Neuroevolutionary Inventory Control in Multi-Echelon Systems

  • Steve D. Prestwich
  • S. Armagan Tarim
  • Roberto Rossi
  • Brahim Hnich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5783)


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.


Hide Unit Penalty Cost Inventory Control Scenario Tree Optimal Plan 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Steve D. Prestwich
    • 1
  • S. Armagan Tarim
    • 2
  • Roberto Rossi
    • 3
  • Brahim Hnich
    • 4
  1. 1.Cork Constraint Computation CentreIreland
  2. 2.Operations Management DivisionNottingham University Business SchoolNottinghamUK
  3. 3.Logistics, Decision and Information Sciences GroupWageningen URThe Netherlands
  4. 4.Faculty of Computer ScienceIzmir University of EconomicsTurkey

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