Collaborative reconfiguration of supply networks based on GNN and ALC

Abstract

With the prevalence of lean and just-in-time principles, traditional supply chains often exhibit inflexibility, leading to challenges in satisfying extensive customized orders and managing risks during disruptions. Thus, there is a need for a more flexible, resilient, and collaborative network and strategies to tackle the aforementioned challenges. In this study, we introduce a new supply network called the industry supply chain, aimed at enabling collaborative decision-making and dynamic reconfiguration. We create a graph neural network model to promptly identify sudden disturbances and devise a distributed multidisciplinary optimization model to facilitate collaborative reconfiguration. The experimental findings from an air-conditioning industry supply chain show that network reconfiguration under real-time disturbance detection reduces losses and improves operational stability.