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.
|Title of host publication||Algorithmic Decision Theory|
|Subtitle of host publication||First International Conference, ADT 2009, Venice, Italy, October 20-23, 2009. Proceedings|
|Editors||Francesca Rossi, Alexis Tsoukias|
|Number of pages||12|
|Publication status||Published - 13 Oct 2009|
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publisher||Springer Berlin / Heidelberg|