• Korean Management Science Review
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• v.31 no.2
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• pp.1-13
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• 2014

In almost all of the organizations, the cost for acquiring and maintaining the inventory takes a considerable portion of the management budget, and thus a certain constraint is set upon the budget itself. The previous studies on inventory control for each item that aimed to improve the fill rate, backorder, and the expenditure on inventory are fitting for the commercially-operated SCM, but show some discrepancies when they are applied to the spare parts for repairing disabled systems. Therefore, many studies on systematic approach concept considering spare parts of various kinds simultaneously have been conducted to achieve effective performance for the inventory control at a lower cost, and primarily, METRIC series models can be named. However, the past studies were limited when dealing with the probability distributions for representing the situation on demand and transportation of the parts, with the (S-1, S) inventory control policy, and so on. To address these shortcomings, the Continuous Time Markov Chain (CTMC) model, which considers the phase-type distributions and the (s, Q) inventory control policies to best describe the real-world situations inclusively, is presented in this study. Additionally, by considering the cost versus the system availability, the optimization of the inventory level, based on this model, is also covered.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.133-138
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• 2006

The problem that we address is to determine the inventory stockage levels in a multi-echelon inventory system for repairable items in a multi-indenture system. We propose the simulation optimization approach to determine the stockage levels at each echelon, where a simulator for the underlying system is combined with an appropriate optimization tool, Genetic Algorithm (GA).

• Journal of the Korea Society for Simulation
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• v.12 no.2
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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).

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.52-63
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• 2017

The inventory level of concurrent spare parts (CSP) has a significant impact on the availability of a weapon system. A failure rate function might be of particular importance in deciding the CSP inventory level. We developed a CSP optimization model which provides a compromise between purchase costs and shortage costs on the basis of the Weibull and the exponential failure rate functions, assuming that a failure occurs according to the (non-) homogeneous Poisson process. Computational experiments using the data obtained from the Korean Navy identified that, throughout the initial provisioning period, the optimization model using the exponential failure rate tended to overestimate the optimal CSP level, leading to higher purchase costs than the one using the Weibull failure rate. A Pareto optimality was conducted to find an optimal combination of these two failure rate functions as input parameters to the model, and this provides a practical solution for logistics managers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.69-78
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• 2010

Multi-criteria ABC inventory classification is one of the most widely employed techniques for efficient inventory control, and it considers more than one criterion for categorizing inventory items into groups of different importance. Recently, Ramanathan (2006) proposed a weighted linear optimization (WLO) model for the problem of multi-criteria ABC inventory classification. The WLO model generates a set of criteria weights for each item and assigns a normalized score to each item for ABC analysis. Although the WLO model is considered to have many advantages, it has a limitation that many items can share the same optimal efficiency score. This limitation can hinder a precise classification of inventory items. To overcome this deficiency, we propose a context-dependent DEA based method for multi-criteria ABC inventory classification problems. In the proposed model, items are first stratified into several efficiency levels, and then the relative attractiveness of each item is measured with respect to less efficient ones. Based on this attractiveness measure, items can be further discriminated in terms of their importance. By a comparative study between the proposed model and the WLO model, we argue that the proposed model can provide a more reasonable and accurate classification of inventory items.

• Management Science and Financial Engineering
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• v.21 no.1
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• pp.11-17
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• 2015

Min-max stochastic optimization is an approach to address the distribution ambiguity of the underlying random variable. We present a unified approach to the problem which utilizes the theory of convex order on the random variables. First, we consider a general framework for the problem and give a condition under which the convex order can be utilized to transform the min-max optimization problem into a simple minimization problem. Then extremal distributions are presented for some interesting classes of distributions. Finally, applications to the single-period inventory control problems are given.

• Journal of Korean Society for Quality Management
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• v.22 no.1
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• pp.214-218
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• 1994

An inventory supplies stock continuously at a constant rate. A deliveryman arrives according to a Poisson process. If the level of the inventory, when he arrives, exceeds a threshold, no action is taken, otherwise a delivery is made by a random amount. Costs are assigned to each visit of the deliveryman, to each delivery, to the inventory being empty and to the stock being kept. It is shown that there exists a unique arrival rate of the deliveryman which minimizes the average cost per unit time over an infinite horizon.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.1
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• pp.165-171
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• 2014

We develop an optimization algorithm for a periodic review inventory system under a stochastic budget constraint. While most conventional studies on the periodic review inventory system consider a simple budget limit in terms of the inventory investment being less than a fixed budget, this study adopts more realistic assumption in that purchasing costs are paid at the time an order is arrived. Therefore, probability is employed to express the budget constraint. That is, the probability of total inventory investment to be less than budget must be greater than a certain value assuming that purchasing costs are paid at the time an order is arrived. We express the budget constraint in terms of the Lagrange multiplier and suggest a numerical method to obtain optional values of the cycle time and the safety factor to the system. We also perform the sensitivity analysis in order to investigate the dependence of important quantities on the budget constraint. We find that, as the amount of budget increases, the cycle time and the average inventory level increase, whereas the Lagrange multiplier decreases. In addition, as budget increases, the safety factor increases and reaches to a certain level. In particular, we derive the condition for the maximum safety factor.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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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.

• Management Science and Financial Engineering
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• v.20 no.2
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• pp.1-5
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• 2014

The robust inventory control problem was proposed and solved by Bertsimas and Thiele (2006). Their results are very interesting in that the problem can be solved easily and also the solution possesses nice properties of those found in the traditional stochastic inventory control problem. However, their formulation is shown to be incorrect, which invalidates all of the results given there. In this paper, we propose an alternative formulation of the problem which uses a different but practically applicable uncertainty set. Under the newly proposed model, all of the useful properties given in Bertsimas and Thiele (2006) will be shown to be valid.