Heterogeneous Graph Attention Networks for Scheduling in Cloud Manufacturing and Logistics

Efficient task scheduling and resource allocation in manufacturing are vital for gaining competitive advantages in dynamic economic environments. Modern manufacturing systems must integrate logistics considerations such as delivery times and costs, yet traditional scheduling methods often overlook these factors. To address this gap, we investigate task scheduling in cloud manufacturing systems, emphasizing logistics integration. We propose a novel Graph Neural Network architecture for optimizing task scheduling by representing the problem on a heterogeneous graph, where nodes denote tasks and locations. Our model minimizes both manufacturing and logistics costs, achieving significant performance improvements over greedy algorithms and comparable results to strong genetic algorithms in large-scale scenarios with up to 20 locations. This work advances the efficiency and flexibility of cloud manufacturing systems, offering practical solutions for dynamic, cost-sensitive environments.