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
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Shrnutí: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. However, its training process is time consuming, and it faces the retraining problem once the characteristics of job shop scenarios vary. It is known that multitask learning is a promising paradigm for solving multiple tasks simultaneously by sharing knowledge among the tasks. To improve the training efficiency and effectiveness, this article proposes a novel surrogate-assisted evolutionary multitask algorithm via GP to share useful knowledge between different scheduling tasks. Specifically, we employ the phenotypic characterization for measuring the behaviors of scheduling rules and building a surrogate for each task accordingly. The built surrogates are used not only to improve the efficiency of solving each single task but also for knowledge transfer in multitask learning with a large number of promising individuals. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for all scenarios. In addition, the proposed algorithm manages to solve multiple tasks collaboratively in terms of the evolved scheduling heuristics for different tasks in a multitask scenario.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2021.3065707