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

计算机科学 启发式 作业车间调度 调度(生产过程) 超启发式 遗传程序设计 数学优化 多目标优化 流水车间调度 人工智能 机器学习 数学 地铁列车时刻表 操作系统 机器人 机器人学习 移动机器人
作者
Fangfang Zhang,Yi Mei,Su Nguyen,Mengjie Zhang
出处
期刊:IEEE transactions on cybernetics [Institute of Electrical and Electronics Engineers]
卷期号:53 (7): 4473-4486 被引量:36
标识
DOI:10.1109/tcyb.2022.3196887
摘要

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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