• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.2
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• pp.285-293
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• 2003

This paper explores an optimal design methodology for an auto lever using a genetic algorithm. Components of the auto lever have been designed sequentially in the industry, but this study presents a novel design method to determine the design parameters of components simultaneously. The genetic algorithm approach is described to decide a set of design parameters for auto lever. The authors have attempted to model the design problem with the objective of minimizing the angle variation of detent spring subject to constraints such as modulus of elasticity of steel, geometry of shift pipe, and stiffness of spring. This method gives the promising design alternative.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.520-525
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• 2003

The determination of unknown parameters in rotating machinery is a difficult task and optimization techniques represent an alternative technique for parameter identification. The Simulated Annealing(SA) and Genetic Algorithm(GA) are powerful global optimization algorithm. This paper proposes new hybrid algorithm which combined GA with SA and local search algorithm for the purpose of parameter identification. Numerical examples are also presented to verify the efficiency of proposed algorithm. And, this paper presents the general methodology based on hybrid algorithm to identify unknown bearing parameters of flexible rotors using measured unbalance responses. Numerical examples are used to ilustrate the methodology used, which is then validated experimentally.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.421-430
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• 2000

This paper presents a new approach which uses $A^*$ search and genetic algorithms for solving large scale multi-objective shortest path problem. The focus of this paper is motivated by the problem of finding Pareto optimal paths for an advanced traveler information system(ATIS) in the context of intelligent transportation system(ITS) application. The individual description, the decoding rule, the selection strategy and the operations of crossover and mutation are proposed for this problem. The keynote points of the algorithm are how to represent individuals and how to calculate the fitness of each individual. The high performance of the proposed algorithm is demonstrated by computer simulations.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.430-438
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• 2004

Among the optimization method, GA (genetic algorithm) is a very powerful searching method enough to compete with design sensitivity analysis method. GA is very easy to apply, since it dose not require any design sensitivity information. However, GA has been computationally not efficient due to huge repetitive computation. In this study, parallel computation is adopted to Improve computational efficiency, Paralleled GA is introduced on a clustered LINUX based personal computer system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.179-187
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• 2005

In this paper, we consider the problem of regrouping a number of service sites into a smaller number of service sites called centers. Each service site is represented as a point in the plane and has an associated value of service demand. We aim to group the sites so that each group has the balanced service demand and the sum of distances from the sites in the group to their corresponding center is minimized. To solve this problem, we propose a hybrid genetic algorithm that is combined with Voronoi diagrams. We provide a variety of experimental results by changing the weights of the two factors: service demands and distances. Our hybrid algorithm finds better solutions in a shorter computation time in comparison with a pure genetic algorithm.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.173-179
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• 2000

Genetic algorithm(GA) is applied for detecting multiple outliers. GA is a heuristic optimization tool solving for near optimal solution. We compare the performance of GA and the other diagnostic measures commonly used for detecting outliers in regression model. The results show that GA seems to have better performance than the others for the detection of multiple outliers.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.259-262
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• 2003

Group technology(GT) is a manufacturing philosophy which identifies and exploits the similarity of parts and processes in design and manufacturing. A specific application of GT is cellular manufacturing. the first step in the preliminary stage of cellular manufacturing system design is cell formation, generally known as a machine-part cell formation(MPCF). This paper presents and tests a grouping gentic algorithm(GGA) for solving the MPCF problem and uses the measurements of e(ficacy. GGA's replacement heuristic used similarity coefficients is presented.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.45-48
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• 1996

The most research on facility layout problems ignored the actual shape of building where the activities(departments) are to be arranged. They also ignored the aisles between departments inside the building. In this paper, we present a genetic algorithm that searches a very good facility layout with horizontal aisles for two different cases with respect to the department shape. From the extensive experiments, the proposed genetic algorithm generated better layouts than the ones obtained by applying Tam's algorithm. It showed about 10% improvement of performance. We found out the best combination of genetic operators through the experiments.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.107-109
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• 2006

직렬-병렬 시스템의 중복설계 문제는 비용, 무게등을 고려한 다양한 제약 조건하에서 시스템의 신뢰도를 최대화하기 위해 하부시스템의 중복설계의 최적 개수를 결정해주는 것이다. 일반적으로 이 문제는 다루기 힘든 NP-hard 문제로 분류된다. 특히 Coit과 Smith가 제시한 문제에 대해 유전자 해법 등의 다양한 발견적 해법(Heuristic methods)들이 개발되었으나 이 문제에 대한 전역 최적해(Globally optimal solution)는 아직 알려져 있지 않다. 따라서 본 논문에서는 기존의 해법들이 다루었던 이 문제에 대한 전역 최적해를 구하여 그 성능을 비교하고자 한다.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.3
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• pp.289-295
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• 2000

The triangulation problem is to make triangles using the given points on the space. The Minimum Weight Triangulation(MWT) is the problem of finding a set of triangles with the minimum weight among possible set of the triangles. In this paper, a modified genetic algorithm(GA) based on an existing genetic algorithm and multispace smoothing technique is proposed. Through the computational results, we can find the tendency that the proposed GA finds good solutions though it needs longer time than the existing GA does as the problem size increases.