Neuroevolutionary inventory control in multi-echelon systems

S.D. Prestwich, S.A. Tarim, R. Rossi, B. Hnich

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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 large 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 a neuroevolutionary approach: using an artificial neural network to approximate the scenario tree, and training the network by a simulation-based evolutionary algorithm. We show experimentally that this method can quickly find good plans.
Original languageEnglish
Title of host publicationAlgorithmic Decision Theory
Subtitle of host publicationFirst International Conference, ADT 2009, Venice, Italy, October 20-23, 2009. Proceedings
EditorsFrancesca Rossi, Alexis Tsoukias
PublisherSpringer-Verlag GmbH
Pages402-413
Number of pages12
Volume5783 LNAI
ISBN (Print)978-3-642-04427-4
DOIs
Publication statusPublished - 13 Oct 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Berlin / Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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