Bi-Objective Integrated Scheduling of Job Shop Problems and Material Handling Robots with Setup Time

This work investigates the bi-objective integrated scheduling of job shop problems and material handling robots with setup time. The objective is to minimize the maximum completion time and the mean of earliness and tardiness simultaneously. First, a mathematical model is established to describe the problems. Then, different meta-heuristics and their variants are developed to solve the problems, including genetic algorithms, particle swarm optimization, and artificial bee colonies. To improve the performance of algorithms, seven local search operators are proposed. Moreover, two reinforcement learning algorithms, Q-learning and SARSA, are designed to help the algorithm select appropriate local search operators during iterations, further improving the convergence of algorithms. Finally, based on 82 benchmark cases with different scales, the effectiveness of the suggested algorithms is evaluated by comprehensive numerical experiments. The experimental results and discussions show that the genetic algorithm with SARSA is more competitive than its peers.