A hybrid genetic programming algorithm for the distributed assembly scheduling problems with transportation and sequence-dependent setup times

dc.contributor.authorDeng, Jiawen
dc.contributor.authorZhang, Jihui
dc.contributor.authorYang, Shengxiang
dc.date.acceptance2024-03-21
dc.date.accessioned2024-05-02T13:15:34Z
dc.date.available2024-05-02T13:15:34Z
dc.date.issued2024-04-23
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.description.abstractThis paper investigates a distributed assembly permutation flow-shop scheduling problem with transportation and sequence-dependent set-up times (DAPFSP-TSDST). A hybrid genetic programming (HGP) algorithm is proposed to optimize the makespan of the assembly stage, which inherits the merits of genetic programming (GP) and neighbourhood search operators. In HGP, a hybrid problem-specific initialization heuristic is developed to make populations more diverse. Multiple neighbourhood search operators are employed as the leaf nodes, which are vital for the success of GP. A product shift strategy is proposed to strengthen its exploitability. In addition, a simulated annealing criterion is adopted to make the HGP explore more thoroughly. Finally, statistical and computational experiments are carried out on the benchmark instances. The results exhaustively identify the notable competitiveness of the HGP algorithm in coping with the DAPFSP-TSDST.
dc.funderOther external funder (please detail below)
dc.funder.otherNational Natural Science Foundation of China
dc.funder.otherNatural Science Foundation of Shandong Province, China
dc.identifier.citationDeng, J., Zhang, J. and Yang, S. (2024) A hybrid genetic programming algorithm for the distributed assembly scheduling problems with transportation and sequence-dependent setup times. Engineering Optimization,
dc.identifier.doihttps://doi.org/10.1080/0305215X.2024.2335284
dc.identifier.urihttps://hdl.handle.net/2086/23756
dc.language.isoen
dc.peerreviewedYes
dc.projectid61673228, 62072260
dc.projectidZR2020MF094
dc.publisherTaylor and Francis
dc.researchinstituteInstitute of Artificial Intelligence (IAI)
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDistributed assembly problem
dc.subjectGenetic programming
dc.subjectScheduling
dc.subjectManufacturing
dc.titleA hybrid genetic programming algorithm for the distributed assembly scheduling problems with transportation and sequence-dependent setup times
dc.typeArticle

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