Heterogeneous Graph Neural Network with Dual-View Fusion for Machine-Robot Collaborative Scheduling

In recent years, deep reinforcement learning (DRL), by integrating neural network technologies, has made significant progress in the field of production scheduling, represents a promising approach. However, current DRL-based solutions lack sufficient analysis of the heterogeneity among production factors in scheduling problems, resulting in suboptimal representations of scheduling states received by DRL algorithms during training. To address this issue, this paper proposes a heterogeneous graph neural network with dual-view fusion for the machine-robot collaborative scheduling problem. It effectively resolves the heterogeneity among the three production factors—operations, machines, and logistics robots—through three neural network modules, thereby providing high-quality representations of scheduling states for DRL algorithms. Experimental results demonstrate that the proposed method outperforms a representative metaheuristic algorithm (genetic algorithm), a priority dispatching rule (first in first out), and existing DRL-based methods, both in small-scale problems and in generalization to large-scale scenarios.