An improved deep reinforcement learning approach for the dynamic job shop scheduling problem with random job arrivals. Issue 1 (April 2021)

• Record Type: Journal Article
• Authors: Luo, Bin; Wang, Sibao; Yang, Bo; Yi, Lili
• Is Part Of: Journal of physics. Volume 1848:Issue 1(2021)
• Publication Date: 2021-04
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1848/1/012029
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Deep reinforcement learning (DRL) method is a powerful way to solve the dynamic job shop scheduling problems (DJSSP). However, these DRL approaches are dispatching rules-based, meaning they are problem-specific, dependent on experience, and code effort. We propose a double loop deep Q-network (DLDQN) method with exploration loop and exploitation loop to solve DJSSP under random job arrivals, aiming to minimize the makespans of DJSSP. Simultaneously, by integrating into a single agent scheduling system, the proposed method could avoid complicated dispatching rules, enhancing the proposed method's versatility. The experiment results have confirmed the superiority of our method compared to other algorithms.