Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이기천 | - |
dc.date.accessioned | 2019-12-10T15:27:18Z | - |
dc.date.available | 2019-12-10T15:27:18Z | - |
dc.date.issued | 2018-12 | - |
dc.identifier.citation | COMPUTERS & OPERATIONS RESEARCH, v. 100, page. 77-88 | en_US |
dc.identifier.issn | 0305-0548 | - |
dc.identifier.issn | 1873-765X | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0305054818301837?via%3Dihub | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/120977 | - |
dc.description.abstract | Cell formation in cellular manufacturing is a critical step to improving productivity by grouping parts and machines. Numerous heuristic search algorithms and several performance measures have been used in finding an effective cell formation solution. It is still a challenging task to find a good cell formation that satisfies several performance measures. Clustering approaches aim to find good clusters of parts and machines according to their own similarity measures. We propose a two-mode modularity clustering method with new similarity measures for parts and machines using an ordinal part-machine matrix. The proposed method considers both incidence and transition among parts and machines and can find an optimal number of clusters. We demonstrate the effectiveness of the proposed method using cell formation problems in comparison with a few existing ones. The result shows that the proposed method produces good cell formation solutions in terms of several performance measures. In addition, we show a possible application area of the proposed method in process mining, using it to find interpretable clusters of processes and activities from real-life event log data. (C) 2018 Elsevier Ltd. All rights reserved. | en_US |
dc.description.sponsorship | This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017R1D1A1B03032673). This research was also supported by the grant (C0532192) funded by Small and Medium Business Administration (SMBA) and AURI in the Republic of Korea. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | en_US |
dc.subject | Cell formation | en_US |
dc.subject | Clustering | en_US |
dc.subject | Modularity | en_US |
dc.subject | Performance measure | en_US |
dc.subject | Ordinal data | en_US |
dc.title | Two-mode modularity clustering of parts and activities for cell formation problems | en_US |
dc.type | Article | en_US |
dc.relation.volume | 100 | - |
dc.identifier.doi | 10.1016/j.cor.2018.06.018 | - |
dc.relation.page | 77-88 | - |
dc.relation.journal | COMPUTERS & OPERATIONS RESEARCH | - |
dc.contributor.googleauthor | Kong, Taewoon | - |
dc.contributor.googleauthor | Seong, Kyungje | - |
dc.contributor.googleauthor | Song, Kiburm | - |
dc.contributor.googleauthor | Lee, Kichun | - |
dc.relation.code | 2018009695 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF INDUSTRIAL ENGINEERING | - |
dc.identifier.pid | skylee | - |
dc.identifier.orcid | https://orcid.org/0000-0002-5184-7151 | - |
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