A reinforcement learning hyper-heuristic algorithm for the distributed flowshops scheduling problem under consideration of emergency order insertion

Abstract

Large enterprises are composed of several subproduction centers. The production plan is changed based on the procedure of the manufacturing system. The distributed flowshop scheduling problem under consideration of emergence order insertion is challenging as the assignment of the product, and the scheduling of the process are coupled with each other. A general distributed flow shop scheduling problem regarding emergency order insertion (DFSP_EOI) is addressed under rescheduling circumstances in this paper. The Q-learning hyper-heuristic algorithm with dynamic insertion rule (QLHH_DIR) is proposed to solve the DFSP_EOI. Eight low-level heuristics (LLHs) for static job assignment. A dynamic insertion rule based on the state of each production center is designed for emergency order insertion. The Q-learning mechanism at high-level space selects appropriate LLH through learning the experience from the optimization process. The computational simulation is carried out, and the results confirm that the proposed algorithm is superior to the competitors in solving the distributed flow shop rescheduling problem. The results of the 720 problem instances show that the proposed algorithm is highly efficient in rescheduling problems.