An effective hybrid biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem

•The BBO algorithm is deeply studied by integrating several novel local search heuristics.•A hybrid algorithm called HBBO is proposed for solving the DAPFSP.•The performance of the HBBO is evaluated by using 1710 benchmark instances.•New best solutions are obtained by the proposed hybrid scheme. Dis...

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Veröffentlicht in:	Computers & industrial engineering Jg. 97; S. 128 - 136
Hauptverfasser:	Lin, Jian, Zhang, Shuai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Ltd 01.07.2016 Pergamon Press Inc
Schlagworte:	Algorithms Assembly Biogeography Biogeography-based optimization Computer simulation Distributed assembly permutation flow-shop scheduling problem Evolutionary computations Heuristic Job shops Local search Manufacturers Mathematical models Optimization Optimization algorithms Permutations Scheduling Studies Supply chains Distributed assembly permutation flow-shop scheduling problem Evolutionary computations Local search Biogeography-based optimization
ISSN:	0360-8352, 1879-0550
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Abstract	•The BBO algorithm is deeply studied by integrating several novel local search heuristics.•A hybrid algorithm called HBBO is proposed for solving the DAPFSP.•The performance of the HBBO is evaluated by using 1710 benchmark instances.•New best solutions are obtained by the proposed hybrid scheme. Distributed assembly permutation flow-shop scheduling problem (DAPFSP) is widely exists in modern supply chains and manufacturing systems. In this paper, an effective hybrid biogeography-based optimization (HBBO) algorithm that integrates several novel heuristics is proposed to solve the DAPFSP with the objective of minimizing the makespan. Firstly, the path relinking heuristic is employed in the migration phase as product local search strategy to optimize the assembly sequence. Secondly, an insertion-based heuristic is used in the mutation phase to determine the job permutation for each product. Then, a novel local search method is designed based on the problem characteristics and embedded in the HBBO scheme to further improve the most promising individual. Finally, computational simulations on 900 small-sized instances and 810 large-sized instances are conducted to demonstrate the effectiveness of the proposed algorithm, and the new best known solutions for 162 instances are found.
AbstractList	Distributed assembly permutation flow-shop scheduling problem (DAPFSP) is widely exists in modern supply chains and manufacturing systems. In this paper, an effective hybrid biogeography-based optimization (HBBO) algorithm that integrates several novel heuristics is proposed to solve the DAPFSP with the objective of minimizing the makespan. Firstly, the path relinking heuristic is employed in the migration phase as product local search strategy to optimize the assembly sequence. Secondly, an insertion-based heuristic is used in the mutation phase to determine the job permutation for each product. Then, a novel local search method is designed based on the problem characteristics and embedded in the HBBO scheme to further improve the most promising individual. Finally, computational simulations on 900 small-sized instances and 810 large-sized instances are conducted to demonstrate the effectiveness of the proposed algorithm, and the new best known solutions for 162 instances are found. •The BBO algorithm is deeply studied by integrating several novel local search heuristics.•A hybrid algorithm called HBBO is proposed for solving the DAPFSP.•The performance of the HBBO is evaluated by using 1710 benchmark instances.•New best solutions are obtained by the proposed hybrid scheme. Distributed assembly permutation flow-shop scheduling problem (DAPFSP) is widely exists in modern supply chains and manufacturing systems. In this paper, an effective hybrid biogeography-based optimization (HBBO) algorithm that integrates several novel heuristics is proposed to solve the DAPFSP with the objective of minimizing the makespan. Firstly, the path relinking heuristic is employed in the migration phase as product local search strategy to optimize the assembly sequence. Secondly, an insertion-based heuristic is used in the mutation phase to determine the job permutation for each product. Then, a novel local search method is designed based on the problem characteristics and embedded in the HBBO scheme to further improve the most promising individual. Finally, computational simulations on 900 small-sized instances and 810 large-sized instances are conducted to demonstrate the effectiveness of the proposed algorithm, and the new best known solutions for 162 instances are found.
Author	Lin, Jian Zhang, Shuai
Author_xml	– sequence: 1 givenname: Jian orcidid: 0000-0003-1265-5314 surname: Lin fullname: Lin, Jian email: linjian1001@126.com – sequence: 2 givenname: Shuai surname: Zhang fullname: Zhang, Shuai
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Keywords	Distributed assembly permutation flow-shop scheduling problem Evolutionary computations Local search Biogeography-based optimization
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Snippet	•The BBO algorithm is deeply studied by integrating several novel local search heuristics.•A hybrid algorithm called HBBO is proposed for solving the... Distributed assembly permutation flow-shop scheduling problem (DAPFSP) is widely exists in modern supply chains and manufacturing systems. In this paper, an...
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SubjectTerms	Algorithms Assembly Biogeography Biogeography-based optimization Computer simulation Distributed assembly permutation flow-shop scheduling problem Evolutionary computations Heuristic Job shops Local search Manufacturers Mathematical models Optimization Optimization algorithms Permutations Scheduling Studies Supply chains
Title	An effective hybrid biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem
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