Heterogeneous Graph Neural-Network-Based Scheduling Optimization for Multi-Product and Variable-Batch Production in Flexible Job Shops

In view of the Flexible Job-shop Scheduling Problem (FJSP) under multi-product and variable-batch production modes, this paper presents an intelligent scheduling approach based on a heterogeneity-enhanced graph neural network combined with deep reinforcement learning. By constructing a heterogeneity-enhanced incidence graph to dynamically represent the scheduling state, the proposed method effectively captures both the dependencies among operations and the interaction features between operations and machines. Moreover, the Proximal Policy Optimization (PPO) algorithm is leveraged to achieve end-to-end optimization of scheduling decisions. Specifically, the FJSP is formulated as a Markov Decision Process. A heterogeneous enhanced graph neural network architecture is designed to extract deep features from operation nodes, machine nodes, and their heterogeneous relationships. Then, a policy network generates joint actions for operation assignment and machine selection, while the PPO algorithm iteratively refines the scheduling policy. Finally, the method is validated in an aerospace component machining workshop scenario and the benchmark dataset. Experimental results demonstrate that, compared with traditional dispatching rules and existing deep reinforcement learning techniques, the proposed approach not only achieves superior scheduling performance but also maintains an excellent balance between response efficiency and scheduling quality.