• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.426-426
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• 1999

;There are many sources of uncertainty in a typical production and inventory system. There is uncertainty as to how many items customers will demand during the next day, week, month, or year. There is uncertainty about delivery times of the product. Uncertainty exacts a toll from management in a variety of ways. A spurt in a demand or a delay in production may lead to stockouts, with the potential for lost revenue and customer dissatisfaction. Firms typically hold inventory to provide protection against uncertainty. A cushion of inventory on hand allows management to face unexpected demands or delays in delivery with a reduced chance of incurring a stockout. The proposed strategies are used for the design of a probabilistic inventory system. In the traditional approach to the design of an inventory system, the goal is to find the best setting of various inventory control policy parameters such as the re-order level, review period, order quantity, etc. which would minimize the total inventory cost. The goals of the analysis need to be defined, so that robustness becomes an important design criterion. Moreover, one has to conceptualize and identify appropriate noise variables. There are two main goals for the inventory policy design. One is to minimize the average inventory cost and the stockouts. The other is to the variability for the average inventory cost and the stockouts The total average inventory cost is the sum of three components: the ordering cost, the holding cost, and the shortage costs. The shortage costs include the cost of the lost sales, cost of loss of goodwill, cost of customer dissatisfaction, etc. The noise factors for this design problem are identified to be: the mean demand rate and the mean lead time. Both the demand and the lead time are assumed to be normal random variables. Thus robustness for this inventory system is interpreted as insensitivity of the average inventory cost and the stockout to uncontrollable fluctuations in the mean demand rate and mean lead time. To make this inventory system for robustness, the concept of utility theory will be used. Utility theory is an analytical method for making a decision concerning an action to take, given a set of multiple criteria upon which the decision is to be based. Utility theory is appropriate for design having different scale such as demand rate and lead time since utility theory represents different scale across decision making attributes with zero to one ranks, higher preference modeled with a higher rank. Using utility theory, three design strategies, such as distance strategy, response strategy, and priority-based strategy. for the robust inventory system will be developed.loped.

• Journal of applied mathematics & informatics
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• v.6 no.2
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• pp.539-552
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• 1999

An order-level inventory model for a perishable product with a time-dependent demand is developed for a fixed planning pe-riod allowing backlogging in all cycles within the said period. The market demand is assumed to decrease exponentially as time elapses. The average system cost is derived and its optimization procedure is illustrated with a numerical example. Sensitivity of the optimal so-lution to changes in the values of different parameters of the system is also analysed.

• Journal of Korean Institute of Industrial Engineers
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• v.4 no.1
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• pp.49-55
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• 1978

This paper presents a new method, called a modified economic order quantity method, for determining the optimal inventory policy, which uses the rate of return as its decision criterion. Especially for the simplest possible inventory system with constant demand rate, no backlogging, no lead time, etc., the formula for the optimal order policy is derived. Also mentioned are the relative merits and shortcomings of this method compared to the conventional EOQ model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.1-8
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• 2000

We present in this paper an optimal stocking policy for a repairable inventory system under reliability improvement. For this purpose we illustrate commercial flight lines with a large number of planes. This model is supported by a central repair facility. For modeling the nonstationary M/M/s system we implemented SIMAN for computing the time dependent number of units in the repair facility with any number of units. In this model we provide the required inventory level at each location. 1y month. for various levels of associated stock-out risk.

• IE interfaces
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• v.10 no.1
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• pp.119-134
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• 1997

The management of blood inventory is very important within the medical care system. The efficient management of blood supplies and demands for transfusions is of great economic and social importance to both hospitals and patients. For any blood type, there is a complex interaction among the optimal inventory level, daily demand level, daily supply level, transfusion to crossmatch ratio, crossmatch release period, issuing policy and the age of arriving units that determine the shortage and outdate rate. In this paper, we develop an efficient decision rule for blood inventory management in a hospital blood bank which can support efficient hospital blood inventory management using simulation. The primary use of the efficient decision rule will be to establish minimum cost function which consists of inventory levels, period in inventory, outdate and shortage rate for whole blood and various component inventories for a hospital blood bank or a transfusion service. If the administrator compute the mean daily demand for each blood type, the mean daily supply for each blood type, the length of the crossmatch release period and the average transfusion to crossmatch ratio, then it is possible to apply the efficient decision rule to compute the optimal inventory level, inventory period, outdate and shortage rate. This rule can also be used as a decision support system that allows the blood bank administrator to do sensitivity analysis related to controllable blood inventory parameters.

• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.13-27
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• 1996

The management of blood inventory is very important within the medical care system. The efficient management of blood supplies and demands for transfusion is of great economic and social importance to both hospitals and patients. Fro any blood type, there is a complex interaction among the optimal inventory level, daily demand level , daily supply level, transfusion to crossmatch ratio, crossmatch release period, issuing policy and the age of arriving units that determine the shortage and outdate rate. In this paper, we develop an efficient decision rule for blood inventory management in a hospital blood bank which can support efficient hospital blood inventory management using simulation, The primary use of the efficient decision rule will be to establish minimum cost function which consists of inventory levels , period in inventory, outdate and shortage rate for whole blood and various component inventories for a hospital blood bank or a transfusion service, If the adminstrator compute the mean daily demand for each blood type, the mean daily supply for each blood type, the length of the crossmatch release period and the average transfusion to crossmatch ratio , then it is possible to apply the efficient decision rule to compute the optimal inventory level, inventory period , outdate and shortage rate. This rule can also be used as a decision support system that allows the blood bank adminstrator to do sensitivity analysis related to controlled blood inventory parameters.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.05a
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• pp.198-208
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• 2007

Recently business enterprises have forced to face in fierce competition in today's global markets due to the short life cycles of products and the higher expectation of customers. Together with continuing advances in communications and transportation technologies, these environments have motivated the continuous evolution of the supply chain and the management techniques. This paper consider three-echelon inventory system which consist of one manufacturer, one distributor and N retailers for a single product under assumption of constant demand. This paper propose the inventory replenishment period using heuristic method and order policy through coordination of inventory replenishment period. The simulation results show that decrease the total cost of the three-echelon inventory system.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.2
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• pp.15-23
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• 1992

This paper dvelops an inventory model with partial backorders considering both inflation rate and discount rate under the situation of deterministic demand and lead time and then make an economic analysis. Especially, the inventory model with partial backorders provided here is the inventory model minimizing annual total cash outflows, which is extended by the addition of inflation rate and discount rate into "Inventory Model with Partial Bakorders" of Park [6]. An iterative solution procedure is developed to find an optimal inventory policy. To provide guidelines for economic analysis of inventory model with partial backorders, sensitivity analysis for selected values of parameters is performed.performed.

• Journal of the Korea Safety Management & Science
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• v.2 no.4
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• pp.209-217
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• 2000

In this paper, we consider an Inventory system with multi-suppliers. A supply agreement is made with one of the suppliers, to deliver a fixed quantity Q evry review period ； That is, adapting to discounts of under the condition of free addition often implies that the timing and sizes of future replenishment orders are less predetermined. The replenishment decisions for the other supplier are governed by a replenishment policy. This paper, multiple suppliers strategy is a combination of a push system (the main supplier delivers every review period a predetermined quantity Q) and a pull system the replenishment orders placed at other suppliers are governed by replenishment policy. The costs are defined as the sum of the ordering, holding, purchasing and opportunity costs. Based on numerical results, conclusions follow about the division of the replenishment volume among the inventory policy.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.3
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• pp.111-127
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• 2004

We consider a supply chain model with a make-to-order production facility and a single supplier. The model we treat here is a special case of a two-echelon inventory model. Unlike classical two-echelon systems, the demand process at the supplier is affected by production process at the production facility as well as customer order arrival process. In this paper, we address that how the demand variability impacts on the optimal replenishment policy. To this end, we incorporate Erlang and phase-type demand distributions into the model. Formulating the model as a Markov decision problem, we investigate the structure of the optimal replenishment policy. We also implement a sensitivity analysis on the optimal policy and establish its monotonicity with respect to system cost parameters.