TY - CHAP
T1 - Neuroevolutionary inventory control in multi-echelon systems
AU - Prestwich, Steve D.
AU - Tarim, S. Armagan
AU - Rossi, Roberto
AU - Hnich, Brahim
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/71549146592
U2 - 10.1007/978-3-642-04428-1_35
DO - 10.1007/978-3-642-04428-1_35
M3 - Chapter
AN - SCOPUS:71549146592
SN - 3642044271
SN - 9783642044274
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 402
EP - 413
BT - Algorithmic Decision Theory - First International Conference, ADT 2009, Proceedings
T2 - 1st International Conference on Algorithmic Decision Theory, ADT 2009
Y2 - 20 October 2009 through 23 October 2009
ER -