Surrogate-Assisted Evolutionary Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling

Dynamic flexible job shop scheduling (JSS) is an important combinatorial optimization problem with complex routing and sequencing decisions under dynamic environments. Genetic programming (GP), as a hyperheuristic approach, has been successfully applied to evolve scheduling heuristics for JSS. Howev...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 25; H. 4; S. 651 - 665
Hauptverfasser:	Zhang, Fangfang, Mei, Yi, Nguyen, Su, Zhang, Mengjie, Tan, Kay Chen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Combinatorial analysis Dynamic flexible job shop scheduling (DFJSS) Dynamic scheduling Evolutionary algorithms Genetic algorithms genetic programming (GP) Heuristic Heuristic algorithms hyperheuristics Job shop scheduling Job shops Knowledge management Learning multitask learning Optimization Processor scheduling Scheduling Sequential analysis Statistics surrogate Task analysis Task scheduling Training
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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