Reinforcement learning assisted meta-heuristics for scheduling distributed reentrant flowshops with sequence-dependent setup times

Reentrant is widespread in many manufacturing scenarios even if it is a few concerns in literature. This study explores a distributed reentrant flow shop scheduling problem with sequence-dependent setup times (DRFSP-SDST). The goal is to minimize the maximum factory completion time (Makespan). Initially, the mathematical model of DRFSP-SDST is formulated by considering the sequence-dependent setup times. Second, four meta-heuristics, including iterated greedy (IG), artificial bee colony (ABC), Jaya, and particle swarm optimization (PSO) algorithm, are used and their variants are proposed for solving the concerned problems. Third, to enhance the performance of the algorithms, five local search operators are designed based on the nature of the problems. Then, two algorithms for reinforcement learning, Q-learning and state-action-reward-state-action (Sarsa), are integrated into the iterative process to select high-quality local search strategies. Finally, the effectiveness of the proposed improvement strategies is evaluated through comprehensive numerical experiments on 90 instances. The performance of the proposed algorithms is further verified through the Freidman test. The ABC algorithm with Sarsa-based local search exhibits the highest competitiveness for solving the DRFSP-SDST, according to the experimental findings and debates.