• 한국국방경영분석학회지
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• 제7권2호
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• pp.63-99
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• 1981

The generalized goal decomposition model proposed by Ruefli as a single period decision model is presented for the purpose of a review and extended to make a multiple period planning model. The multiple period planning model in the three level organization is formulated with, linear goal deviations by introducing the goal programming method. Dynamic formulation using the generalized goal decomposition model for each single period problem is also presented. An iterative search algorithm is presented as an appropriate solution method of the dynamic formulation of the multiple period planning model.

• 산업경영시스템학회지
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• 제32권4호
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• pp.53-62
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• 2009

Lee[15] examined quantity discount contracts between a manufacturer and a retailer in a stochastic, two-period inventory model where quantity discounts are provided based on the previous order size. During the two periods, the retailer faces stochastic (truncated Poisson distributed) demands and he/she places orders to meet the demands. The manufacturer provides for the retailer a price discount for the second period order if its quantity exceeds the first period order quantity. In this paper we extend the above two-period model to a k-period one (where k < 2) and propose a stochastic nonlinear mixed binary integer program for it. In order to make the program tractable, the nonlinear term involving the sum of truncated Poisson cumulative probability function values over a certain range of demand is approximated by an i-interval piecewise linear function. With the value of i selected and fixed, the piecewise linear function is determined using an evolutionary algorithm where its fitness to the original nonlinear term is maximized. The resulting piecewise linear mixed binary integer program is then transformed to a mixed binary integer linear program. With the k-period model developed, we suggest a solution procedure of receding horizon control style to solve n-period (n < k) order decision problems. We implement Lee's two-period model and the proposed k-period model for the use in receding horizon control style to solve n-period order decision problems, and compare between the two models in terms of the pattern of order quantities and the total profits. Our computational study shows that the proposed model is superior to the two-period model with respect to the total profits, and that order quantities from the proposed model have higher fluctuations over periods.

• 산업경영시스템학회지
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• 제27권4호
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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.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 봄 학술논문 발표대회
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• pp.361-362
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• 2023

In construction work, there is often a difference between the estimated construction period and the actual construction period. Accordingly, the project may be delayed from the scheduled date, leading to huge losses due to problems such as increased costs during construction. In this way, it is important to calculate the appropriate construction period at the project planning stage in construction work. To solve this problem, we would like to study a model that will increase the accuracy of the scheduled construction period at the project planning stage. This study compared and analyzed linear regression, Lasso regression, Ridge regression among the types of regression analysis to select an appropriate construction period prediction model to secure an appropriate construction period at the project planning stage to reduce problems during construction.

• 한국경영과학회지
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• 제37권4호
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• pp.37-50
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• 2012

The existing DEA models have been devoted to evaluate relative efficiency of DMUs based on multiple input and output factors of a same period. However, a certain kind of lead time can be required to produce outputs using inputs in an organization. R&D evaluation is a typical area with this kinds of time lag. Thus, the purpose of this paper is to develop a new DEA model to deal with time lag effect in performance evaluation. The proposed model is to find relative efficiency of each DMU for each period considering the time lag effect. A case example using a real data set is also given to show the usage or implication of the suggested model. The results are compared with the ones of the CCR model and the multi-periods input model.

• 한국산림과학회지
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• 제83권2호
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• pp.121-132
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• 1994

윤벌기(輪伐期)는 각 영급이 존재하는 삼림의 생산(生産)을 조절하기 위해서 설정된 삼림경영목표(森林經營目標)중 가장 중요한 인자중의 하나이다. 본 연구(硏究)에서는 특히 최적화(最適化)계산의 형태로 법정임(法正林)모델에서의 최적윤벌기(最適輪伐期)가 추정(推定)되었다. 그리고 법정림모델과의 연계하에 자연상태에서 여러가지 위험조건에 처해있는 현실임분(現實林分)에 더욱 접근시키기 위하여 목표임(目標林)모델이 간단작업급삼림(間斷作業級森林)모델(모델 1)과 연년보속작업급삼림(連年保續作業級森林)모델(모델 2)의 두가지로 나뉘어 전개되었으며 이 두 모델에 의해 최적윤벌기가 계산되었다. 동시에 법정림모델, 목표림모델 1, 2에 의해 추정된 최적윤벌기가 상호 비교되었다. 이때 강원대학교(江原大學校) 임과대학(林科大學) 부속(附屬) 연습림(演習林) 내(內) 잣나무임분이 최적윤벌기(最適輪伐期) 계산의 대상이 되었다.

• 한국통신학회논문지
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• 제34권6B호
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• pp.623-629
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• 2009

본 연구에서는 소프트웨어 제품을 개발하는 과정에서 보증기간을 고려하여 사용자에게 인도하는 시기를 결정하는 방출시기에 관한 모형의 비교 연구이다. 실증 비교 결과 지수화 소프트웨어 신뢰성장모형에서는 보증기간이 증가함에 따라 초기에는 최적방출시기가 증가했으나 점차 거의 유사한 방출시기를 나타내고 있으며 이와 반대로 불완전한 디버깅 소프트웨어 신뢰성장 모형에서는 초기에는 최적방출시기가 감소하였으나 보증기간이 길수록 소폭 증가하는 패턴을 보이고 있으며 제안된 극값모형은 점차적으로 증가하는 형태를 가진다는 결론을 도출 할 수 있다. 이러한 연구결과를 통하여 소프트웨어 개발자들은 보증기간과 방출최적시기의 관계를 파악함으로서 소프트웨어 개발 시 사전정보로 활용 할 수 있으리라 사료된다.

• The Journal of Asian Finance, Economics and Business
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• 제8권5호
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• pp.533-542
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• 2021

The main objective of this article is to investigate the existence of the lunar effect during the full moon period (FM period) and the new moon period (NM period) on the selected Islamic stock market returns and volatilities. For this purpose, the Ordinary Least Squares model, Autoregressive Conditional Heteroscedasticity model, Generalised Autoregressive Conditional Heteroscedasticity model and Generalised Autoregressive Conditional Heteroscedasticity-in-Mean model are employed using the mean daily returns data between January 2010 and December 2019. Next, the log-likelihood, Akaike Information Criterion and Schwarz Information Criterion value are analyzed to determine the best models for explaining the returns and volatility of returns. The empirical results have deduced that, during the NM period, excluding Malaysia, the total mean daily returns for all of the selected countries have increased mean daily returns in contrast to the mean daily returns during the FM period. The volatility shocks are intense and conditional volatility is persistent in all countries. Subsequently, the volatility behavior tends to have lower volatility during the FM period and NM period in the Islamic stock market, except Malaysia. This article also concluded that the ARCH (1) model is the preferred model for stock returns whereas GARCH-M (1, 1) is preferred for the volatility of returns.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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• pp.260-260
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• 2011

SWAT is semi-distributed and continuous-time distributed simulation watershed model, which can simulate point and nonpoint source pollutants as well as hydrology and water quality. It was developed to predict the effects of alternative management decisions on water, sediment, and chemical yields with reasonable accuracy. It is able to predict and manage hydrology, sediments, nutrients, and pesticides with Best Management Practices (BMPs) in a watershed. SWAT model also has potential for use in ungauged basins to predict streamflow and baseflow from saturated source area in watersheds. According to various cultivation practices and climate change, SWAT model is available to analyze relative change in hydrology and water quality. In order to establish optimum management of water quality, both monitering and modeling have been conducted actively using SWAT model. As SWAT model is computer program to simulate a lot of natural phenomena, it has limitation to predict and reflect them with on hundred percent accuracy. Thus, it is possible to analyze the effect of BMPs in the watershed where users want to simulate hydrology and water quality only if model accuracy and applicability are assessed first of all and the result of it is well for the study watershed. For assessment of SWAT applicability, most researchers have used $R^2$ and Nash and Sutcliffe Efficiency (NSE). $R^2$ and NSE are likely to show different results according to a warm up period and sometimes its results are very different. There have been hardly any studies of whether warm up period can affect simulation results in SWAT model. In this study, how warm up period has a effect on SWAT results was analyzed and a appropriate warm up period was suggested. Lots of SWAT results were compared after using measured data of Soyanggang-dam watershed and applying various warm up period (0 ~ 10 year(s)). As a result of this study, when there was no warm up period, $R^2$ and NSE were 0.645, 0.602 respectively, when warm up period was 2 years, $R^2$ and NSE were 0.648, 0.632, and when warm up period was 4 years, $R^2$ and NSE were 0.663, 0.652 separately. Through this study, sensitive analysis of warm up period in SWAT model was conducted, and this study could give a guideline able to simulate hydrology and water quality for more accuracy than before as users change a lot of warm up periods as well as any simulation parameters.

• 산업경영시스템학회지
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• 제42권3호
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• pp.8-14
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• 2019

Most of the data envelopment analysis (DEA) models evaluate the relative efficiency of a decision making unit (DMU) based on the assumption that inputs in a specific period are consumed to produce the output in the same period of time. However, there may be some time lag between the consumption of input resources and the production of outputs. A few models to handle the concept of the time lag effect have been proposed. This paper suggests a new multi-period input DEA model considering the consistent time lag effects. Consistency of time lag effect means that the time delay for the same input factor or output factor are consistent throughout the periods. It is more realistic than the time lag effect for the same output or input factor can vary over the periods. The suggested model is an output-oriented model in order to adopt the consistent time lag effect. We analyze the results of the suggested model and the existing multi period input model with a sample data set from a long-term national research and development program in Korea. We show that the suggested model may have the better discrimination power than existing model while the ranking of DMUs is not different by two nonparametric tests.