Intelligent Supply Chain Management Using Adaptive Critic Learning

Authors

Stephen Shervais, Eastern Washington University
Thaddeus T. Shannon, Portland State University
George G. Lendaris, Portland State UniversityFollow

Sponsor

This work is supported by the National Science Foundation under Grant ECS-9904378.

Published In

IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans.

Document Type

Citation

Publication Date

3-1-2003

Abstract

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.

Rights

Copyright 2003 IEEE

Locate the Document

https://doi.org/10.1109/TSMCA.2003.809214

DOI

10.1109/TSMCA.2003.809214

Persistent Identifier

https://archives.pdx.edu/ds/psu/37311

Citation Details

Shervais, S., Shannon, T. T., & Lendaris, G. G. (2003). Intelligent supply chain management using adaptive critic learning. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 33(2), 235-244.