• 산업경영시스템학회지
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• 제10권16호
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• pp.129-132
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• 1987

Multi-item inventory problems can be well characterized by the nature of interaction of the quantities and timing. This interaction may be due to the effect of certain combination of orders. It is that the set-up cost of ordering individual items can be saved by jointly ordering at a time. This study finds a decision criteria of optimum inventory policy through the comparisons of individual multi-item order policy(IMP), joint multi-item order policy(JMP), augmented multi-item order policy(AMP) in cost ratio. Subsequently we assume that there exists a unique optimum order level corresponding to an optimum reorder range for the augmented multi-item order, at which a cost saying is maximum.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1994년도 춘계공동학술대회논문집; 창원대학교; 08월 09일 Apr. 1994
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• pp.427-431
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• 1994

The objective of this paper is to provide an inventory control policy for the system that carries a single item with a multiple demand classes, when the demand is Poisson distributed random variable. The inventory control process includes the process of determining the reorder point, and the process of inventory control during the lead time. The goal of the optimization process is to achieve the service level of each demand class as well as the system-wide total service level at a preset desired service level while sustaining a minimum average inventory.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2002년도 추계학술대회 논문집
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• pp.101-105
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• 2002

In a traditional hierarchical inventory system, direct orders are the only information for inventory management that is exchanged between the firms involved. But due to the rapid development of modern information technology, it becomes possible for the firms to share more information in real time, e.g. demand and inventory status data. And so the term Supply Chain has emerged because it is seen as an important source of competitive advantage. Now it is possible to challenge traditional approaches to inventory management. In the past, one of the de-facto assumptions for inventory management was that the demand pattern follows a specific distribution function. However, it is undesirable to apply this assumption in real situations because the demand information in the supply chain tends to be distorted due to the bullwhip effect in a supply chain. To overcome this weakness, we propose a new solution method using NN (Neural Network). Our method proceeds in three steps. First, we find the patterns of optimal reorder points by analyzing past data. Second. train the NN using these pattern data and finally decide the reorder point. Using simulation experiment, we show that the proposed solution method gives better result than that of traditional research.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2000년도 춘계공동학술대회 논문집
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• pp.502-508
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• 2000

정보 시스템과 전자 상거래의 보편화로 실시간 판매 정보 공유가 용이하여짐에 따라, 공유 정보를 적절히 활용하는 재고 정책의 필요성이 증가하고 있다. 본 연구에서는 정보 공유 환경하의 one-warehouse multi-retailer 모델에서 개별 공유 정보를 보다 정확히 활용하여 재주문점 결정을 할 수 있는 방안을 제시하였다. 기존의 하위 계층의 재고량의 합에 기반하는 echelon stock policy 의 경우 상세 정보의 손실을 발생하는 문제가 있는 반면 본 연구에서는 개별 retailer 들의 재고량에 기반한 marginal savings의 개념을 정의하고, 이를 바탕으로 상세 정보에 기반한 재주문 정책을 제시하였다. 제시된 정책은 기존의 echelon stock policy 에 비해 우수한 결과를 나타냄을 실험을 통해 입증하였다.

• 한국경영과학회지
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• 제11권1호
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• pp.44-50
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• 1986

In this paper, we find optimal policy for the (Q, r) inventory model under the lead time uncertainty. The (Q, r) inventory model is such that the fixed order quantity Q is placed whenever the level of on hand stock reaches the reorder point r. We first develop the single level inventory model as the basis for the analysis multi-level distribution systems. The functional problem is to determine when and how much to order in order to minimize the expected total cost per unit time, which includes the set up, inventory holding and inventory shortage cost. The model, then, is extended to the multi-level distribution system consisting of the factory, warehouses and retailers. In this case, we also find an optimal policy which minimizes the total cost of the contralized multi-level distribution system.

• 한국시뮬레이션학회논문지
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• 제12권3호
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• pp.31-40
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• 2003

This paper studies an optimal policy for a certain class of (s, S) inventory control systems, where the demands are characterized by the renewal arrival process. To minimize the average cost over a simulation period, we apply a stochastic optimization algorithm which uses the gradients of parameters, s and S. We obtain the gradients of objective function with respect to ordering amount S and reorder point s via a combined perturbation method. This method uses the infinitesimal perturbation analysis and the smoothed perturbation analysis alternatively according to occurrences of ordering event changes. The optimal estimates of s and S from our simulation results are quite accurate. We consider that this may be due to the estimated gradients of little noise from the regenerative system simulation, and their effect on search procedure when we apply the stochastic optimization algorithm. The directions for future study stemming from this research pertain to extension to the more general inventory system with regard to demand distribution, backlogging policy, lead time, and inter-arrival times of demands. Another direction involves the efficiency of stochastic optimization algorithm related to searching procedure for an improving point of (s, S).

• 한국경영과학회지
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• 제37권3호
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• pp.95-116
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• 2012

The purpose of this paper is to show the effect of information sharing strategy on the supply chain performances. While traditional information sharing models assumed centralized stock information, recent supply chain practices often implement fully shared stock information, where real-time stock information is accessible on retailers. When retailers are competing with each other, this fully shared stock information may incur retailers' strategic order behaviors. Thus, this paper analyzes a simple two-level supply chain consisting of one warehouse and two identical competing retailers where the real time stock information is fully shared. The warehouse uses the traditional echelon stock policy. Under this environment the retailers' reorder decisions are derived using the order risk concept and the retailer competition mechanism is analyzed. Computational results show that the supply chain performace degradation in the fully shared stock information is quite significant, implying the importance of designing information sharing strategies in the supply chain design phase.

• 산업경영시스템학회지
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• 제35권4호
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• pp.126-132
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• 2012

This paper deals with a centralized warehouse problem with multi-item and capacity constraint. The objective of this paper is to decide the number and location of centralized warehouses and determineorder quantity (Q), reorder point (r) of each centralized warehouse to minimize holding, setup, penalty, and transportation costs. Each centralized warehouse uses continuous review inventory policy and its budget is limited. A SA (Simulated Annealing) approach is developed and its performance is tested by using some computational experiments.

• 산업경영시스템학회지
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• 제38권2호
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• pp.145-151
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• 2015

As order quantity is increased, the ordering cost per item will be cheaper due to saving of transportation and material handling costs. In this paper, two realistic assumptions such as quantity discount and budget limit are considered. Quantity discount means that all units in the order will be discounted according to the predetermined order levels. Budget limit represents that the costs for inventory investments are bounded. This paper develops a Lagrangian relaxation approach for a continuous review inventory model with a budget constraint and quantity discounts. Computational results indicate that the proposed approach provides a good solution. Sensitivity analysis is done to get some insights on budget limit and quantity discount. As budget limit or the amount of discount according to order quantity is increased, order quantity is increased, whereas reorder point is not always increased.

• 한국시뮬레이션학회논문지
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• 제12권2호
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• pp.1-11
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• 2003

In this paper, we focus an optimal policy focus optimal class of (s, S) inventory control systems. To this end, we use the perturbation analysis and apply a stochastic optimization algorithm to minimize the average cost over a period. We obtain the gradients of objective function with respect to ordering amount S and reorder point s via a combined perturbation method. This method uses the infinitesimal perturbation analysis and the smoothed perturbation analysis alternatively according to occurrences of ordering event changes. Our simulation results indicate that the optimal estimates of s and S obtained from a stochastic optimization algorithm are quite accurate. We consider that this may be due to the estimated gradients of little noise from the regenerative system simulation, and their effect on search procedure when we apply the stochastic optimization algorithm. The directions for future study stemming from this research pertain to extension to the more general inventory system with regard to demand distribution, backlogging policy, lead time, and review period. Another directions involves the efficiency of stochastic optimization algorithm related to searching procedure for an improving point of (s, S).