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An EOQ model with fuzzy demand and learning in fuzziness

Author

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  • Christoph H. Glock
  • Kurt Schwindl
  • Mohamad Y. Jaber

Abstract

This paper develops an economic order quantity (EOQ) model with fuzzy demand that may vary between upper and lower limits. The imprecision in demand is assumed to reduce with time because of learning. The results from the developed model are compared to those of an EOQ model with fuzzy demand and no learning. It is shown that learning in fuzziness improves the information base for future orders by reducing uncertainty, which favours delivering demand in smaller lots which are delivered more frequently. As the learning rate increases and fuzziness in demand reduces, the results were shown to converge to those of the classical EOQ model.

Suggested Citation

  • Christoph H. Glock & Kurt Schwindl & Mohamad Y. Jaber, 2012. "An EOQ model with fuzzy demand and learning in fuzziness," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 12(1), pages 90-100.
    • Handle: RePEc:ids:ijsoma:v:12:y:2012:i:1:p:90-100
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    Cited by:

    1. Nima Kazemi & Salwa Hanim Abdul-Rashid & Ehsan Shekarian & Eleonora Bottani & Roberto Montanari, 2016. "A fuzzy lot-sizing problem with two-stage composite human learning," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 5010-5025, August.

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