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A neural network model for solving the lot-sizing problem

Author

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  • Gaafar, Lotfi K.
  • Choueiki, M. Hisham
Abstract
Artificial neural network models have been used successfully to solve demand forecasting and production scheduling problems; the two steps that typically precede and succeed Material Requirements Planning (MRP). In this paper, a neural network model is applied to the MRP problem of lot-sizing. The model's performance is evaluated under different scenarios and is compared to common heuristics that address the same problem. Results show that the developed artificial neural network model is capable of solving the lot-sizing problem with notable consistency and reasonable accuracy.

Suggested Citation

  • Gaafar, Lotfi K. & Choueiki, M. Hisham, 2000. "A neural network model for solving the lot-sizing problem," Omega, Elsevier, vol. 28(2), pages 175-184, April.
    • Handle: RePEc:eee:jomega:v:28:y:2000:i:2:p:175-184
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    References listed on IDEAS

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    1. Robert C. Carlson & James V. Jucker & Dean H. Kropp, 1979. "Less Nervous MRP Systems: A Dynamic Economic Lot-Sizing Approach," Management Science, INFORMS, vol. 25(8), pages 754-761, August.
    2. Alok Aggarwal & James K. Park, 1993. "Improved Algorithms for Economic Lot Size Problems," Operations Research, INFORMS, vol. 41(3), pages 549-571, June.
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    Cited by:

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    3. Jans, Raf & Degraeve, Zeger, 2007. "Meta-heuristics for dynamic lot sizing: A review and comparison of solution approaches," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1855-1875, March.

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