• Journal of the Korean Operations Research and Management Science Society
• /
• v.34 no.3
• /
• pp.71-84
• /
• 2009

This paper considers a remanufacturing and purchasing planning problem, in which either used products(or wastes) are remanufactured or remanufactured products(or final products) are purchased to satisfy dynamic demands of remanufactured products over a discrete and finite time horizon. Also, as remanufactured products are purchased more than or equal to a special quantity Q, a discount price policy is applied. The problem assumes that the related cost(remanufacturing and inventory holding costs of used products, and the purchasing and inventory holding costs of remanufactured products) functions are concave and backlogging is not allowed. The objective of this paper is to determine the optimal remanufacturing and purchasing policy that minimizes the total cost to satisfy dynamic demands of remanufactured products. This paper characterizes the properties of the optimal policy and then, based on these properties, presents a dynamic programming algorithm to find the optimal policy. Also, a network-based procedure is proposed for the case of a large quantity of low cost used products. A numerical example is then presented to demonstrate the procedure of the proposed algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.10 no.16
• /
• pp.129-132
• /
• 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.

• Journal of the Korea Safety Management & Science
• /
• v.16 no.4
• /
• pp.323-330
• /
• 2014

Various inventory control theories have tried to modelling and analyzing supply chains by using quantitative methods and characterization of optimal control policies. However, despite of various efforts in this research filed, the existing models cannot afford to be applied to the realistic problems. The most unrealistic assumption for these models is customer demand. Most of previous researches assume that the customer demand is stationary with a known distribution, whereas, in reality, the customer demand is not known a priori and changes over time. In this paper, we propose a reinforcement learning based adaptive echelon base-stock inventory control policy for a multi-stage, serial supply chain with non-stationary customer demand under the service level constraint. Using various simulation experiments, we prove that the proposed inventory control policy can meet the target service level quite well under various experimental environments.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.18 no.34
• /
• pp.61-67
• /
• 1995

Through the model presented in this paper, we study on the depletion of stock taking place due to random loss of items as well as random demand, under the assumption that the distributions of demand are independent of those of loss, and both of them are identical, and that life time distribution of each item is negative exponential. The steady state probability distribution of the stock level assuming instantaneous delivery of order under (s, S) inventory policy. Also we have derived total expected cost expression with loss cost. The results of sensitive analysis show that the effect of loss rate is substantial on the total cost and optimal value of inventory level.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.31 no.2
• /
• pp.113-127
• /
• 2006

We consider an integrated supply chain model in which multiple suppliers replenish items for a single buyer's demand. Also the buyer specifies a basic replenishment cycle and the suppliers replenish the items only at those time intervals. Namely, we propose a model to study and analyze the benefit by coordinating supply chain inventories through the basic replenishment cycle time. The objective of this model is to minimize the total relevant annual cost of the integrated inventory model. After developing proposed coordinated models, we suggest heuristics for searching the solutions of our models. Finally, numerical and computational experiments for each policy are carried out to evaluate the benefits of those models and the compensation policy is addressed to share the benefits.

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

• Journal of Korean Society for Quality Management
• /
• v.16 no.2
• /
• pp.92-98
• /
• 1988

In this paper a model is developed for an inventory system in which the number of units of acceptable quality in a replenishment lot is uncertain and the demand. during the stockout period is back ordered and. also under the same condition an inventory model with experdited stockout is developed. It is assumed that the fraction of the acceptable quality in a replenishment lot is a random variable whose probability distribution is known. The optimal replenishment policy is synthesized for such a system. A numerical example is used to illustrate the theory.

• Journal of the military operations research society of Korea
• /
• v.9 no.1
• /
• pp.69-74
• /
• 1983

This article presents a deterministic 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. By defining a time proportional backorder cost and a fixed penalty cost per unit lost, a convex objective function representing the average annual cost of operating the inventory system is obtained. The optimal operating policy variables are calculated directly. At the extremes ${\beta}\;=\;1$ and ${\beta}\;=\;0$ the model presented reduces to the usual backorders and lost sales case, respectively.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.24 no.65
• /
• pp.11-22
• /
• 2001

Recently, many studies on the supply chain management have been published due to increasing attention placed on the design and performance analysis of the supply chain as a whole. Using the Beer distribution game introduced in Sterman[1995], we develop a simple order-up-to-R inventory model to minimize sum of the inventory holding cost and shortage cost under probabilistic demand. We show that performance of the model is robust through extensive simulation experiment. Applying the model to serially connected supply chain, we observe that, if the unit shortage cost is relatively high, R value computed independently is an optimal solution.

• Journal of the Korea Society for Simulation
• /
• v.12 no.3
• /
• pp.31-40
• /
• 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).