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. (C) 2008 Elsevier B.V. All rights reserved.