TY - BOOK
T1 - Neural networks and production planning
AU - Zwietering, P.J.
AU - Kraaij, van, M.J.A.L.
AU - Aarts, E.H.L.
AU - Wessels, J.
PY - 1991
Y1 - 1991
N2 - Because of the combination of classification, association, adaptation, and pattern recognition capabilities, neural networks are shown to be suitable for solving problems in production planning with uncertain and non-stationary demand. We demonstrate that a properly designed and trained multi-layered perceptron outperforms traditional algorithms for the rolling horizon version of the dynamic lotsizing problem. Formal arguments are supported by numerical experiments.
Keywords: Lotsizing, Multi-Layered Perceptrons, Neural Networks, Pattern Recognition,
Production Planning, Uncertainty.
AB - Because of the combination of classification, association, adaptation, and pattern recognition capabilities, neural networks are shown to be suitable for solving problems in production planning with uncertain and non-stationary demand. We demonstrate that a properly designed and trained multi-layered perceptron outperforms traditional algorithms for the rolling horizon version of the dynamic lotsizing problem. Formal arguments are supported by numerical experiments.
Keywords: Lotsizing, Multi-Layered Perceptrons, Neural Networks, Pattern Recognition,
Production Planning, Uncertainty.
M3 - Report
T3 - Memorandum COSOR
BT - Neural networks and production planning
PB - Technische Universiteit Eindhoven
CY - Eindhoven
ER -