Think4SCND: Reinforcement Learning With Thinking Model for Dynamic Supply Chain Network Design

Abstract

Supply chain network design is a critical strategic challenge that significantly influences operational efficiency and competitiveness in the global marketplace. This paper introduces Think4SCND, a novel deep reinforcement learning framework that addresses the dynamic complexities of supply chain network design by integrating a Supply Chain Transformer Network with a Thinking Model. Our approach formulates the supply chain network design problem as a Markov Decision Process, developing an architecture capable of handling mixed discrete-continuous action spaces. The core innovation lies in the Thinking Model, which enhances the Supply Chain Transformer Network’s ability to reason about future states and evaluate decision sequences, enabling more informed and forward-looking decision-making. We propose an end-to-end training algorithm that effectively combines model-free reinforcement learning with model-based planning. Extensive experiments on both synthetic and real-world datasets show that Think4SCND significantly outperforms state-of-the-art baselines, achieving near-optimal solutions with a fraction of the computational cost. The framework demonstrates superior adaptability to disruptions and strong generalization capabilities, successfully transferring knowledge from medium-sized problems to larger, unseen instances.

Keywords

• Supply chain network design
• inventory management
• reinforcement learning
• thinking model
• transformer network