Inventory management in supply chains: A reinforcement learning approach

Inventory management in supply chains: A reinforcement learning approach

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Article ID: iaor200355
Country: Netherlands
Volume: 78
Issue: 2
Start Page Number: 153
End Page Number: 161
Publication Date: Jan 2002
Journal: International Journal of Production Economics
Authors: ,
Keywords: markov processes
Abstract:

A major issue in supply chain inventory management is the coordination of inventory policies adopted by different supply chain actors, such as suppliers, manufacturers, distributors, so as to smooth material flow and minimize costs while responsively meeting customer demand. This paper presents an approach to manage inventory decisions at all stages of the supply chain in an integrated manner. It allows an inventory order policy to be determined, which is aimed at optimizing the performance of the whole supply chain. The approach consists of three techniques: (i) Markov decision processes (MDP) and (ii) an artificial intelligent algorithm to solve MDPs, which is based on (iii) simulation modeling. In particular, the inventory problem is modeled as an MDP and a reinforcement learning (RL) algorithm is used to determine a near optimal inventory policy under an average reward criterion. RL is a simulation-based stochastic technique that proves very efficient particularly when the MDP size is large.

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