Neural networks and production planning

P.J. Zwietering, M.J.A.L. Kraaij van, E.H.L. Aarts, J. Wessels

Research output: Book/ReportBookScientific

Abstract

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.
Original languageEnglish
Place of PublicationEindhoven
PublisherTechnische Universiteit Eindhoven
Publication statusPublished - 1991
Externally publishedYes

Publication series

NameMemorandum COSOR

Fingerprint

Neural networks
Planning
Pattern recognition
Experiments
Uncertainty

Cite this

Zwietering, P. J., Kraaij van, M. J. A. L., Aarts, E. H. L., & Wessels, J. (1991). Neural networks and production planning. (Memorandum COSOR). Eindhoven: Technische Universiteit Eindhoven.
Zwietering, P.J. ; Kraaij van, M.J.A.L. ; Aarts, E.H.L. ; Wessels, J. / Neural networks and production planning. Eindhoven : Technische Universiteit Eindhoven, 1991. (Memorandum COSOR).
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Zwietering, PJ, Kraaij van, MJAL, Aarts, EHL & Wessels, J 1991, Neural networks and production planning. Memorandum COSOR, Technische Universiteit Eindhoven, Eindhoven.

Neural networks and production planning. / Zwietering, P.J.; Kraaij van, M.J.A.L.; Aarts, E.H.L.; Wessels, J.

Eindhoven : Technische Universiteit Eindhoven, 1991. (Memorandum COSOR).

Research output: Book/ReportBookScientific

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AU - Aarts, E.H.L.

AU - Wessels, J.

PY - 1991

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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 - Book

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BT - Neural networks and production planning

PB - Technische Universiteit Eindhoven

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ER -

Zwietering PJ, Kraaij van MJAL, Aarts EHL, Wessels J. Neural networks and production planning. Eindhoven: Technische Universiteit Eindhoven, 1991. (Memorandum COSOR).