An End-to-End Deep Reinforcement Learning Approach for Job Shop Scheduling

Job shop scheduling problem (JSSP) is a typical scheduling problem in manufacturing. Traditional scheduling methods fail to guarantee both efficiency and quality in complex and changeable production environments. This paper proposes an end-to-end deep reinforcement learning (DRL) method to address the JSSP. In order to improve the quality of solutions, a network model based on transformer and attention mechanism is constructed as the actor to enable a DRL agent to search in its solution space. The Proximal policy optimization (PPO) algorithm is utilized to train the network model to learn optimal scheduling policies. The trained model generates sequential decision actions as the scheduling solution. Numerical experiment results demonstrate the superiority and generality of the proposed method compared with other three classic heuristic rules.