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
This study investigates an inventory model for a non-instantaneous deteriorating item with partial backlogging wherein the demand is stochastic in nature and depends on price and promotional effort whereas the deterio...This study investigates an inventory model for a non-instantaneous deteriorating item with partial backlogging wherein the demand is stochastic in nature and depends on price and promotional effort whereas the deterioration rate is time proportional.Under these settings,a mathematical model is developed with the objective to maximise the expected profit per unit time by determining the optimal price and the length of replenishment cycle.Some useful theoretical results are established to deduce the optimal replenishment schedule.An effective algorithmic procedure is developed to find the optimal solutions to the proposed model.The applicability of the proposed model is illustrated by numerical example.Sensitivity analysis of the optimal solution with respect to key parameters has been carried out and the implications are discussed.The study indicates that though promotional effort stimulates the market demand,it is beneficial in an economic sense when it applies in a conservative manner.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.
文摘This study investigates an inventory model for a non-instantaneous deteriorating item with partial backlogging wherein the demand is stochastic in nature and depends on price and promotional effort whereas the deterioration rate is time proportional.Under these settings,a mathematical model is developed with the objective to maximise the expected profit per unit time by determining the optimal price and the length of replenishment cycle.Some useful theoretical results are established to deduce the optimal replenishment schedule.An effective algorithmic procedure is developed to find the optimal solutions to the proposed model.The applicability of the proposed model is illustrated by numerical example.Sensitivity analysis of the optimal solution with respect to key parameters has been carried out and the implications are discussed.The study indicates that though promotional effort stimulates the market demand,it is beneficial in an economic sense when it applies in a conservative manner.
