Evaluating supply partner's capability for seasonal products using machine learning techniques
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
Adaptive dynamic programming: an introduction
IEEE Computational Intelligence Magazine
Approximate dynamic programming for an inventory problem: Empirical comparison
Computers and Industrial Engineering
Multilayer perceptron for simulation models reduction: Application to a sawmill workshop
Engineering Applications of Artificial Intelligence
Computers and Industrial Engineering
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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, fixed policies (found by either linear programming or genetic algorithms) in a high-penalty cost environment with time-varying demand.