Multitask Multiobjective Genetic Programming for Automated Scheduling Heuristic Learning in Dynamic Flexible Job-Shop Scheduling

Evolutionary multitask multiobjective learning has been widely used for handling more than one multiobjective task simultaneously. However, it is rarely used in dynamic combinatorial optimization problems, which have valuable practical applications such as dynamic flexible job-shop scheduling (DFJSS...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 53; H. 7; S. 4473 - 4486
Hauptverfasser: Zhang, Fangfang, Mei, Yi, Nguyen, Su, Zhang, Mengjie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Business competition
Combinatorial analysis
Dynamic job-shop scheduling
Dynamic scheduling
Genetic algorithms
genetic programming (GP)
Heuristic
Heuristic algorithms
Heuristic job shop scheduling
hyperheuristics
Job shop scheduling
Knowledge sharing
Learning
multiobjective
Multiple objective analysis
multitask learning
Optimization
Scheduling
Sociology
Solution space
Statistics
Strategy
Task analysis
Task scheduling
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:Evolutionary multitask multiobjective learning has been widely used for handling more than one multiobjective task simultaneously. However, it is rarely used in dynamic combinatorial optimization problems, which have valuable practical applications such as dynamic flexible job-shop scheduling (DFJSS) in manufacturing. Genetic programming (GP), as a popular hyperheuristic approach, has been used to learn scheduling heuristics for generating schedules for multitask single-objective DFJSS only. Searching in the heuristic space with GP is more difficult than in the solution space, since a small change on heuristics can lead to ineffective or even infeasible solutions. Multiobjective DFJSS is more challenging than single DFJSS, since a scheduling heuristic needs to cope with multiple objectives. To tackle this challenge, we first propose a multipopulation-based multitask multiobjective GP algorithm to preserve the quality of the learned scheduling heuristics for each task. Furthermore, we develop a multitask multiobjective GP algorithm with a task-oriented knowledge-sharing strategy to further improve the effectiveness of learning scheduling heuristics for DFJSS. The results show that the designed multipopulation-based GP algorithms, especially the one with the task-oriented knowledge-sharing strategy, can achieve good performance for all the examined tasks by maintaining the quality and diversity of individuals for corresponding tasks well. The learned Pareto fronts also show that the GP algorithm with task-oriented knowledge-sharing strategy can learn competitive scheduling heuristics for DFJSS on both of the objectives.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2022.3196887