• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.9 no.1
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• pp.99-110
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• 2005

This paper suggests an efficient approach for stochastic bicriteria programming problem (SBCPP) with random variables in both the objective functions and in the right-hand side of the constraints. The suggested approach uses the statistical inference through two different techniques: In one of them, the SBCPP is transformed into an equivalent deterministic bicriteria programming problem (DBCPP), then the nonnegative weighted sum approach will be used to transform the bicriteria programming problem into a single objective programming problem, and the other technique, the nonnegative weighted sum approach is used to transform the SBCPP to an equivalent stochastic single objective programming problem, then apply the same procedure to convert stochastic single objective programming problem into its equivalent deterministic single objective programming problem (DSOPP). In both techniques the resulting problem can be solved as a nonlinear programming problem to get the efficient solutions. Finally, a comparison between the two different techniques is discussed, and illustrated example is given to demonstrate the actual application of these techniques.

• Journal of Korean Port Research
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• v.13 no.1
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• pp.27-40
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• 1999

This study aims to apply and examine the stochastic approach for empty container repositioning and leasing problem. For this a case study has been carried out on actual data such as various cost components and traffic flow. The results reveal that the proposed methodology produces more realistic results than the conventional deterministic approaches. It is also found that the results are significantly affected by the accuracy of demand and supply forecast.

• Journal of the Korean Operations Research and Management Science Society
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• v.3 no.2
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• pp.41-45
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• 1978

Duel models are frequently used in war game simulation. Both game-theoretic approach and stochastic approach are applied to duel situations in war game. Game-theoretic models are usually classified into three categories, noisy duel, silent duel, and duel of continuous firing. Stochastic duels are classified depending upon assumptions. In this paper formulation and a general solution for each model will be summarized.

• Structural Engineering and Mechanics
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• v.3 no.1
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• pp.1-10
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• 1995

This study presents a methodology for the system reliability analysis of cracked structures with random material properties, which are modeled as random fields, and crack geometry under random static loads. The finite element method provides the computational framework to obtain the stress intensity solutions, and the first-order reliability method provides the basis for modeling and analysis of uncertainties. The ultimate structural system reliability is effectively evaluated by the stable configuration approach. Numerical examples are given for the case of random fracture toughness and load.

• Korean Journal of Computational Design and Engineering
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• v.2 no.4
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• pp.215-221
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• 1997

A new approach in solving design centering problem is presented. Like most stochastic optimization problems, optimal design centering problems have intrinsic difficulties in multivariate intergration of probability density functions. In order to avoid to avoid those difficulties, genetic algorithm and very coarse Monte Carlo simulation are used in this research. The new algorithm performs robustly while producing improved yields. This result implies that the combination of robust optimization methods and approximated simulation schemes would give promising ways for many stochastic optimizations which are inappropriate for mathematical programming.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.156-161
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• 1994

In this paper, we consider an optimal control problem of a nonlinear stochastic system. Dynamic programming approach is employed for the formulation of a stochastic optimal control problem. As an optimality condition, dynamic programming equation so called the Bellman equation is obtained, which seldom yields an analytical solution, even very difficult to solve numerically. We obtain the numerical solution of the Bellman equation using an algorithm based on the finite difference approximation and the contraction mapping method. Optimal controls are constructed through the solution process of the Bellman equation. We also construct a test case in order to investigate the actual performance of the algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.440-444
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• 1998

In this paper, we consider numerical solution of a H-J-B (Hamilton-Jacobi-Bellman) equation of elliptic type arising from the stochastic control problem. For the numerical solution of the equation, we take an approach involving contraction mapping and finite difference approximation. We choose the It(equation omitted) type stochastic differential equation as the dynamic system concerned. The numerical method of solution is validated computationally by using the constructed test case. Map of optimal controls is obtained through the numerical solution process of the equation. We also show how the method applies by taking a simple example of nonlinear spacecraft control.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.411-419
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• 2016

This article investigates the problem of distributed channel selection in opportunistic spectrum access networks from a perspective of interference minimization. The traditional physical (PHY)-layer interference model is for information theoretic analysis. When practical multiple access mechanisms are considered, the recently developed binary medium access control (MAC)-layer interference model in the previous work is more useful, in which the experienced interference of a user is defined as the number of competing users. However, the binary model is not accurate in mathematics analysis with poor achievable performance. Therefore, we propose a real-valued one called stochastic MAC-layer interference model, where the utility of a player is defined as a function of the aggregate weight of the stochastic interference of competing neighbors. Then, the distributed channel selection problem in the stochastic MAC-layer interference model is formulated as a weighted stochastic MAC-layer interference minimization game and we proved that the game is an exact potential game which exists one pure strategy Nash equilibrium point at least. By using the proposed stochastic learning-automata based uncoupled algorithm with heterogeneous learning parameter (SLA-H), we can achieve suboptimal convergence averagely and this result can be verified in the simulation. Moreover, the simulated results also prove that the proposed stochastic model can achieve higher throughput performance and faster convergence behavior than the binary one.

• Transactions of the Korean Society of Mechanical Engineers
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• v.6 no.4
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• pp.308-314
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• 1982

An experimental study has been made for the dispersion phenomena by a stochastic model in a turbulent pipe flow. Local instantaneous passage of suspended solid particles were recorded in two dimensions, employing a periscopic system coupled vidicon camera. Probability density of passage was calculated. Second moment shows qualitatively that dispersive action is dependent on particle's geometric characteristics in vertical pipe flow. In case that density of the solid particles is larger than that of liquid, particles have a tendency to approach from the center of pipe to the wall, and in the contrary case the approach the center of pipe. It seems that there exists a field of radial accelerations, centrifugal or centripetal according to the sign of density difference between two phases.

• 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.