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.
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Saitoh, F., Utani, A. (2013). Coordinated Rule Acquisition of Decision Making on Supply Chain by Exploitation-Oriented Reinforcement Learning. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_67
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DOI: https://doi.org/10.1007/978-3-642-40728-4_67
Publisher Name: Springer, Berlin, Heidelberg
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