33 0

Full metadata record

DC FieldValueLanguage
dc.contributor.author이기천-
dc.date.accessioned2019-12-10T15:27:18Z-
dc.date.available2019-12-10T15:27:18Z-
dc.date.issued2018-12-
dc.identifier.citationCOMPUTERS & OPERATIONS RESEARCH, v. 100, page. 77-88en_US
dc.identifier.issn0305-0548-
dc.identifier.issn1873-765X-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0305054818301837?via%3Dihub-
dc.identifier.urihttp://repository.hanyang.ac.kr/handle/20.500.11754/120977-
dc.description.abstractCell 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.sponsorshipThis 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.isoen_USen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.subjectCell formationen_US
dc.subjectClusteringen_US
dc.subjectModularityen_US
dc.subjectPerformance measureen_US
dc.subjectOrdinal dataen_US
dc.titleTwo-mode modularity clustering of parts and activities for cell formation problemsen_US
dc.typeArticleen_US
dc.relation.volume100-
dc.identifier.doi10.1016/j.cor.2018.06.018-
dc.relation.page77-88-
dc.relation.journalCOMPUTERS & OPERATIONS RESEARCH-
dc.contributor.googleauthorKong, Taewoon-
dc.contributor.googleauthorSeong, Kyungje-
dc.contributor.googleauthorSong, Kiburm-
dc.contributor.googleauthorLee, Kichun-
dc.relation.code2018009695-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF INDUSTRIAL ENGINEERING-
dc.identifier.pidskylee-
dc.identifier.orcidhttps://orcid.org/0000-0002-5184-7151-
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > INDUSTRIAL ENGINEERING(산업공학과) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE