• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.191-196
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• 2008

This paper presents an evolutionary algorithm for goal programming with preemptive priority. To do this, an evolutionary strategy is suggested which search for the solution satisfying the goals in the order of the priority. Two-sided assembly line balancing problems with multiple goals are used to validate the applicability of the algorithm. In the problems, three goals are considered in the following priority order: minimizing the number of mated-stations, achieving the goal level of workload smoothness, and maximizing the work relatedness. The proper evolutionary components such as encoding and decoding method, evaluation scheme, and genetic operators, which are specific to the problem being solved, are designed in order to improve the algorithm's performance. The computational result is reported.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.3
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• pp.58-64
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• 1999

Evolutionary programming is mainly characterized by two factors; one is the selection strategy and the other the mutation rule. In this paper, a new mutation rule that is the same form of well-known backpropagation learning rule of neural networks has been presented. The proposed mutation rule adapts the best individual's value as the target value at the generation. The temporal error improves the exploration through guiding the direction of evolution and the momentum speeds up convergence. The efficiency and robustness of the proposed algorithm have been verified through benchmark test functions.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.381-385
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• 2003

We consider the bandwidth parking problem arising from telecommunication networks The problem is to determine the set of calls to be routed and an assignment or them to the paths in arc capacitated network. The objective is to maximize profit. We formulate the problem as an integer programming and propose an algorithm to solve it. Column generation technique to solve the linear programming relxation is proposed with two types of columns in addition, to obtain an optimum integer solution, we consider a new branching strategy. Computational experiments show that the algorithm gives option at solutions within reasonably small time limits.

• Computational Structural Engineering : An International Journal
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• v.1 no.1
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• pp.21-29
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• 2001

The general topology optimization can be considered as optimal material distribution. Such an approach can be unstable, unless composite materials are introduced. In this research, a nodal volume fraction method is used to obtain the optimum topology of continuum structures. This method is conducted from a composite material model composed of isotropic matter and spherical void. Because the appearance of the chessboard patterns makes the interpretation of the optimal material layout very difficult, this method contains a chessboard prevention strategy. In this research, several topology optimization problems are presented to demonstrate the validity of the present method and the recursive quadratic programming algorithm is used to solve the topology optimization problems.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.19 no.4
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• pp.417-428
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• 2015

Although, in general, the random fluctuation of interest rates gives a limited impact on portfolio optimization, their stochastic nature may exert a significant influence on the process of selecting the proportions of various assets to be held in a given portfolio when the stochastic volatility of risky assets is considered. The stochastic volatility covers a variety of known models to fit in with diverse economic environments. In this paper, an optimal strategy for portfolio selection as well as the smoothness properties of the relevant value function are studied with the dynamic programming method under a market model of both stochastic volatility and stochastic interest rates.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.100-104
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• 2003

The SVDD(support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to consider the problem of modifying the SVDD into the direction of utilizing ellipsoids instead of balls in order to enable better classification performance. After a brief review about the original SVDD method, this paper establishes a new method utilizing ellipsoids in feature space, and presents a solution in the form of SDP(semi-definite programming) which is an optimization problem based on linear matrix inequalities.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.50
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• pp.45-54
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• 1999

Cellular manufacturing(CM) is a manufacturing philosophy and strategy for improving both productivity and flexibility. Cell formation(CF), the first and key problem faced in designing an effective CM system, is a process whereby parts with similar design features or processing requirements are grouped into part families, and the corresponding machines into machine cells. In this paper, a sophisticated fuzzy mixed-integer programming model is proposed to simultaneously form manufacturing cells and minimize the total costs of dealing with exceptional elements. Also, we will proposed a new method to solve the cell formation problem in the fuzzy environment.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1085-1087
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• 1998

A distribution systems loss minimum reconfiguration method by simulated annealing is proposed. The problem is a complex mixed integer programming problem and is very difficult to solve by a mathematical programming approach. Simulated annealing generates feasible solutions randomly and moves among these solutions using a strategy leading to a global minimum with high probabilities. The solution algorithm has been implemented in developed software package and tested on 32-bus system.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.322-329
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• 2005

This paper considers the application of mobile robot to the herding problem. The herding problem involves a ‘pursuer’ trying to herd a moving ‘evader’ to a predefined location. In this paper, two mobile robots act as pursuer and evader in the fenced area, where the pursuer robot uses a fuzzy cooperative decision strategy (FCDS) in the herding algorithm. To herd evader robot to a predefined position, the pursuer robot calculates strategic herding point and then navigates to that point using FCDS. FCDS consists of a two-level hierarchy: low level motion descriptors and a high level coordinator. In order to optimize the FCDS, we use the multi­thread evolutionary programming algorithm. The proposed algorithm is implemented in the real mobile robot system and its performance is demonstrated using experimental results.

• Journal of the military operations research society of Korea
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• v.28 no.1
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• pp.47-66
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• 2002

Instead of using signal-to-noise ratio, we attempt to optimize both the mean and variance responses using dual response optimization technique. The alternative experimental strategy analyzes a robust parameter design problem to obtain the best settings that give a target condition on the mean while minimizing its variance. The mean and variance are treated as the two responses of interest to be optimized. Unlike to the crossed array and combined array approaches, our experimental setup requires replicated runs for each control factor's treatment under noise sampling. When the postulated response models are true, they enable the coefficients to be estimated and the desired performance measure to be analyzed more efficiently. The procedure and illustrative example are given for the dual response optimization techniques of nonlinear goal programming.