A Novel Reinforcement Learning Based Optimization Approach for Supply Chain Management

In this research, a method for improving supply chain management transport routing decisions is developed. It made use of reinforcement learning, more especially the Advan-tage Actor-Critic (A2C) model. The A2C approach integrated value-based and policy-based learning to successfully handle the dynamic and unpredictable nature of transport routing difficulties. The A2C model could choose the best route, allocate the load, and schedule by learning from previous experiences. The goal of this research to create a simulation environment that fully accounted for the complexities of transport routing in supply chain networks, taking into account variables like traffic conditions, delivery deadlines, vehicle capacities, and resource availability. In order to increase the efficacy and efficiency of transport routing decisions, the A2C model was trained in this context. To assess the effectiveness of the A2C model in achieving optimal transport routing solutions, a thorough performance study and comparison with baseline techniques were performed. The findings and understandings from this study advanced the subject of supply chain management and gave organizations looking to improve their transport routing plans invaluable advice.