• Journal of the military operations research society of Korea
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• v.23 no.2
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• pp.135-154
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• 1997

This paper deals with the perishable inventory models with uncertainties of demand functions. The traditional perishable inventory costs of holding and stockout are incorporated into the cost function. The average expected cost will be minimized to find the optimal quantile estimator. After three candidate estimators are proposed on the basis of order statistics, they will be evaluated by the simulation results and statistical analysis. Then the transition procedure algorithm using this estimator will be proposed to make the optimal decision under uncertainty.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.211-219
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• 2009

The logistic environment of Korean companies faces very challenging circumstances due to lack of resources and tough price competitions. It is obvious that such a load of logistic cost becomes an obstacle to national or business competition advantages in the era of unlimited market competition. Prior studies concerning these issues sought to express a large-scale distribution network in a unified numerical formula using defined symbols and could take systematic approaches to distribution network. But inventory policy and operation in material distribution system have been limited to conventional methodology. so they are exposed to many questions in practical applications. This study analyzed distribution system cost and developed a relevant model for operation policy of distribution system for the efficeient treating of stockout. allowing practical applications, so that it will contribute to saving operational cost related to material distribution system.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.1
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• pp.51-58
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• 1991

This paper presents a single-echelon, single item, stochastic lead time and static demand inventory model for situations in which, during the stockout period, a fraction ${\beta}$ of the demand is backordered and the remaining fraction $(1-{\beta})$ is lost. In this situations, an objective function representing the average annual cost of inventory system is obtained by defining a time-proportional backorder cost and a fixed penalty cost per unit lost. The optimal operating policy variables minimizing the average annual cost are calculated iteratively. At the extremet ${\beta}=1$, the model presented reduces to the usual backorder case. A numerical example is solved to illustrate the algorithm developed.

• Journal of Korean Society for Quality Management
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• v.14 no.2
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• pp.21-27
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• 1986

This paper deals with a continuous review (s,S) spare part inventory system. The distributions of service life of each part and the replenishment lead time are assumed to be exponential. Assuming that there is never more than a single order outstanding, we obtain the average annual cost of operating the inventory system. If the length of stockout period is small enough to be neglected compared to the length of operating period, the optimal operating policy variables minimizing the cost rate can be calculated iteratively. For the case of one-for-one ordering (that is, s=S-1), an exact cost rate, and a closed form decision rule minimizing the cost rate are obtained for a more general situation in which more than one order is allowed to be outstanding and the distribution of the replenishment lead time is general.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.139-142
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• 2000

Excessive inventories result from poor scheduling, planning, and controls, and the converse is also same. With adequate inventories, supplies can transport products to customers in time without excessive delivering cost. So efficient inventory control is vital for successful logistics management operation. In terms of mass discount retailers such as Wal-Mart, Carrefour, E-Mart, and so on, they require the high-quality services such as a small-amount and a high-frequency delivery because of having small warehouse and wanting possess much more various goods. In the opposite, manufactures ask mass discount retailer to delivery more lots of goods because of reducing the number of deliveries. It goes without saying that both wish to prevent stockout(lack of inventories). Usually, mass discount retailers have the power more than manufactures. This paper proposed how to manage inventory and how many to order in view of the TPLC and supplier. We considered the economic order quantity models for multiple items so as to prevent urgent deliveries as possible as. And the tradeoff stockout costs and delivering costs.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.3
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• pp.105-116
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• 1994

This paper presents an inventory model with partial backorders for the situation in which demand is deterministic, lead time follows normal distribution and back order ratio during the stockout period varies in proportion to the length of backorder period In this situations, an objective function is formulated to minimize a time-proportional backorder cast and a fixed penalty cost per unit lost. And then the procedure of iterative solution method for the model is developed to find optimal reorder paint and order quantity and a numerical example to illustrate the proposed method is presented.

• IE interfaces
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• v.13 no.3
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• pp.431-437
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• 2000

This paper refers to the flow of products from supplier to discount retailers(DRs). Discount retailers prefer frequent delivery of small amount because of limited storage space, while suppliers prefer less frequent delivery of larger amount in order to save transportation cost. In this paper we propose a heuristic algorithm to determine the amount of order and the frequency of delivery which decreases the expected length of stockout. We also evaluate various order policies for Vendor Managed Inventory(VMI) system using simulation with real data.

• 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 the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.81-91
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• 1994

This paper is to build an economic production quantity model for situations, in which, during the stockout period, a fraction .betha.(backorder ratio) of the demand is backordered and remaining fraction (1-.betha.) is lost. This paper develops an objective function representing the average annual cost of a production system by defining a time-weighted backorder cost and a lost sales penalty cost per unit lost under the assumptions of deterministic demand rate and deterministic production rate, and provides an algorithm for its optimal solution. At the extreme .betha.= 1, the presented model reduces to the Fabrycky's model with complete backorders.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.1
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• pp.127-136
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• 1998

This paper presents a stochastic partial backorder inventory model for the situation in which demand follows normal distribution and back order ratio during that stockout period decreases exponentially according to the length of backorder period. In the paper, an objective function is formulated to minimize the average annual cost, which is the sum of the ordering, carrying, time-proportional backordering, and lost sales cost. And then sensitivity analysis for various exponential backorder ratios and standard deviations of leadtime demand are presented. The inventory model in the paper is reduced to a backorder model and lost sales model, when backorder ratio is 1 and 0, respectively.