Reinforcement Learning for Inventory Management
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The decision of “how much to order” at each stage of the supply chain is a major task to minimize inventory costs. Managers tend to follow particular ordering policy seeking individual benefit which hampers the overall performance of the supply chain. Major findings from the literature show that, with the advent of machine learning and artificial intelligence, the trend in this area has been heading from simple base stock policy to intelligence-based learning algorithms to gain near-optimal solution. This paper initially focuses on formulating a multi-agent four-stage serial supply chain as reinforcement learning (RL) model for ordering management problem. In the final step, RL model for a single-agent supply chain is optimized using Q-learning algorithm. The results from the simulations show that the RL model with Q-learning algorithm is found to be better than Order-Up-To policy and 1–1 policy.
KeywordsSupply chain Ordering policy Inventory management Reinforcement learning Q-learning
- 3.Claus C, Boutilier C (1998) The dynamics of reinforcement learning in cooperative multiagent systems. In: Proceedings of the fifteenth national conference on artificial intelligence. AAAI, Madison, Wisconsin, pp 746–752Google Scholar
- 4.Forester JW (1961) Industrial dynamics, 1st edn. MIT Press; Wiley, New YorkGoogle Scholar
- 8.Mosekilde E, Larsen ER (1986) Deterministic chaos in the beer production-distribution model. Syst Dyn Rev 4(1–2):131–147Google Scholar
- 15.Oroojlooyjadid A, Nazari M, Snyder L, Takáč M (2017) A deep Q-network for the beer game: a reinforcement learning algorithm to solve inventory optimization problems. arXiv preprint arXiv:1708.05924 [cs. LG]