Intelligent optimization under blocking constraints: A novel iterated greedy algorithm for the hybrid flow shop group scheduling problem

This paper introduces a new flow shop combinatorial optimization problem, called the blocking hybrid flow shop group scheduling problem (BHFGSP). In the problem, no buffers exist between any adjacent machines, and a set of jobs with different sequence-dependent setup times needs to be scheduled and...

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Veröffentlicht in:Knowledge-based systems Jg. 258; S. 109962
Hauptverfasser: Qin, Haoxiang, Han, Yuyan, Wang, Yuting, Liu, Yiping, Li, Junqing, Pan, Quanke
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 22.12.2022
Schlagworte:
Blocking
Hybrid flow shop group scheduling problem
Iterated greedy algorithm
Makespan
Neighborhood probabilistic selection strategies
Blocking
Iterated greedy algorithm
Makespan
Hybrid flow shop group scheduling problem
Neighborhood probabilistic selection strategies
ISSN:0950-7051, 1872-7409
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Beschreibung
Zusammenfassung:This paper introduces a new flow shop combinatorial optimization problem, called the blocking hybrid flow shop group scheduling problem (BHFGSP). In the problem, no buffers exist between any adjacent machines, and a set of jobs with different sequence-dependent setup times needs to be scheduled and processed at organized manufacturing cells. We verify the correctness of the mathematical model of BHFGSP by using CPLEX. In this paper, we proposed a novel iterated greedy algorithm to solve the problem. The proposed algorithm has two key techniques. One is the decoding procedure that calculates the makespan of a job sequence, and the other is the neighborhood probabilistic selection strategies with families and blocking-based jobs. The performance of the proposed algorithm is investigated through a large number of numerical experiments. Comprehensive results show that the proposed algorithm is effective in solving BHFGSP.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2022.109962