A hybrid genetic programming algorithm for the distributed assembly scheduling problems with transportation and sequence-dependent setup times
dc.contributor.author | Deng, Jiawen | |
dc.contributor.author | Zhang, Jihui | |
dc.contributor.author | Yang, Shengxiang | |
dc.date.acceptance | 2024-03-21 | |
dc.date.accessioned | 2024-05-02T13:15:34Z | |
dc.date.available | 2024-05-02T13:15:34Z | |
dc.date.issued | 2024-04-23 | |
dc.description | The 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.abstract | This 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.funder | Other external funder (please detail below) | |
dc.funder.other | National Natural Science Foundation of China | |
dc.funder.other | Natural Science Foundation of Shandong Province, China | |
dc.identifier.citation | Deng, 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.doi | https://doi.org/10.1080/0305215X.2024.2335284 | |
dc.identifier.uri | https://hdl.handle.net/2086/23756 | |
dc.language.iso | en | |
dc.peerreviewed | Yes | |
dc.projectid | 61673228, 62072260 | |
dc.projectid | ZR2020MF094 | |
dc.publisher | Taylor and Francis | |
dc.researchinstitute | Institute of Artificial Intelligence (IAI) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | en |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Distributed assembly problem | |
dc.subject | Genetic programming | |
dc.subject | Scheduling | |
dc.subject | Manufacturing | |
dc.title | A hybrid genetic programming algorithm for the distributed assembly scheduling problems with transportation and sequence-dependent setup times | |
dc.type | Article |
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