Deep Reinforcement Learning Model Adjusted Information Sharing in Supply Chain Management

Abstract

Supply chain ordering management (SCOM) attracts attention due to structural changes in supply chain (SC). SCOM can be modeled by reinforcement learning, in which the SC is regarded as an environment and the companies belonging to the SC as agents. Most previous studies premise that each agent's information is shared to all agents in the SC. But actually it is difficult for companies to disclose their own information to other companies without hiding it, and companies can only communicate with each other based on partial information. Therefore, a learning model that appropriately sets the range of information that each agent shares is necessary. This study focused on linear multi-stage SC and proposed a deep reinforcement learning model that determines an ordering policy that maximally reduces inventory costs while restricting the range of information shared among agents. The experimental results demonstrated that the proposed model can achieve the better inventory cost than the previous study's one.