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where TC is the total inventory cost, OC is the ordering cost, HC is holding cost, and PC is the purchase cost.
Where: A = fixed ordering cost, [D.sub.H] = total demand over considered time horizon, [h.sub.H] = holding cost per item unit over considered time horizon, [x.sub.L] = expected demand over lead-time, f(.) = probability density function of demand over lead-time.
In this system, the total cost of inventory management consists of four components: fixed ordering cost, inventory holding cost at internal storage, inventory holding cost at external storage (called over-ordering cost in this paper), and shortage cost.
Each time the retailer has a need for replenishment, a major ordering cost, regardless of the number of the materials included, and a minor ordering cost, related to material, are incurred.
Generally, the imprecision may originate from two aspects in the JRD modeling: (1) the imprecise specification of objectives, for example, a decision-maker may have to face vague goals such as "this total cost should be around $20000"; (2) the imprecise specification of related parameters, an important task involved in the fuzzy JRD model is to predict parameters' values such as the holding cost and major ordering cost. However, due to the nonavailability of sufficient and precise input data, the precise predicted values cannot be obtained easily; just an "approximate" value may be ascertained, while fuzzy numbers can efficiently model the imprecise values.
In this paper, we consider a single-period inventory system with two suppliers and different ordering cost structures.
Economic order quantity, or EOQ, refers to the level of inventory that minimizes inventory holding and ordering cost. Ordering costs are the physical activities required to process an order.
Additionally, fixed ordering cost is incurred for placing each order.
Ordering cost is the cost of making requisition of raw material from the supplier.
Our model also allows for the ordering cost of the special one-time only order to be different from the retailer's regular ordering cost.
At first glance, it may be expected that an increase in number of deliveries will raise ordering costs. However, the LFL lot sizing method allows bundling of purchase orders for the same supplier, resulting in a lower ordering cost despite an increase in delivery frequency.
In the classical EOQ model the total annual cost of an item ([TC.sub.E]) is the sum of the cost of delivered goods, inventory ordering cost, and carrying cost, or:
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