Why is eoq robust

Unfortunately, the parameters are ordinarily unknown, and furthermore, it is also hard to identify their probability information. This thesis assumes that all parameters are unknown and information about their probability distributions is also unknown.

In this uncertain situation, a robust optimization point of view describes each unknown parameter as a continuous value that is restricted to some prespecified interval. To address uncertain data input, this thesis uses robust optimization to analyze the basic Economic Order Quality EQQ model and the deterministic serial two-echelon inventory model.

First of all, this thesis derives the functions that show the upper and lower bounds of EOQs under input data uncertainty. Your rate of demand for an item must be known, and spread evenly throughout the year. Your lead time must be fixed. The purchase price of the item in question must be constant, with no discounts available. You must be able to make replenishment instantaneously, with the whole batch delivered at once.

Now, tell me: can you guarantee that all five of these statements are true for the items you're ordering? Take 1, for example. Most companies can calculate their carrying costs fairly easily. But what about your ordering costs-can you measure them accurately?

Even small changes in these costs can have a major impact on how much you have to order and how often. The EOQ is very sensitive to changes in the order cost or carrying cost. If your business doesn't measure this accurately, how can you put EOQ into use?

And perhaps more importantly, if your business is like most, your demand is anything but flat and constant throughout the year. Most companies see demand fluctuate from one season to the next, often dramatically. It is essentially a single formula for determining the optimal order size that minimizes the sum of carrying costs and ordering costs. The model formula is derived under a set of simplifying and restrictive assumptions, as follows :.

Assumptions of the EOQ model include constant demand, no shortages, constant lead time, and instantaneous order receipt. Figure An order quantity, Q , is received and is used up over time at a constant rate. When the inventory level decreases to the reorder point, R , a new order is placed, and a period of time, referred to as the lead time , is required for delivery.

The order is received all at once, just at the moment when demand depletes the entire stock of inventory and the inventory level reaches zero , thus allowing no shortages. This cycle is continuously repeated for the same order quantity, reorder point, and lead time. As we mentioned earlier, Q is the order size that minimizes the sum of carrying costs and holding costs.

These two costs react inversely to each other in response to an increase in the order size. As the order size increases, fewer orders are required, causing the ordering cost to decline, whereas the average amount of inventory on hand increases , resulting in an increase in carrying costs.

Thus, in effect, the optimal order quantity represents a compromise between these two conflicting costs. Carrying cost is usually expressed on a per-unit basis for some period of time although it is sometimes given as a percentage of average inventory.

Traditionally, the carrying cost is referred to on an annual basis i. The total carrying cost is determined by the amount of inventory on hand during the year. The amount of inventory available during the year is illustrated in Figure In Figure The line connecting Q to time, t , in our graph represents the rate at which inventory is depleted, or demand , during the time period, t.

Demand is assumed to be known with certainty and is thus constant, which explains why the line representing demand is straight. Also, notice that inventory never goes below zero; shortages do not exist. In addition, when the inventory level does reach zero, it is assumed that an order arrives immediately after an infinitely small passage of time, a condition referred to as instantaneous receipt.

This is a simplifying assumption that we will maintain for the moment. Referring to Figure Similarly, the amount of inventory is zero for an infinitely small period of time because the only time there is no inventory is at the specific time t. Thus, the amount of inventory available is somewhere between these two extremes.

In this approach, each unknown parameter is described as a continuous value restricted to be in a prespecified interval. The objective of this paper is to build a robust inventory policy in… Expand. Save to Library Save. Create Alert Alert.

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