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Neuroevolutionary Inventory Control in Multi-Echelon Systems

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Algorithmic Decision Theory (ADT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5783))

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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.

B. Hnich is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No. SOBAG-108K027. This material is based in part upon works supported by the Science Foundation Ireland under Grant No. 05/IN/I886.

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Author information

Authors and Affiliations

  1. Cork Constraint Computation Centre, Ireland

    Steve D. Prestwich

  2. Operations Management Division, Nottingham University Business School, Nottingham, UK

    S. Armagan Tarim

  3. Logistics, Decision and Information Sciences Group, Wageningen UR, The Netherlands

    Roberto Rossi

  4. Faculty of Computer Science, Izmir University of Economics, Turkey

    Brahim Hnich

Authors
  1. Steve D. Prestwich
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  2. S. Armagan Tarim
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  3. Roberto Rossi
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  4. Brahim Hnich
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Editor information

Editors and Affiliations

  1. Department of Pure and Applied Mathematics, University of Padova, Via Trieste 63, 35121, Padova, Italy

    Francesca Rossi

  2. LAMSADE-CNRS, University of Paris Dauphine, Place du Maréchal De Lattre de Tassigny, 75775, Paris Cedex 16, France

    Alexis Tsoukias

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Prestwich, S.D., Tarim, S.A., Rossi, R., Hnich, B. (2009). Neuroevolutionary Inventory Control in Multi-Echelon Systems. In: Rossi, F., Tsoukias, A. (eds) Algorithmic Decision Theory. ADT 2009. Lecture Notes in Computer Science(), vol 5783. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04428-1_35

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  • DOI: https://doi.org/10.1007/978-3-642-04428-1_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04427-4

  • Online ISBN: 978-3-642-04428-1

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