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Intelligent supply chain management using adaptive critic learning
2003
IEEE transactions on systems, man and cybernetics. Part A. Systems and humans
A set of neural networks is employed to develop control policies that are better than fixed, theoretically optimal policies, when applied to a combined physical inventory and distribution system in a nonstationary demand environment. Specifically, we show that model-based adaptive critic approximate dynamic programming techniques can be used with systems characterized by discrete valued states and controls. The control policies embodied by the trained neural networks outperformed the best,
doi:10.1109/tsmca.2003.809214
fatcat:iozw6g76gbbxxbhoojx5663qt4