Learning to Detect Planning Horizons with Multi-Layered Perceptrons: A Case Studyfor Lot-Sizing

摘要

The authors present a neural network approach to the problem of determining lotsizes under demand uncertainty. The situation is considered in which at any decision moment the demand is given for a small finite data horizon into the future and the lot sizes are determined on a rolling-horizon basis. The authors demonstrate how a properly designed multi-layered perceptron can successfully be learned to detect planning horizons in case of a simple lot-sizing problem with Wagner-Whitin cost structure. The authors develop a two-stage decision procedure in which in the first stage the multi-layered perceptron estimates a planning horizon within the data horizon. In the second stage, a detailed plan for this estimated planning horizon is calculated. The authors compare the cost performance of this procedure with some of the well-known lot-sizing heuristics for a number of different cost and demand conditions.