This dissertation presents the configuration and capacity management of manufacturing cells using cluster analysis. A heuristic based on cluster analysis is developed to solve cell formation in cellular manufacturing systems (CMS). The clustering heuristic is applied for cell formation considering processing requirement (CFOPR) as well as various manufacturing factors (CFVMF). The proposed clustering heuristic is developed by employing a new solving structure incorporating hierarchical and non-hierarchical clustering methods. A new similarity measure is constructed by modifying the Jarccard similarity and a new assignment algorithm is proposed by employing the new pairwise exchange method. In CFOPR, the clustering heuristic is modified by adding a feedback step and more exact allocation rules. Grouping efficacy is employed as a measure to evaluate solutions obtained from the heuristic. The clustering heuristic for CFOPR was evaluated on 23 test problems taken from the literature in order to compare with other approaches and produced the best solution in 18 out of 23 and the second best in the remaining problems. These solutions were obtained in a considerably short time and even the largest test problem was solved in around one and a half seconds. In CFVMF, the machine capacity was first ensured, and then manufacturing cells were configured to minimize intercellular movements. In order to ensure the machine capacity, the duplication of machines and the split of operations are allowed and operations are assigned into duplicated machines by the largest-first rule. The clustering heuristic for CFVMF proposes a new similarity measure incorporating processing requirement, material flow and machine workload and a new machine-part matrix representing material flow and processing time assigned to multiple identical machines. Also, setup time, which has not been clearly addressed in existing research, is discussed in the solving procedure. The clustering heuristic for CFVMF employs two evaluation measures such as the number of intercellular movements and grouping efficacy. In two test problems taken from the literature, the heuristic for CFVMF produced the same results, but the trade-off problem between the two evaluation measures is proposed to consider the goodness of grouping.

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Thesis (Ph. D.)--University of Cincinnati, 2006. / Includes bibliographical references (leaf 142-147). Also available online.

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Statistics and Biostatistics." Includes bibliographical references (p. 75-78).

Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 87-90). Also available in print.

Includes bibliographical references (p. 103-111).

Zugl.: München, Univ. der Bundeswehr, Diss., 2009

This research is based on a paradox. This paradox is the fact that, even if themain cluster theories define a bottom-up logic as the best way to develop dynamic clusters,Chinese clusters which use a top-down method supported by a global national plan seem tobe successful. The purpose of this study is to identify if it is possible for a business cluster to besuccessful while having a top-down logic heavily influenced by governmental institutions. To reach this objective, three angles are used in order to judge the quality ofinnovation techniques in these structures. These angles are classic cluster theories,innovation theories, and the understanding of companies as individualities being part of abigger structure. The study, has been conducted through a survey in Chinese businesscluster. The study resulted in the confirmation of the idea that a top-downlogic can also be associated to clusters of good quality. However, some doubts areremaining about the sustainability of this method.

With the advancement of Internet technology and the changes in the mode of communications, it is found that much first-hand news have been discussed in Internet forums well before they are reported in traditional mass media. Also, this communication channel provides an effective channel for illegal activities such as dissemination of copyrighted movies, threatening messages and online gambling etc. The law enforcement agencies are looking for solutions to monitor these discussion forums for possible criminal activities and download suspected postings as evidence for investigation. The volume of postings is huge, for 10 popular forums in Hong Kong; we found that there are 300,000 new messages every day. In this thesis, we propose an automatic system that tackles this problem. Our proposed system downloads postings from selected discussion forums continuously and employs data mining techniques to identify hot topics and cluster authors into different groups using word based user profiles. Using these data, we try to locate some useful trends and detect crime from the data, the result is discussed afterward with include advantages and limitations of different approaches and at the end, there is a conclusion of the way to solve those problems and provide future direction of this research. / published_or_final_version / Computer Science / Master / Master of Philosophy

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