Integrated Dynamic Flexible Job Shop and AIV Scheduling: Deep Reinforcement Learning Approach Considering AIV Charging and Capacity Constraints

Scheduling Automated Intelligent Vehicles (AIV) in Dynamic Flexible Job Shops is a challenging problem due to its high level of stochasticity and dynamic nature. Various heuristic and exact methods have proven effective when the complexity of the problem is relatively low. Moreover, in the past decade, machine learning algorithms, particularly reinforcement learning, have been applied to sophisticated scheduling tasks and demonstrated their efficiency in solving such problems. In this study, a deep reinforcement learning approach is proposed to address the integrated Dynamic Flexible Job Shop and AIV scheduling. Aimed at optimizing two objectives: total lateness of jobs and total energy consumption of AIVs. The study takes into account the limitations of AIV transporters, such as charging consumption and loading capacity. To validate the proposed method, a case study of a flexible job shop is designed, and our approach is compared with a combination of existing heuristics.