This study investigates the effect of learning in fuzziness by considering fuzzy demand in theEOQ model for deteriorating items under a finite time horizon.The crisp equivalent form of the fuzzy objective function is ...This study investigates the effect of learning in fuzziness by considering fuzzy demand in theEOQ model for deteriorating items under a finite time horizon.The crisp equivalent form of the fuzzy objective function is obtained by employing the centroid method.Using calculus,the number of replenishments which optimizes the fuzzy objective function is derived.The model is extended by applying learning in fuzziness and an algorithm is developed to determine the number of replenishments.Numerical illustrations are provided for the model under a crisp,fuzzy and fuzzylearning environment.Numerical results reveal that the cost is lower with learning in fuzziness than that of without learning in fuzziness.Besides,results indicate that the learning in fuzziness is more effective whenever the parameter has higher impreciseness in the estimation of its value.展开更多
文摘This study investigates the effect of learning in fuzziness by considering fuzzy demand in theEOQ model for deteriorating items under a finite time horizon.The crisp equivalent form of the fuzzy objective function is obtained by employing the centroid method.Using calculus,the number of replenishments which optimizes the fuzzy objective function is derived.The model is extended by applying learning in fuzziness and an algorithm is developed to determine the number of replenishments.Numerical illustrations are provided for the model under a crisp,fuzzy and fuzzylearning environment.Numerical results reveal that the cost is lower with learning in fuzziness than that of without learning in fuzziness.Besides,results indicate that the learning in fuzziness is more effective whenever the parameter has higher impreciseness in the estimation of its value.
