.authorAli, Sadia Samar
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2022-11-07T07:03:51Z
dc.date.available2022-11-07T07:03:51Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractIn this paper, a Mixed-Integer Linear Programming (MILP) model to simultaneously schedule jobs and transporters in a flexible flow shop system is suggested. Wherein multiple jobs, finite transporters, and stages with parallel unrelated machines are considered. In addition to the mentioned technicalities, the jobs are able to omit one or more stages, and may not be executable by all the machines, and similarly, transportable by all the transporters. To the best of our knowledge, no study in the literature has featured efficacy of the parallel computing in simultaneous scheduling of jobs and transporters in the flexible flow shop system which remarkably shortens run time if the solution approaches are designed accordingly. To this end, we employ Gurobi solver, Parallel Genetic Algorithm (PGA), Parallel Particle Swarm Optimization (PPSO) and hybrid Parallel PSO-GA Algorithm (PPSOGA) to deal with the problem instances. Furthermore, a parallel version of Ant Colony Optimization (ACO) algorithm adapted from the state-of-the-art literature is developed to verify the performance of our suggested solution methods. Using 60 problem instances generated via uniform distribution, the suggested solution approaches are compared against one another. After assessing the results of the computational experiments, it is deduced that PPSOGA algorithm outperforms PGA, PPSO, Parallel Ant Colony Optimization (PACO) and Gurobi solver in terms of the quality of the solutions. The efficiency and run time of the suggested approaches are then assessed through two prominent statistical tests (i.e., Wald and Analysis of Variance (ANOVA)). Eventually, it comes to spotlight that PPSOGA algorithm is computationally rewarding and dependable.en_US
dc.identifier.citationAmirteimoori, A., Mahdavi, I., Solimanpur, M., Ali, S. S., & Tirkolaee, E. B. (2022). A parallel hybrid PSO-GA algorithm for the flexible flow-shop scheduling with transportation. Computers and Industrial Engineering, 173 doi:10.1016/j.cie.2022.108672en_US
dc.identifier.doi10.1016/j.cie.2022.108672en_US
dc.identifier.scopus2-s2.0-85139332388en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.cie.2022.108672
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3236
dc.identifier.volume173en_US
dc.identifier.wosWOS:000875853700003en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTirkolaee, Erfan Babaee
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofComputers and Industrial Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFlexible Flow Shop Schedulingen_US
dc.subjectHybrid Parallel PSO-GA Algorithmen_US
dc.subjectMetaheuristicsen_US
dc.subjectMixed-Integer Linear Programmingen_US
dc.subjectParallel Computingen_US
dc.titleA parallel hybrid PSO-GA algorithm for the flexible flow-shop scheduling with transportationen_US
dc.typeArticleen_US

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