• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.4
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• pp.91-112
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• 2009

The bullwhip effect means the phenomenon of increasing demand variation as moving UP to the upstream in the supply chain. Therefore, it is recognized that the bullwhip effect is problematic for effective supply chain operations. In this paper, we exactly quantifies the bullwhip effect for the case of stochastic lead time and seasonal demand in two-echelon supply chain where retailer employs a base-stock policy considering SARMA demand processes and stochastic lead time. We also investigate the behavior of the proposed measurement for the bullwhip effect with autoregressive and moving average coefficient, stochastic lead time, and seasonal factor.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.1
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• pp.20-24
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• 2008

A bid-based pool(BBP) model is representative of energy market structure in a number of restructured electricity markets. Supply function equilibrium(SFE) models of interaction better match what is explicitly required in the bid formats of typical BBP markets. Many of the results in the SFE literature involve restrictive parametrization of the bid cost functions. In the SFE models, two parameters, intercept and slope, are available for strategic bidding. This paper addresses the realistic competition format that players can choose both parameters arbitrarily. In a fixed demand function, equilibrium conditions for generation company's profit maximization have a degree of freedom, which induces multi-equilibrium. So it is hard to choose a convergent equilibrium. However, consideration of stochastic demand function makes the equilibrium conditions independent each other based on the amount of variance of stochastic demand function. This variance provides the bidding players with incentives to change the slope parameter from an equilibrium for a fixed demand function until the slope parameter equilibrium.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.44-51
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• 2017

In today's competitive environment, supply chain management is a major concern for a company. Two of the key issues in supply chain management are transportation and inventory management. To achieve significant savings, companies should integrate these two issues instead of treating them separately. In this paper we develop a framework for modeling stochastic programming in a supply chain that is subject to demand uncertainty. With reasonable assumptions, two stochastic programming models are presented, respectively, including a single-period and a multi-period situations. Our assumptions allow us to capture the stochastic nature of the problem and translate it into a deterministic model. And then, based on the genetic algorithm and stochastic simulation, a solution method is developed to solve the model. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.

• 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.

• Journal of the Korean Operations Research and Management Science Society
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• v.4 no.1
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• pp.59-64
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• 1979

The application of optimization techniques to the planning of industrial, economic, administrative and military activities with random technological coefficients has been extensively studied in the literature. Stochastic (linear) programs with simple recourse essentially model the allocation of scarce resources under uncertainty with linear penalties associated with shortages or surplus. This work on a problem with a discrete random resource vector, "The allocation of aircraft under uncertain demand" given in (1), is easily and efficiently handled by the application of the recently developed Wets' algorithm (8) for solving stochastic programs with simple recourse, which approves that such class of stochastic problems can be solved with the same efficiency as solving linear programs of the same size. It is known that the algorithm is also applicable to stochastic programs with continuous random demands for their approximate solutions.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.27-35
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• 2005

Due to the importance of lead time demand in the design of inventory management systems, researchers and practitioners have paid continuous attention and a few analytic models using the compound distribution approach have been reported. However, since the nature of compound distributions is hardly amenable, the analytic models have been done by non‐recognition of the compound nature of some components to reduce the analytic task. This study concerns some of the important aspects in the analytic models. Through the theoretic examination of the analytic model approach and the comparison with the rigid compound stochastic process approach, this study clarifies the assumptions implicitly made by the analytic models and provides some precautions in using the analytic models. Illustrative examples are also presented.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.2
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• pp.169-175
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• 2001

Most of studies on automated storage/retrieval (AS/R) system assumed that storage capacity is given, although it is a very important decision variable in the design phase. We propose a simple algorithm to estimate the required storage capacity, i.e., number of aisles and number of openings in vertical and horizontal directions in each aisle, of an AS/R system under stochastic demand, in which storage requests occur endogenously and exogenously while the retrieval requests occur endogenously from the machines. Two design criteria, maximum permissible overflow probability and maximum allowable storage/retrieval (S/R) machine utilization, are used to compute the storage capacity. This model can be effectively used in the design phase of new AS/R systems.

• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.99-108
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• 2007

This paper presents an elastic demand stochastic user equilibrium traffic assignment that could not be easily tackled. The elastic demand coupled with a travel performance function is known to converge to a supply-demand equilibrium, where a stochastic user equilibrium (SUE) is obtained. SUE is the state in which all equivalent path costs are equal, and thus no user can reduce his perceived travel cost. The elastic demand SUE traffic assignment can be formulated based on a dynamic system, which is a means of describing how one state develops into another state over the course of time. Traditionally it has been used for control engineering, but it is also useful for transportation problems in that it can describe time-variant traffic movements. Through the Lyapunov Function Theorem, the author proves that the model has a stable solution and confirms it with a numerical example.

• Korean Management Science Review
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• v.15 no.1
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• pp.33-47
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• 1998

The facility location problem considered here is to determine facility location sites under future's uncertain demand. The objective of this paper is to propose a solution method and algorithm for a two-stage stochastic facility location problem. utilizing the Benders decomposition method. As a two-stage stochastic facility location problem is a large-scale and complex to solve, it is usually attempted to use a mean value problem rather than using a stochastic problem. Thus, the other objective is to study the relative error of objective function values between a stochastic problem and a mean value problem. The simulation result shows that the relative error of objective function values between two problems is relatively small, when a feasibility constraint is added to a facility location model.

• Journal of Electrical Engineering and Technology
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• v.11 no.5
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• pp.1093-1099
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• 2016