A Multi-objective Reinforcement Learning Algorithm for JSSP

Scheduling is a decision making process that takes care of the allocation of resources to tasks over time. The Job Shop scheduling problem is one of the most complex scheduling scenarios and is commonly encountered in manufacturing industries. Most of the existing studies are based on optimizing one objective, but in real-world problems, multiple criteria often need to be optimized at once. We propose a Multi-Objective Multi-Agent Reinforcement Learning Algorithm that aims to obtain the non-dominated solutions set for Job Shop scheduling problems. The proposed algorithm is used to solve a set of benchmark problems optimizing makespan and tardiness. The performance of our algorithm is evaluated and compared to other algorithms from the literature using two measures for evaluating the Pareto front. We show that our algorithm is able to find a set of diverse and high quality non-dominated solutions, that significantly and consistently improves upon the results obtained by other state-of-the-art algorithms.