Reinforcement Learning‐Assisted Meta‐Heuristics for Scheduling Job Shops With Material Handling Robots

ABSTRACT This study addresses an integrated job shop scheduling problem with material handling robots (JSPMHR), aiming to minimise the maximum completion time (makespan). First, a mathematical model is developed to formulate the JSPMHR. Second, three meta‐heuristics, genetic algorithm (GA), particle swarm optimisation (PSO) and artificial bee colony (ABC), are improved to solve the concerned problems. Based on the problem‐specific features, seven local search strategies are designed to improve the convergence speed. Third, two reinforcement learning algorithms, that is, Q‐learning and Sarsa, are employed to assist meta‐heuristics in selecting the premium local search strategies during iterations. Finally, comprehensive experiments are conducted to evaluate the performance of the proposed algorithms by solving 82 benchmark instances. The proposed GA with Q‐learning shows the strongest competitiveness among all compared algorithms solving JSPMHR.