A deep reinforcement learning method for job shop scheduling integrated with automated guided vehicles

With the emergence of the smart factory concept, Automated Guided Vehicles have been widely utilized in job shop transportation systems, drawing increased attention to the job shop scheduling problem integrated with multiple AGVs. In this problem, the sequential-logic constraint between transportation and processing tasks in each operation complicates the problem's decoupling compared to traditional job shop scheduling problems. In this paper, a deep reinforcement learning-based method is proposed to address this integrated scheduling problem. The problem is initially formulated as a Markov Decision Process, with state features designed from the perspectives of jobs and AGVs. Furthermore, a novel reward function is designed to maximize job shop resource utilization. The primary objective of the scheduling is to obtain an optimal solution that maximizes resource utilization, thereby enhancing the core competitiveness of the factory. Benchmark experiments demonstrate the effectiveness and competitiveness of this method in obtaining solutions for the integrated problems.