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 …