Coordinated rule acquisition of decision making on supply chain by exploitation-oriented reinforcement learning -beer game as an example-

Abstract

Product order decision-making is an important feature of inventory control in supply chains. The beer game represents a typical 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 environment is difficult for these approaches because their learning algorithm assume a static environment. As exploitation-oriented reinforcement 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 comparing performances. © 2013 Springer-Verlag Berlin Heidelberg.