Metaheuristic algorithms for the hybrid flowshop scheduling problem

dc.contributor.author Hande Oztop
dc.contributor.author M. Fatih Tasgetiren
dc.contributor.author D. T. Eliiyi
dc.contributor.author Quanke Pan
dc.date.accessioned 2025-10-06T17:51:13Z
dc.date.issued 2019
dc.description.abstract The hybrid flowshop scheduling problem (HFSP) has been widely studied in the literature as it has many real-life applications in industry. Even though many solution approaches have been presented for the HFSP with makespan criterion studies on HFSP with total flow time minimization have been rather limited. This study presents a mathematical model four variants of iterated greedy algorithms and a variable block insertion heuristic for the HFSP with total flow time minimization. Based on the well-known NEH heuristic an efficient constructive heuristic is also proposed and compared with NEH. A detailed design of experiment is carried out to calibrate the parameters of the proposed algorithms. The HFSP benchmark suite is used for evaluating the performance of the proposed methods. As there are only 10 large instances in the current literature further 30 large instances are proposed as new benchmarks. The developed model is solved for all instances on CPLEX under a time limit and the performances of the proposed algorithms are assessed through comparisons with the results from CPLEX and the two best-performing algorithms in literature. Computational results show that the proposed algorithms are very effective in terms of solution time and quality. Additionally the proposed algorithms are tested on large instances for the makespan criterion which reveal that they also perform superbly for the makespan objective. Especially for instances with 30 jobs the proposed algorithms are able to find the current incumbent makespan values reported in literature and provide three new best solutions. © 2019 Elsevier B.V. All rights reserved.
dc.identifier.doi 10.1016/j.cor.2019.06.009
dc.identifier.issn 03050548
dc.identifier.issn 0305-0548
dc.identifier.uri https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068125455&doi=10.1016%2Fj.cor.2019.06.009&partnerID=40&md5=a6b60e05a30592bf38c8209901488126
dc.identifier.uri https://gcris.yasar.edu.tr/handle/123456789/9352
dc.language.iso English
dc.publisher Elsevier Ltd
dc.relation.ispartof Computers & Operations Research
dc.source Computers and Operations Research
dc.subject Block Insertion Heuristic, Hybrid Flowshop Scheduling, Iterated Greedy Algorithm, Makespan, Total Flow Time, Benchmarking, Design Of Experiments, Scheduling, Block Insertion Heuristic, Hybrid Flow Shop Scheduling, Iterated Greedy Algorithm, Makespan, Total Flowtime, Heuristic Algorithms
dc.subject Benchmarking, Design of experiments, Scheduling, Block insertion heuristic, Hybrid flow shop scheduling, Iterated greedy algorithm, Makespan, Total flowtime, Heuristic algorithms
dc.title Metaheuristic algorithms for the hybrid flowshop scheduling problem
dc.type Article
dspace.entity.type Publication
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gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.endpage 196
gdc.description.startpage 177
gdc.description.volume 111
gdc.identifier.openalex W2951537749
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gdc.oaire.isgreen true
gdc.oaire.keywords Makespan
gdc.oaire.keywords Deterministic scheduling theory in operations research
gdc.oaire.keywords total flow time
gdc.oaire.keywords hybrid flowshop scheduling
gdc.oaire.keywords makespan
gdc.oaire.keywords Total Flow Time
gdc.oaire.keywords Hybrid Flowshop Scheduling
gdc.oaire.keywords Approximation methods and heuristics in mathematical programming
gdc.oaire.keywords iterated greedy algorithm
gdc.oaire.keywords block insertion heuristic
gdc.oaire.keywords Block Insertion Heuristic
gdc.oaire.keywords Iterated Greedy Algorithm
gdc.oaire.popularity 5.865619E-8
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gdc.oaire.sciencefields 0211 other engineering and technologies
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gdc.opencitations.count 80
gdc.plumx.crossrefcites 85
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oaire.citation.endPage 196
oaire.citation.startPage 177
person.identifier.scopus-author-id Oztop- Hande (57194232319), Tasgetiren- M. Fatih (6505799356), Eliiyi- D. T. (14521079300), Pan- Quanke (15074237600)
project.funder.name M. Fatih Tasgetiren and Quan-Ke Pan acknowledge the HUST Project in Wuhan in China. They are supported by the National Natural Science Foundation of China (grant no: 51435009 ). The authors would also like to thank the anonymous referees for their insightful comments on a previous version of the paper.
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