• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.37
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• pp.111-115
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• 1996

This study is to decide each level ordering policy for deterministic demand in multilevel distribution system. The ordering policy is used the combinations of EOQ and LTC. The combinations of EOQ and LTC are 4 cases. Case 1 : Regional Warehouse∼EOQ, Central Warehouse∼EOQ. Case 2 : Regional Warehouse∼EOQ, Central Warehouse∼LTC. Case 3 : Regional Warehouse∼LTC, Central Warehouse∼EOQ. Case4 : Regional Warehouse∼LTC, Central Warehouse∼LTC. The criterion is to minimize total cost per year.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.2
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• pp.99-107
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• 2007

We consider (worst-case) robust optimization versions of the Economic Order Quantity (EOQ) model with decreasing cost functions. Two variants of the EOQ model are discussed, in which the purchasing costs are decreasing power functions in either the order quantity or demand rate. We develop the corresponding worst-case robust optimization models of the two variants, where the parameters in the purchasing cost function of each model are uncertain but known to lie in an ellipsoid. For the robust EOQ model with the purchasing cost being a decreasing function of the demand rate, we derive the analytical optimal solution. For the robust EOQ model with the purchasing cost being a decreasing function of the order quantity, we prove that it is a convex optimization problem, and thus lends itself to efficient numerical algorithms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.2
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• pp.43-50
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• 2008

In this paper we suggest the methods that compute the total inventory cost based on EOQ and the total inventory cost based on OMMIP. The total inventory cost consists of purchasing cost, ordering cost, inventory holding cost, stockout cost and so on. This papers also proposes the method that decides optimum order quantity as the order amount to minimize the total inventory cost with comparison of EOQ total inventory cost and OMMIP total inventory cost according to inventory holding cost as a fraction of unit cost.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.221-227
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• 2021

An early warning system was developed to help identify stock status as early as possible. For performance to improve, there needs to be a feature to predict the amount of stock that must be provided and a feature to estimate when to buy goods. This research was conducted to improve the inventory early warning system and optimize the Reminder Block's performance in minimum stock settings. The models used in this study are the single exponential smoothing (SES) method for prediction and the economic order quantity (EOQ) model for determining the quantity. The research was conducted by analyzing the Reminder Block in the early warning system, identifying data needs, and implementing the SES and EOQ mathematical models into the Reminder Block. This research proposes a new Reminder Block that has been added to the SES and EOQ models. It is hoped that this study will help in obtaining accurate information about the time and quantity of repurchases for efficient inventory management.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.532-541
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• 1997

This article derives an analytic solution to determine the optimal size of multiple noncontinuous process and storage units. The total cost to be minimized consists of the setup cost of noncontinuous processing units and the inventory holding cost of feedstock/product storages. A novel approach, which is called PSW(Periodic Square Wave) model, is applied to represent the material flow among non-continuous units and storages. PSW model presumes that the material flow between unit and storage is periodic square wave shaped. The resulting optimal unit size has similar characteristics with the classical economic lot sizing model such as EOQ(Economic Order Quantity) or EPQ(Economic Production Quantity) model in a sense that the unit size is determined as the balance between setup and inventory holding cost. However, the influence of inventory holding cost of PSW model is different from that of EOQ/EPQ model. EOQ/EPQ model includes only the product inventory holding cost but PSW model includes all inventory holding costs around the non-continuous unit with proportional contribution. PSW model is suitable for analyzing interlinked process-storage system. The optimal lot size of PSW model is smaller than that of EOQ/EPQ model. This is quitea remarkable result considering that the EOQ/EPQ model has been is widely used since last half century.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.3
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• pp.13-26
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• 1992

Set-up reduction is an important aspect of the Japanese Just-In-Time (JIT) and Zero Inventory (ZI) concepts. In this paper, we first analyze the effects of set-up cost reduction on tatal inventory, average lot size and forecast horizon in the dynamic lot size model. We also examine the various effects of set-up cost reduction in the EOQ model and explain why many Japanese firms try to cut set-up cost and/or set-up time greatly.

• Management Science and Financial Engineering
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• v.13 no.1
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• pp.35-53
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• 2007

We consider a(worst-case) robust optimization version of the Economic Order Quantity(EOQ) model. Order setup costs and inventory carrying costs are assumed to have uncertainty in their values, and the uncertainty description of the two parameters is supposed to be given by an ellipsoidal representation. A genetic algorithm combined with Monte Carlo simulation is proposed to approximate the ellipsoidal representation. The objective function of the model under ellipsoidal uncertainty description is derived, and the resulting problem is solved by another genetic algorithm. Computational test results are presented to show the performance of the proposed method.

• IE interfaces
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• v.17 no.1
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• pp.78-83
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• 2004

We consider the sensitivity of average inventory cost rate when true values of the parameters in the EOQ model are unknown over known ranges. In particular, in the case that the valid range on the true economic lot size are known, we provide a formula for estimating the lot size under minimax criterion. Moreover, to estimate the valid range, we apply the propagation of errors technique. Then, we present a scheme to find a (valid) lot size, based on the estimated range of the true lot size from the propagation of errors technique.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.3
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• pp.248-256
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• 2005

In this paper, we compare two EOQ based inventory models under total cost minimization and profit maximization to investigate the difference in the optimal solutions. First of all, optimal solutions for both models through geometric programming (GP) techniques are found considering production (lot sizing) as well as marketing (pricing) decisions. An investigation of the effects of the changes in the optimal solutions according to varied parameters is performed by studying optimality conditions as well as by performing numerical analysis. We then conduct comparative analysis between the models to show the relationships between the optimal solutions of the models where certain conditions in the cost per unit and the demand per unit time are given. Several interesting economic implications and managerial insights are observed from this analysis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.4
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• pp.83-89
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• 2004

In this paper MIP(mean inventory period) Model and OMMIP decision flow have been developed. MIP model can calculate mean inventory period which is subject to the order quantity alternative plan. OMMIP decision flow leads how can decide the most minimized order quantity in mean inventory period among various order quantity alternatives. This paper also suggests how to select the order quantity with minimum inventory period as optimal order quantity by means of comparison each mean inventory period with other mean inventory period, after simulating EOQ and order quantity of OMMIP calculated in MIP model.