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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Vydané v:	IEEE transactions on evolutionary computation Ročník 25; číslo 4; s. 651 - 665
Hlavní autori:	Zhang, Fangfang, Mei, Yi, Nguyen, Su, Zhang, Mengjie, Tan, Kay Chen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
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