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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Abstract	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.
AbstractList	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.
Author	Zhang, Fangfang Mei, Yi Zhang, Mengjie Tan, Kay Chen Nguyen, Su
Author_xml	– sequence: 1 givenname: Fangfang orcidid: 0000-0001-5516-3972 surname: Zhang fullname: Zhang, Fangfang email: fangfang.zhang@ecs.vuw.ac.nz organization: Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand – sequence: 2 givenname: Yi orcidid: 0000-0003-0682-1363 surname: Mei fullname: Mei, Yi email: yi.mei@ecs.vuw.ac.nz organization: Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand – sequence: 3 givenname: Su orcidid: 0000-0003-2365-1087 surname: Nguyen fullname: Nguyen, Su email: p.nguyen4@latrobe.edu.au organization: Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, Australia – sequence: 4 givenname: Mengjie orcidid: 0000-0003-4463-9538 surname: Zhang fullname: Zhang, Mengjie email: mengjie.zhang@ecs.vuw.ac.nz organization: Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand – sequence: 5 givenname: Kay Chen orcidid: 0000-0002-6802-2463 surname: Tan fullname: Tan, Kay Chen email: kctan@polyu.edu.hk organization: Department of Computing, Hong Kong Polytechnic University, Hong Kong
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SubjectTerms	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
Title	Surrogate-Assisted Evolutionary Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling
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