Coordinated Rule Acquisition of Decision Making on Supply Chain by Exploitation-Oriented Reinforcement Learning -Beer Game as an Example-
Fumiaki Saıtoh, Akihide Utani · International Conference on Artificial Neural Networks · 2013
Product order decision-making is an important feature of inventory control in supply chains. The beer game represents a typi- cal task in this process. Recent approaches that have applied the agent model to the beer game have shown. Q-learning performing better than genetic algorithm (GA). However, flexibly adapting to dynamic envi- ronment is difficult for these approaches because their learning algo- rithm assume a static environment. As exploitation-oriented reinforce- ment learning algorithm are robust in dynamic environments, this study, approaches the beer game using profit sharing, a typical exploitation- oriented agent learning algorithm, and verifies its result's validity by com- paring performances.