Abstract
One of the challenging issues in supply chain management is the coordination of ordering processes, especially in dynamic situations. In recent years, reinforcement learning algorithms are considered to be efficient techniques for solving such problems. In this paper, an agent-based simulation technique has been integrated with a reinforcement learning algorithm and has been applied to model a four-echelon supply chain that faces non-stationary customer demands. This approach leads to the development of a novel and intelligent simulation-based optimization framework, which includes a detailed simulation modeling of supply chain behavior. Finally statistical methods, including the Var technique, are used for the risk evaluation and sensitivity analysis have been provided to support the decision making process under uncertainty.
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