Abstract-The single-period inventory models have wide applications in the real world in s&sting
the decision maker to determine the optimal quantity to order. Due to lack of historical data, the
demand has to be subjectively determined in many cases. In this paper, a single-period inventory
model for cases of fuzzy demand is constructed. The costs considered include the procurement cost,
shortage cost, and holding cost. For different fuzzy total cost resulted from different order quantity, a
method for ranking fuzzy numbers is adopted to find the optimal order quantity in terms of the cost.
When the profit gained from selling one item is less (greater) than the loss incurred due to one unsold
item, the optimal order quantity lies in the range defined for the left-shape (right-shape) function of
the fuzzy demand. If the unit profit is equal to the unit loss, then all quantities with a membership
grade 1 are optimal to be ordered. The methodology of this paper can be applied to construct other
inventory models with fuzzy demand. @ 2002 Elsevier Science Ltd. All rights reserved.
Abstract-The single-period inventory models have wide applications in the real world in s&sting
the decision maker to determine the optimal quantity to order. Due to lack of historical data, the
demand has to be subjectively determined in many cases. In this paper, a single-period inventory
model for cases of fuzzy demand is constructed. The costs considered include the procurement cost,
shortage cost, and holding cost. For different fuzzy total cost resulted from different order quantity, a
method for ranking fuzzy numbers is adopted to find the optimal order quantity in terms of the cost.
When the profit gained from selling one item is less (greater) than the loss incurred due to one unsold
item, the optimal order quantity lies in the range defined for the left-shape (right-shape) function of
the fuzzy demand. If the unit profit is equal to the unit loss, then all quantities with a membership
grade 1 are optimal to be ordered. The methodology of this paper can be applied to construct other
inventory models with fuzzy demand. @ 2002 Elsevier Science Ltd. All rights reserved.
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