Flexible job shop machines and AGVs cooperative scheduling on the basis of DQN algorithm

In flexible job shop, the transportation time and path of Automated Guided Vehicles (AGVs) greatly impact the production cycle time. However, traditional flexible job shop scheduling problems (FJSSP) mainly focus on job sequencing and machine selection, neglecting the effect of AGVs transportation on the production process. This often leads to suboptimal scheduling solutions that are difficult to guide the shop floor operation effectively. To address this issue, this paper formulates a cooperative scheduling model that integrates the AGVs scheduling into traditional flexible job shop scheduling problem, abbreviated as the FJSMAS problem, with the objective of makespan minimization. Furthermore, by designing state features, action space, and reward function, the FJSMAS problem is transformed into a Markov decision process, a deep reinforcement learning algorithm (DRL) is then adopted to solve it. The feasibility of the proposed FJSMAS model and the effectiveness of DRL algorithm in solving the FJSMAS problem are verified through benchmark’s instances.