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

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 geneti...

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Veröffentlicht in:Engineering optimization Jg. 57; H. 3; S. 786 - 812
Hauptverfasser: Deng, Jiawen, Zhang, Jihui, Yang, Shengxiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 04.03.2025
Taylor & Francis Ltd
Schlagworte:
Assembly
Distributed assembly problem
Genetic algorithms
genetic programming
Job shop scheduling
manufacturing
Operators
Permutations
Programming
scheduling
Setup times
Simulated annealing
ISSN:0305-215X, 1029-0273
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2024.2335284