• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.378-384
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• 2002

In this paper, we propose a mixed integer optimization approach for solving the inventory problem with variable lead time, reorder point, crashing cost and price -quantity discount. Chang and Chang[15] study a continuous review inventory model in which lead time is a decision variable under price-quantity discount. However, their study cannot find the optimal solution due to the flaws in the modeling and the solution procedure. We present a complete procedure to find the optimal solution for the model. In addition to the above contribution, we also apply the minimax distribution free approach to the model to devise a practical procedure which can be used without specific information on demand distribution.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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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.

• Journal of the military operations research society of Korea
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• v.18 no.1
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• pp.86-98
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• 1992

In this research, and expert system for military requirement management was developed using the expert system shell, EXSYS Professional. Expert system is an intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solution. From the expert system developed in this research, we can get the informations about the economic order quantity and its related informations for the specific items. And, for an effective supply management, the informations about operating level, safety level, order and shipping time, stockage objective, reorder point, requisitioning objective and requisition quantity of the specific items are acquired by this expert system.

• Journal of the Korea Society for Simulation
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• v.12 no.3
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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).

• 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).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.2
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• pp.57-65
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• 2009

In continuous review inventory model, (${\varrho}$, ${\gamma}$) system, order quantity(${\varrho}$) and reorder point(${\gamma}$) should be determined to calculate inventory-related cost that consists of setup, holding, and penalty costs. The procedure to obtain the exact value of ${\varrho}$ and ${\gamma}$ is complex. In this paper, a regression analysis is proposed to get the approximate inventory-related cost without the determination of ${\varrho}$ and ${\gamma}$ in the case that the standard deviation(${\sigma}$) of the lead time demand is small or that the mean(${\mu}$) of the lead time demand is proportional to ${\sigma}$. To save inventory-related cost, central warehouses with (${\varrho}$, ${\gamma}$) system can be built. Central warehouse can provide some stores with products with the consideration of the tradeoff between inventory-related cost and transportation cost. The number and the location of central warehouses to cover all the stores are determined by a regression-based approach. The performance of the proposed approach is tested by using some computational experiments.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.4
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• pp.134-139
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• 2008

Most approaches for continuous review inventory problem need tables for loss function and cumulative standard normal distribution. Furthermore, it is time-consuming to calculate order quantity (Q) and reorder point (r) iteratively until required values are converged. The purpose of this paper is to develop a direct method to get the solution without any tables. We used approximation approaches for loss function and cumulative standard normal distribution. The proposed method can get the solution directly without any iterative procedure for Q, r and without any tables. The performance of the proposed approach is tested by using numerical examples. The budget constraint of this paper assumes that purchasing costs are paid at the time an order is arrived. This constraint can be easily replaced by capacity constraint or budget constraint in which' purchasing costs are paid at the time an order is placed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.4
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• pp.7-14
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• 2016

Unlike most researches that focus on single manufacturer or single buyer, this research studies the cooperation policy for two participants of supply chain such as single vendor and single buyer. Especially, this paper deals with single vendor-single buyer integrated-production inventory problem. If the buyer orders products, then the vendor will start to make products and then the products will be shipped from the vendor to the buyer many times. The buyer is supposed to order again when the buyer's inventory level hits reorder point during the last shipment and this cycle keeps repeated. The buyer uses continuous review inventory policy and customer's demand is assumed to be probabilistic. The contribution of this paper is to present a mixed approach and derive its cost function. The existing policy assumes that the size of shipping batch from single vendor to single buyer is increasing, called Type 1, or constant, called Type 2. In mixed approach, the size of shipping batch is increasing at the beginning part of the cycle, and then its size is constant at the ending part of the cycle. The number of shipping for Type 1 and Type 2 in a cycle in mixed approach is determined to minimize total cost. The relationship between parameters, for example, the holding cost per product, the set up cost per order, and the shortage cost per item and decision variables such as order quantity, safety factor, the number of shipments, and shipment increasing factor is figured out via sensitivity analysis. Finally, it is statistically proved that the mixed approach is superior to the existing approaches.

• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.217-229
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• 2013

As customers' demands for diversified small-quantity products have been increased, there have been great efforts for a firm to respond to customers' demands flexibly and minimize the cost of inventory at the same time. To achieve that goal, in SCM perspective, many firms have tried to control the inventory efficiently. We present an mathematical model to determine the near optimal (s, S) policy of the supply chain, composed of multi suppliers, a warehouse and multi retailers. (s, S) policy is to order the quantity up to target inventory level when inventory level falls below the reorder point. But it is difficult to analyze inventory level because it is varied with stochastic demand of customers. To reflect stochastic demand of customers in our model, we do the analyses in the following order. First, the analysis of inventory in retailers is done at the mathematical model that we present. Then, the analysis of demand pattern in a warehouse is performed as the inventory of a warehouse is much effected by retailers' order. After that, the analysis of inventory in a warehouse is followed. Finally, the integrated mathematical model is presented. It is not easy to get the solution of the mathematical model, because it includes many stochastic factors. Thus, we get the solutions after the stochastic demand is approximated, then they are verified by the simulations.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.143-149
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• 2020

Currently, a multi-core processor is mainly used as a central processing unit of a computer system, and a high-performance out-of-order processor is adopted as each core to maximize system performance. The early out-of-order execution processor with Tomasulo algorithm aimed at floating-point instructions, and it took several cycles to execute by the use of complex structures such as reorder buffer and reservation station. However, in order for the processor to properly utilize out-of-order execution and increase the throughput of instructions, it must operate in a fully pipelined manner. In this paper, a fully pipelined out-of-order processor with speculative execution is designed with VHDL and verified with GHDL. As a result of the simulation, a program composed of ARM instructions is successfully performed.