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An EOQ model with fuzzy demand and learning in fuzziness

机译：具有模糊需求和模糊性学习的EOQ模型

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摘要

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.
机译：本文开发了一种模糊需求的经济订单量（EOQ）模型，该需求可能在上限和下限之间变化。需求的不精确性被认为是由于学习而随时间减少的。将开发模型的结果与具有模糊需求且没有学习的EOQ模型的结果进行比较。结果表明，模糊学习可以通过减少不确定性来改善未来订单的信息基础，这有利于以较小的批次交付需求，而这些批次的交付频率更高。随着学习率的提高和需求模糊性的降低，结果表明收敛于经典EOQ模型的结果。

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