A reinforcement learning model for supply chain ordering management: An application to the beer game

A major challenge in supply chain ordering management is the coordination of ordering policies adopted by each level of the chain, so as to minimize inventory costs. This paper describes a new approach to decide on ordering policies of supply chain members in an integrated manner. In the first step supply chain ordering management has been considered as a multi-agent system and formulated as a reinforcement learning (RL) model. In the final step a Q-learning algorithm is proposed to solve the RL model. Results show that the reinforcement learning ordering mechanism (RLOM) is better than two other known algorithms.