An EOQ model with fuzzy demand and learning in fuzziness

Christoph H. Glock, Kurt Schwindl and Mohamad Y. Jaber

International Journal of Services and Operations Management, 2012, vol. 12, issue 1, 90-100

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.

Keywords: EOQ model; inventory control; fuzzy demand; learning; uncertainty reduction; economic order quantity. (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijsoma:v:12:y:2012:i:1:p:90-100

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