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A Neuroevolutionary Approach to Stochastic Inventory Control in Multi-Echelon Systems

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    Rights statement: This is an Author's pre-print version of an article whose final and definitive form, the Version of Record, has been published in the International Journal of Production Research,Volume 50, Issue 8, 2012. ©Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/00207543.2011.574503

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Original languageEnglish
Pages (from-to)2150-2160
Number of pages11
JournalInternational Journal of Production Research
Issue number8
Early online date26 Aug 2011
Publication statusPublished - 2012


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.

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