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.
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: