Genetic Programming With Lexicase Selection for Large-Scale Dynamic Flexible Job Shop Scheduling

Meng Xu, Yi Mei, Fangfang Zhang, Mengjie Zhang

Abstract: Dynamic flexible job shop scheduling (JSS) is a prominent combinatorial optimization problem with many real-world applications. Genetic programming (GP) has been widely used to automatically evolve effective scheduling heuristics for dynamic flexible JSS (DFJSS). A limitation of GP is the premature convergence due to the loss of population diversity. To overcome this limitation, this work considers using lexicase selection (LS) to improve population diversity, which has achieved success on regression and program synthesis problems. However, it is not trivial to apply LS to GP for DFJSS, 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 multicase fitness scheme, which creates multiple cases from a single scheduling simulation. Based on the multicase fitness, we develop a new GP algorithm with LS, which uses a single simulation for fitness evaluation, thus, achieving a better balance between the number of cases for LS 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 GP parent selection methods and a state-of-the-art deep reinforcement learning method.