An efficient and adaptive design of reinforcement learning environment to solve job shop scheduling problem with soft actor-critic algorithm

Abstract

Shop scheduling is deeply involved in manufacturing. In order to improve the efficiency of scheduling and fit dynamic scenarios, many Deep Reinforcement Learning (DRL) methods are studied to solve scheduling problems like job shop and flow shop. But most studies focus on using the latest algorithms while ignoring that the environment plays an important role in agent learning. In this paper, we design an effective, robust and size-agnostic environment for job shop scheduling. The proposed design of environment uses centralised training and decentralised execution (CTDE) to implement a multi-agent architecture. Together with the observation space we design, environmental information that is irrelevant to the current decision is eliminated as much as possible. The proposed action space enlarges the decision space of agents, which performs better than the traditional way. Finally, Soft Actor-Critic (SAC) algorithm is adapted to learning within this environment. By comparing with traditional scheduling rules, other reinforcement learning algorithms, and relevant literature, the superiority of the results obtained in this study is demonstrated.