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2003•한국경영과학회 학술대회논문집Requires access

[Poster Session 포스터 발표]셀 구성을 위한 그룹유전자 알고리듬의 변형들에 대한 연구

이종윤, 박양병

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Abstract

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 efficacy. GGA's replacement heuristic used similarity coefficients is presented.

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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 efficacy. GGA's replacement heuristic used similarity coefficients is presented.

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Available abstract

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 efficacy. GGA's replacement heuristic used similarity coefficients is presented.

Key concepts: Cellular manufacturing, Group technology, Session (web analytics), Cell formation, Exploit, Similarity (geometry), Heuristic, Computer science

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[Poster Session 포스터 발표]셀 구성을 위한 그룹유전자 알고리듬의 변형들에 대한 연구 — Research Paper | ScholarLens