Korean Management Science Review (경영과학)

The Korean Operations Research and Management Science Society (한국경영과학회)
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Genetic algorithms for optimization : a case study of machine-part group formation problems

기계-부품군 형성문제의 사례를 통한 유전 알고리즘의 최적화 문제에의 응용

  • 한용호 (부산외국어대학교, 경영정보학과) ;
  • 류광렬 (부산대학교 컴퓨터공학과)
  • Published : 1995.08.01
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Abstract

This paper solves different machine-part group formation (MPGF) problems using genetic algorithms to demonstrate that it can be a new robust alternative to the conventional heuristic approaches for optimization problems. We first give an overview of genetic algorithms: Its principle, various considerations required for its implementation, and the method for setting up parameter values are explained. Then, we describe the MPGF problem which are critical to the successful operation of cellular manufacturing or flexible manufacturing systems. We concentrate on three models of the MPGF problems whose forms of the objective function and/or constraints are quite different from each other. Finally, numerical examples of each of the models descibed above are solved by using genetic algorithms. The result shows that the solutions derived by genetic algorithms are comparable to those obtained through problem-specific heuristic methods.

Keywords

References

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