An Ordered Flow Shop Scheduling Problem

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Özet

The subject of the study is ordered flow shop scheduling problems which were first seen in the literature in the 1970s. The main objective is to get a fast and good solution. For the purpose of the study, firstly, the ordered flow shop scheduling problems were defined. Then, a heuristic method was suggested for the ordered flow shop scheduling problems and a sample problem was solved and discussed. A genetic algorithm (GA) based on the complexity property was developed. Full enumeration was applied to determine the optimal makespan values based on Smith’s rule because Smith had specified the conditions under which circumstances the permutation can be optimum. This study is one of the few studies in the literature to obtain optimum solutions up to 15 jobs ordered flow shop problems in a very short amount of time. The developed GA heuristic can also solve large size ordered flow shop scheduling problems very fast. The significant advantage of the proposed method is that, while Smith’s method rule not does not work with large size problems since it requires full enumeration to identify best solution to follow a convexity property where GA algorithm can find a good solution very fast.

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dc.contributor.authorÇakmak, Aslıhan
dc.contributor.authorCeylan, Zeynep
dc.contributor.authorBulkan, Serol
dc.contributor.editorŞen, Z.
dc.contributor.editorUygun, O.
dc.contributor.editorErden, C.
dc.date.accessioned2026-10-27T23:47:41.512728728Z
dc.date.available2026-10-27T23:47:41.512728728Z
dc.date.issued2024
dc.description.abstractThe subject of the study is ordered flow shop scheduling problems which were first seen in the literature in the 1970s. The main objective is to get a fast and good solution. For the purpose of the study, firstly, the ordered flow shop scheduling problems were defined. Then, a heuristic method was suggested for the ordered flow shop scheduling problems and a sample problem was solved and discussed. A genetic algorithm (GA) based on the complexity property was developed. Full enumeration was applied to determine the optimal makespan values based on Smith’s rule because Smith had specified the conditions under which circumstances the permutation can be optimum. This study is one of the few studies in the literature to obtain optimum solutions up to 15 jobs ordered flow shop problems in a very short amount of time. The developed GA heuristic can also solve large size ordered flow shop scheduling problems very fast. The significant advantage of the proposed method is that, while Smith’s method rule not does not work with large size problems since it requires full enumeration to identify best solution to follow a convexity property where GA algorithm can find a good solution very fast.
dc.identifier.doi10.1007/978-981-99-6062-0_16
dc.identifier.endpage173
dc.identifier.isbn978-981996061-3
dc.identifier.issn2195-4356
dc.identifier.scopus2-s2.0-85174633180
dc.identifier.startpage161
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/kostu/10627
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.conference12th International Symposium on Intelligent Manufacturing and Service Systems, IMSS 2023
dc.relation.ispartofLecture Notes in Mechanical Engineering
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectConvexity property
dc.subjectGenetic Algorithms
dc.subjectMakespan
dc.subjectOrdered flowshop
dc.subjectScheduling
dc.subject.otherHeuristic methods
dc.subject.otherMachine shop practice
dc.subject.otherCondition
dc.subject.otherConvexity properties
dc.subject.otherFlow shop scheduling problem
dc.subject.otherFlow-shops
dc.subject.otherMakespan
dc.subject.otherOptimum solution
dc.subject.otherOrdered flowshop
dc.subject.otherProperty
dc.subject.otherScheduling
dc.subject.otherValue-based
dc.subject.otherGenetic algorithms
dc.titleAn Ordered Flow Shop Scheduling Problem
dc.typeConference Object
rdl.citation.openalex2
rdl.citation.scopus1
rdl.conference.date26 May 2023 through 28 May 2023
rdl.conference.placeIstanbul
rdl.oa.statusclosed
rdl.scopus.quartileQ4
rdl.source.indexSCOPUS