Population-based Tabu search with evolutionary strategies for permutation flow shop scheduling problems under effects of position-dependent learning and linear deterioration

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dc.authoridArik, Oguzhan Ahmet/0000-0002-7088-2104
dc.contributor.authorArık, Oğuzhan Ahmet
dc.date.accessioned2025-02-24T17:18:55Z
dc.date.available2025-02-24T17:18:55Z
dc.date.issued2021
dc.departmentFakülteler, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractThis paper investigates permutation flow shop scheduling (PFSS) problems under the effects of position-dependent learning and linear deterioration. In a PFSS problem, there arenjobs andmmachines in series. Jobs are separated into operations onm different machines in series, and jobs have to follow the same machine order with the same sequence. The PFSS problem under the effects of learning and deterioration is introduced with a mixed-integer nonlinear programming model. The time requirement for solving large-scale problems type of PFSS problem is exceedingly high. Therefore, well-known metaheuristic methods for the PFSS problem without learning and deterioration effects such as iterated greedy algorithms and discrete differential evolution algorithm are adapted for the problem with learning and deterioration effects in order to find a faster and near-optimal or optimal solution for the problem. Furthermore, this paper proposes a hybrid solution algorithm that is called population-based Tabu search algorithm (TSPOP) with evolutionary strategies such as crossover and mutation. The search algorithm is built on the basic structure of Tabu search and it searches for the best candidate from a solution population instead of improving the current best candidate at each iteration. Furthermore, the performances of these methods in view of solution quality are discussed in this paper by using test problems for 20, 50, and 100 jobs with 5, 10, 20 machines. Experimental results show that the proposed TS(POP)algorithm outperforms the other existing algorithms in view of solution quality.
dc.identifier.doi10.1007/s00500-020-05234-7
dc.identifier.endpage1518
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85089194284
dc.identifier.scopusqualityQ1
dc.identifier.startpage1501
dc.identifier.urihttps://doi.org/10.1007/s00500-020-05234-7
dc.identifier.urihttps://hdl.handle.net/20.500.14440/915
dc.identifier.volume25
dc.identifier.wosWOS:000557146700002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorArik, Oguzhan Ahmet
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofSoft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250201
dc.subjectPermutation flow shop scheduling
dc.subjectLearning effect
dc.subjectDeterioration effect
dc.subjectIterated greedy
dc.subjectDiscrete differential equation
dc.subjectMakespan
dc.subjectTabu search
dc.subjectEvolutionary strategy
dc.titlePopulation-based Tabu search with evolutionary strategies for permutation flow shop scheduling problems under effects of position-dependent learning and linear deterioration
dc.typeArticle

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