• Journal of Korean Institute of Industrial Engineers
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• v.32 no.1
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• pp.9-17
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• 2006

This paper deals with a discrete simulation optimization method for designing a complex probabilistic discrete event simulation. The developed algorithm uses the configuration algorithm that can change decision variables and the stopping algorithm that can end simulation in order to satisfy the given objective value during single run. It tries to estimate an auto-regressive model for evaluating correctly the objective function obtained by a small amount of output data. We apply the proposed algorithm to M/M/s model, (s, S) inventory model, and known-function problem. The proposed algorithm can't always guarantee the optimal solution but the method gives an approximate feasible solution in a relatively short time period. We, therefore, show the proposed algorithm can be used as an initial feasible solution of existing optimization methods that need multiple simulation run to search an optimal solution.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.87-98
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• 2002

A competitive coevolutionary algorithm is a probabilistic search method that imitates coevolution process through evolutionary arms race. The algorithm has been used to solve adversarial problems. In the algorithms, the selection of competitors is needed to evaluate the fitness of an individual. The goal of this study is to compare and analyze several competition strategies in terms of solution quality, convergence speed, balance between competitive coevolving species, population diversity, etc. With two types of test-bed problems, game problems and solution-test problems, extensive experiments are carried out. In the game problems, sampling strategies based on fitness have a risk of providing bad solutions due to evolutionary unbalance between species. On the other hand, in the solution-test problems, evolutionary unbalance does not appear in any strategies and the strategies using information about competition results are efficient in solution quality. The experimental results indicate that the tournament competition can progress an evolutionary arms race and then is successful from the viewpoint of evolutionary computation.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.698-704
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• 2012

This paper proposes a new methodology for evaluating the probabilistic reliability based grid expansion planning of composite power system including the Wind Turbine Generators. The proposed model includes capacity limitations and uncertainties of the generators and transmission lines. It proposes to handle the uncertainties of system elements (generators, lines, transformers and wind resources of WTG, etc.) by a Composite power system Equivalent Load Duration Curve (CMELDC)-based model considering wind turbine generators (WTG). The model is derived from a nodal equivalent load duration curve based on an effective nodal load model including WTGs. Several scenarios are used to choose the optimal solution among various scenarios featuring new candidate lines. The characteristics and effectiveness of this simulation model are illustrated by case study using Jeju power system in South Korea.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.8-15
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• 2010

A new technique using a search method which is based on fuzzy multi-criteria function is proposed for GMS(generator maintenance scheduling) in order to consider multi-objective function. Not only minimization of probabilistic production cost but also maximization of system reliability level are considered for fuzzy multi-criteria function. To obtain an optimal solution for generator maintenance scheduling under fuzzy environment, fuzzy multi-criteria relaxation method(fuzzy search method) is used. The practicality and effectiveness of the proposed approach are demonstrated by simulation studies for a real size power system model in Korea in 2010.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.331-338
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• 2022

Face stability analyses provides the most probable failure mechanisms and the understanding about parameters that need to be considered for the evaluation of ground movements caused by tunneling. After the Upper Bound Method (UBM) solution which can consider the influence of seepage forces and depth-dependent effective cohesion is verified with the numerical experiments, the probabilistic model is proposed to calculate the unbiased limiting tunnel collapse pressure. A reliability analysis of a shallow circular tunnel driven by a pressurized shield in a frictional and cohesive soil is presented to consider the inherent uncertainty in the input parameters and the proposed model. The probability of failure that exceeding a specified applied pressure at the tunnel face is estimated. Sensitivity and importance measures are computed to identify the key parameters and random variables in the model.

• The Journal of Engineering Geology
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• v.20 no.4
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• pp.437-447
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• 2010

A probabilistic analysis of slope stability is an appropriate solution in dealing with uncertainty in problems related to engineering geology. In this study, a Monte Carlo simulation was performed to evaluate the performance function that is Barton's equation. A large number of randomly generated values were obtained for random variables, and the performance function was calculated repeatedly using randomly generated values. A previous study provided information of slope geometry and the random characteristics of random variables such as JRC and JCS. The present approach was adopted to analyze two failed slopes. The probabilities of failure were evaluated for each slope, and sensitivity analysis was performed to assess the influence of each random variable on the probability of failure. The analysis results were then compared with the results of a deterministic analysis, indicating that the probabilistic analysis yielded reliable results.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1127-1135
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• 2012

Association rule mining is the method to quantify the relationship between each set of items in a large database. One of the well-studied problems in data mining is exploration for association rules. There are three primary quality measures for association rule, support and confidence and lift. We generate some association rules using confidence. Confidence is the most important measure of these measures, but it is an asymmetric measure and has only positive value. Thus we can face with difficult problems in generation of association rules. In this paper we apply the similarity measures by probabilistic interestingness measure to find a solution to this problem. The comparative studies with support, two confidences, lift, and some similarity measures by probabilistic interestingness measure are shown by numerical example. As the result, we knew that the similarity measures by probabilistic interestingness measure could be seen the degree of association same as confidence. And we could confirm the direction of association because they had the sign of their values.

• Bulletin of the Korean Mathematical Society
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• v.53 no.5
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• pp.1497-1530
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• 2016

External barrier options are two-asset options with stochastic variables where the payoff depends on one underlying asset and the barrier depends on another state variable. The barrier state variable determines whether the option is knocked in or out when the value of the variable is above or below some prescribed barrier level. This paper derives the explicit analytic solution of the chained option with an external single or double barrier by utilizing the probabilistic methods - the reflection principle and the change of measure. Before we do this, we examine the closed-form solution of the external barrier option with a single or double-curved barrier using the methods of image and double Mellin transforms. The exact solution of the external barrier option price enables us to obtain the pricing formula of the chained option with the external barrier more easily.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.80-89
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• 2014

A stochastic optimal power flow (S-OPF) model considering uncertainties of load and wind power is developed based on chance constrained programming (CCP). The difficulties in solving the model are the nonlinearity and probabilistic constraints. In this paper, a limit relaxation approach and an iterative learning control (ILC) method are implemented to solve the S-OPF model indirectly. The limit relaxation approach narrows the solution space by introducing regulatory factors, according to the relationship between the constraint equations and the optimization variables. The regulatory factors are designed by ILC method to ensure the optimality of final solution under a predefined confidence level. The optimization algorithm for S-OPF is completed based on the combination of limit relaxation and ILC and tested on the IEEE 14-bus system.

• Journal of Korean Institute of Industrial Engineers
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• v.5 no.2
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• pp.2-7
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• 1979

The fundamental stochastic duel of Williams and Ancker is combined with the probabilistic linear, square and mixed laws of Brown and Smith when the battle time is limited and interfiring times are continuous. The Probability of a given side's winnig or a draw is derived in a recursive equation with Laplace transforms. Examples with negative exponential firing times are given. In linear law an exact closed form solution is obtained, whereas for square and mixed laws only square ($2{\times}2$) duels are considered.