A neuroevolutionary approach to stochastic inventory control in multi-echelon systems

Abstract

Stochastic inventory control in multi-echelon systems poses hard problems in optimisation under uncertainty. Stochastic programming can solve small instances optimally, and approximately solve larger instances via scenario reduction techniques, but it cannot handle arbitrary nonlinear constraints or other non-standard features. Simulation optimisation is an alternative approach that has recently been applied to such problems, using policies that require only a few decision variables to be determined. However, to find optimal or near-optimal solutions we must consider exponentially large scenario trees with a corresponding number of decision variables. We propose instead a neuroevolutionary approach: using an artificial neural network to compactly represent the scenario tree, and training the network by a simulation-based evolutionary algorithm. We show experimentally that this method can quickly find high-quality plans using networks of a very simple form.

Keywords:

• inventory control
• neural networks
• evolutionary algorithms
• neuroevolution
• multi-echelon systems

Acknowledgements

This material is based, in part, upon works supported by the Science Foundation Ireland under grant No. 05/IN/I886. B. Hnich and S.A. Tarim are supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under the Support Programme-1001. Roberto Rossi has received funding from the European Community's Seventh Framework Programme (FP7) under grant agreement No. 244994 (project VEG-i-TRADE).