• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.485-491
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• 2022

The traditional economic order quantity (EOQ) model is analyzed under the basic assumption that the purchase price is paid immediately upon receiving the product. However, product suppliers may allow a certain period of deferral of payment for product purchase costs in order to differentiate themselves from competitors. From the distributor's point of view, such a credit transaction can temporarily divert product purchase costs, resulting in a reduction in inventory investment costs, and ultimately, a factor that lowers the selling price for the purpose of increasing end-customer demand can be. In addition, in that credit transactions are provided for the purpose of increasing the demand of suppliers as a means of differentiation from competitors, it is more general to be allowed flexibly according to the transaction volume. In this regard, assuming that the end customer's demand is represented by a linear decreasing function of the distributor's selling price, this study analyzes a model for determining the distributor's pricing and ordering policies under order-size-dependent delay in payments. For the analysis, we also assume that the inventory is depleted not only by customer's demand but also by decaying.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.4
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• pp.113-128
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• 2016

This paper proposes a Bi-level Genetic Algorithm for the Fixed Charge Transportation Problem with Non-linear Unit Cost. The problem has the property of mixed integer program with non-linear objective function and linear constraints. The bi-level procedure consists of the upper-GA and the lower-GA. While the upper-GA optimize the connectivity between each supply and demand pair, the lower-GA optimize the amount of transportation between the pairs set to be connected by the upper-GA. In the upper-GA, the feasibility of the connectivity are verified, and if a connectivity is not feasible, it is modified so as to be feasible. In the lower-GA, a simple method is used to obtain a pivot feasible solution under the restriction of the connectivity determined by the upper-GA. The obtained pivot feasible solution is utilized to generate the initial generation of chromosomes. The computational experiment is performed on the selected problems with several non-linear objective functions. The performance of the proposed procedure is analyzed with the result of experiment.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.3
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• pp.105-116
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• 1994

This paper presents an inventory model with partial backorders for the situation in which demand is deterministic, lead time follows normal distribution and back order ratio during the stockout period varies in proportion to the length of backorder period In this situations, an objective function is formulated to minimize a time-proportional backorder cast and a fixed penalty cost per unit lost. And then the procedure of iterative solution method for the model is developed to find optimal reorder paint and order quantity and a numerical example to illustrate the proposed method is presented.

• Steel and Composite Structures
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• v.21 no.1
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• pp.93-107
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• 2016

Seismic fragility analysis is a probabilistic decision-making framework which is widely implemented for evaluating vulnerability of a building under earthquake loading. It requires ingredient named probabilistic model and commonly developed using statistics requiring collecting data in large quantities. Preparation of such a data-base is often costly and time-consuming. Therefore, in this paper, by developing generic seismic drift demand model for regular-multi-story steel moment resisting frames is tried to present a novel application of the probabilistic decision-making analysis to practical purposes. To this end, a demand model which is a linear function of intensity measure in logarithmic space is developed to predict overall maximum inter-story drift. Next, the model is coupled with a set of regression-based equations which are capable of directly estimating unknown statistical characteristics of the model parameters.To explicitly address uncertainties arise from randomness and lack of knowledge, the Bayesian regression inference is employed, when these relations are developed. The developed demand model is then employed in a Seismic Fragility Analysis (SFA) for two designed building. The accuracy of the results is also assessed by comparison with the results directly obtained from Incremental Dynamic analysis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.10
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• pp.1548-1553
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• 2012

The important requirement for microgrid operation is to meet the balance between supply and demand. To meet, Combined Heat and Power (CHP) generation should be considered in microgrid scheduling. CHP generation is economical on the side of a consumer because it products heat and power. Therefore, it is high efficient. This paper presents a mathematical model for optimal microgrid operation including CHP generation using the optimal ratio of heat and power due to demand. The objective function and constraints are modeled by linear program (LP). Through the case study, the validation of the proposed model is shown.

• Journal of Korean Institute of Industrial Engineers
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• v.1 no.2
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• pp.39-49
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• 1975

The problem treated is that of locating distribution centers(depot) in a network, so as to minimize the total cost which is the sum of transportation cost, (from factory to centers and from centers to demand points), construction cost, inventory cost and traffic increasing cost. This problem is mathematically an integer program and a non-linear model. This study avoids various inefficient aspects, which many studies have shown, by introducing a matrix notation, node and link function. An algorithm, for determining the optimal location of distribution center which has zone in which demand points are located at some node of a network, is presented. Finally this paper describes a numerical example and discusses its results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.3
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• pp.27-46
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• 1992

This paper considers an (r, Q) policy for operation of a multipurpose facility. It is assumed that whenever the inventory level falls below r, the model starts to produce the fixed amount of Q. The facility can be utilized for extra production during idle periods, that is, when the inventory level is still greater than r right after a main production operation is terminated or an extra production operation is finished. But, whenever the facility is in operation for an extra production, the operation can not be terminated for the main production even though the inventory level falls below r. In the model, the demand for the product is assumed to arrive according to a compound Poisson process and the processing time required to produce a product is assumed to follow an arbitary distribution. Similarly, the orders for the extra production is assumed to accur in a Poisson process are the extra production processing time is assumed to follow an arbitrary distribution. It is further assumed that unsatisfied demands are backordered and the expected comulative amount of demands is less than that of production during each production period. Under a cost structure which includes a setup/ production cost, a linear holding cost, a linear backorder cost, a linear extra production lost sale cost, and a linear extra production profit, an expression for the expected cost per unit time for a given (r, Q) policy is obtained, and using a convex property of the cost function, a procedure to find the optimal (r, Q) policy is presented.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.6
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• pp.340-346
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• 2003

This paper presents a game theory application for an analysis of uniform price auction in a simplified competitive electricity market and analyzes the properties of Nash equilibrium for various conditions. We have assumed that each generation firm submits his bid to a market in the form of a sealed bid and the market is operated as a uniform price auction. Two firms are supposed to be the players of the market, and we consider the maximum generation quantity constraint of one firm only. The system demand is assumed to have a linear relationship with market clearing prices and the bidding curve of each firm, representing the price at which he has a willingness to sell his generation quantity, is also assumed to have a linear function. In this paper, we analyze the effects of maximum generation quantity constraints on the Nash equilibrium of the uniform price auction. A simple numerical example with two generation firms is demonstrated to show the basic idea of the proposed methodology.

• Journal of Environmental Health Sciences
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• v.21 no.4
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• pp.37-43
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• 1995

The course of biodegradation of anionic surfactants, Linear Alkylbenzene Sulfonates(LAS), Sodium Lauryl Ethoxylate Sulfonate(SLES), and Sodium Lauryl Sulfonates(SLS), which are mainly used to make detergents and shampoo, was investigated. The degree of biodegradation was studied as a function of concentration, volumetric flow rate, and temperature in Naktong River. MethyleneBlue Active Substances(MBAS), Total Dissolved Organic Carbon(TOC), and Chemical Oxygen Demand(COD) were measured to evaluate the degree of biodegradation. The degree of biodegradation of LAS was highly dependent upon the concentration and was increased as the concentration was decreased and that of SLES and SLS was almost constant at the concentration of less than 200 ppm, but was much increased as the volumetric flow rate was increased or the temperature was increased.