Genetic Programming with Lexicase Selection for Large-scale Dynamic Flexible Job Shop Scheduling

计算机科学 遗传程序设计 调度(生产过程) 动态优先级调度 数学优化 作业车间调度 人口 启发式 人工智能 数学 地铁列车时刻表 人口学 社会学 操作系统
作者
Meng Xu,Yi Mei,Fangfang Zhang,Mengjie Zhang
出处
期刊:IEEE Transactions on Evolutionary Computation [Institute of Electrical and Electronics Engineers]
卷期号:28 (5): 1235-1249 被引量:12
标识
DOI:10.1109/tevc.2023.3244607
摘要

Dynamic flexible job shop scheduling is a prominent combinatorial optimisation problem with many real-world applications. Genetic programming has been widely used to automatically evolve effective scheduling heuristics for dynamic flexible job shop scheduling. A limitation of genetic programming is the premature convergence due to the loss of population diversity. To overcome this limitation, this work considers using lexicase selection to improve population diversity, which has achieved success on regression and program synthesis problems. However, it is not trivial to apply lexicase selection to genetic programming for dynamic flexible job shop scheduling, since a fitness case (training scheduling simulation) is often large-scale, making the fitness evaluation very time-consuming. To address this issue, we propose a new multi-case fitness scheme, which creates multiple cases from a single scheduling simulation. Based on the multi-case fitness, we develop a new genetic programming algorithm with lexicase selection, which uses a single simulation for fitness evaluation, thus achieving a better balance between the number of cases for lexicase selection and evaluation efficiency. The experiments on a wide range of dynamic scheduling scenarios show that the proposed algorithm can achieve better population diversity and final performance than the current genetic programming parent selection methods and a state-of-the-art deep reinforcement learning method.
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