Dynamic Scheduling for Flexible Job Shop with Insufficient Transportation Resources Via Graph Neural Network and Deep Reinforcement Learning

The smart workshop is a powerful tool for manufacturing companies to reduce waste and improve production efficiency through real-time data analysis for self-organized production. Automated Guided Vehicles (AGVs) have been widely used for material handling in smart workshop due to their high degree of autonomy, flexibility and powerful end-to-end capability to cope with logistics tasks in production modes such as multiple species and small batch, and mass customization. However, the highly dynamic, complex and uncertain nature of the smart job shop environment makes production scheduling with limited transportation resources in mind a challenge. To this end, this paper addresses the dynamic flexible job shop scheduling problem with insufficient transportation resources (DFJSP-ITR), and learn high-quality priority dispatching rule (PDR) end-to-end to minimize makespan by the proposed deep reinforcement learning (DRL) method. To achieve integrated decision making for operation, machine and AGV, an architecture based on heterogeneous graph neural network and deep reinforcement learning is proposed. Considering the impact of different AGV distribution methods on the scheduling objective, this paper compares two different AGV distribution methods. Experiments show that the proposed method has superiority and good generalization ability compared with the current PDRs-based methods regardless of the AGV distribution strategy used