• 대한산업공학회지
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• 제19권1호
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• pp.61-71
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• 1993

We focus on a way of combining the Monte Calro methods of antithetic variates and control variates to reduce the variance of the estimator of the mean response in a simulation experiment. Combined Method applies antithetic variates (partially) for driving approiate stochastic model components to reduce the vaiance of estimator and utilizes the correlations between the response and control variates. We obtain the variance of the estimator for the response analytically and compare Combined Method with control variates method. We explore the efficiency of this method in reducing the variance of the estimator through the port operations model. Combined Method shows a better performance in reducing the variance of estimator than methods of antithetic variates and control variates in the range from 6% to 8%. The marginal efficiency gain of this method is modest for the example considered. When the effective set of control variates is small, the marginal efficiency gain may increase. Though these results are from the limited experiments, Combined Method could profitably be applied to large-scale simulation models.

• 한국시뮬레이션학회논문지
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• 제19권1호
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• pp.133-141
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• 2010

본 연구는 층화추출법과 통제변수기법을 시뮬레이션 실험설계에서 동시에 사용하여 파라미터의 추정 효율성을 개선하는 방법을 제안하였다. 시뮬레이션을 수행하는 도중에 수집한 표준화 부가변수를 통제변수기법에 활용하여 파라미터 추정을 위한 반응변수의 오차를 줄이도록 하였으며 통제변수 구성에 사용되지 않은 부가변수를 층화변수로 활용하는 층화추출기법을 적용하여 통제반응변수의 변이성을 추가로 감소하였다. 반응변수와 통제변수 사이의 공분산 관계가 알려진 경우에 두 방법을 동시에 활용하여 파라미터를 추정하는 기법의 시뮬레이션 효율성을 이론적으로 도출하였다. 반응변수와 통제변수 사이의 상관관계 구조가 알려지지 않은 경우 선택된 모형의 시뮬레이션 결과는 제안된 층화 통제 추정기법이 층화추출법이나 통제변수기법을 개별적으로 적용하여 파라미터를 추정하는 것보다 우수한 것으로 나타나고 있다.

• 한국시뮬레이션학회논문지
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• 제14권3호
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• pp.129-136
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• 2005

We develop a variance reduction technique applicable in one simulation experiment whose purpose is to estimate the parameters of a first order linear model. This method utilizes the control variates obtained during the course of simulation run under Schruben and Margolin's method (S-M method). The performance of this method is shown to be similar in estimating the main effects, and to be superior to S-M method in estimating the overall mean response in a given model. We consider that a proposed method may yield a better result than S-M method if selected control variates are highly correlated with the response at each design point.

• 한국시뮬레이션학회논문지
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• 제5권1호
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• pp.91-106
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• 1996

Control Variate (CV) is very useful technique for variance reduction in a wide class of queueing network simulations. However, the loss in variance reduction caused by the estimation of the optimum control coefficients is an increasing function of the number of control variables. Therefore, in some situations, it is required to select an optimal set of control variables to maximize the variance reduction . In this paper, we develop the Adaptive Control Variates (ACV) method which selects an optimal set of control variates during the simulation adatively. ACV is useful to maximize the simulation efficiency when we need iterated simulations to find an optimal solution. One such an example is the Simulated Annealing (SA) because, in SA algorithm, we have to repeat in calculating the objective function values at each temperature, The ACV can also be applied to the queueing network optimization problems to find an optimal input parameters (such as service rates) to maximize the throughput rate with a certain cost constraint.

• 한국시뮬레이션학회논문지
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• 제1권1호
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• pp.1-16
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• 1992

This research develops two variance reduction methods for estimating the parameters of the experimental simulation model having multiple design points based on an approach focusing on reduction of the variances of the mean responses across multiple design points. The first method extends a combined approach of antithetic variates and control variates for a single design point to the multipopulation context with independent streams across the design points. The second method extends the same strategy in conjunction with the Schruben-Margolin method for improving the first method. We illustrate an example for implementing the second method. We expect these two approaches may improve the estimation of the parameters of interest compared with the control variates method.

• 한국시뮬레이션학회논문지
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• 제4권1호
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• pp.25-36
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• 1995

We develop a variance reduction technique in one simulation experiment whose purpose is to estimate the parameters of a first-order linear model. This method utilizes the control variates obtained during the course of simulation run under Schruben and Margolin's method (S-M method). The performance of this method is shown to be similar in estimating the main effects, and to be superior to S-M method in estimating the overall mean response in the hospital simulation experiment. For the general case, we consider that a proposed method may yield a better result than S-M method if selected control variates are highly correlated with the response at each design point.

• 한국시뮬레이션학회논문지
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• 제9권4호
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• pp.67-75
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• 2000

Often system analysts are interested in the estimation of percentile for system performance. For instance, in PERT network system, the percentile that the project. Typically the control variate method is used to reduce the variability of mean response using the correlation between the response and the control variates with a little additional cost during the course of simulation. In the same spirit, we apply this method to estimate the percentile of project completion time in PERT system, and evaluate the efficiency of the controlled estimator for its percentile.1 Simulation results indicate that the controlled estimators are more effective in reducing the variances of estimators than the simple estimators, however those tend to a little underestimate the percentiles for some critical values. We need more simulation experiments to examine such a kind of bias problem. We expect this research presents a step forward in the area of variance reduction techniques of stochastic simulation.

• 한국시뮬레이션학회논문지
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• 제18권2호
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• pp.49-55
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• 2009

본 연구는 확률적 시스템에서 관심 성과함수의 기대치의 최적을 유도하는 서비스 자원의 최적 배분 문제를 조사하였다. 이러한 목적으로 통제변수를 활용하여 성과함수 기대치에 대한 서비스 자원 파라미터의 gradient를 구하는 방법을 제안하고 이를 최적화 기법의 탐색과정에 적용하여 가용 자원의 최적 배분 문제를 분석하였다. 제안된 gradient 추정 방법은 시뮬레이션 실험에서 입력 파라미터의 차원이 증가하더라도 추가로 표본점의 수를 증가시킬 필요가 없이 단일점에서 시뮬레이션 반응 결과만을 활용하고 또한 시뮬레이션의 발전과정에서 성과함수와 입력 파라미터 사이의 논리적인 관계를 기술할 필요가 없어 적용하기에 편리하다고 볼 수 있다. 본 연구의 결과를 다 차원 파라미터 공간으로의 확장하는 문제와 다양한 형태의 시뮬레이션 모형으로 적용 문제는 향후 연구해야 할 과제로 생각된다.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1992년도 제2회 정기총회 및 추계학술 발표회 발표논문 초록
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• pp.8-8
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• 1992

Through a simulation experiment, often an experimenter is concerned with estimating the system parameters of the linear model consisting of m design points from the outputs oft the simulation model. To improve the estimation of the system parameters and reliability of these estimators, appropriate simulation techniques have been developed. For the first order linear model, Schruben and Margolin (1978) exploited the random number assignment rules which uses a combination of common random numbers and antithetic streams in a simulation experiment designed to estimate the system parameters when the design matrix of simulation model admits orthogonal blocking into two blocks. Nozari, Arnold and Pegden (1984) developed a method for appliying the method of control variates to the situation of the linear model having multiple design points. This talk deals with a different way of utilizing controls under the correlation induction strategy of Schruben and Margolin's to improve the simulation efficiency, and presents a procedure for obtaining the estimators of the system parameters analytically. Simulation results on a selected simulation model indicate a promising evidence that a proposed method may yield better results than Schruben and Margolin's method.