Integration Of Clustering Concept And Fuzzy Topsis For Maintenance Policy Decision Making Model

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dc.contributor.authorSIEW HONG, DING
dc.date.accessioned2016-04-19T07:35:36Z
dc.date.available2016-04-19T07:35:36Z
dc.date.issued2015-01
dc.description.abstractMaintenance policy decision making has become a great challenge in view of the fact that decision making process is highly fuzzy and complicated given that it involves multiple subjective evaluation perspectives. Thus, this study aims to develop a decision making model that is capable to determine the optimal maintenance policy for multiple systems with similar failure mechanisms. Particularly, the development of maintenance policy decision making (MPDM) model is separated into three stages starting from grouping multiple systems into virtual cells according to the similarity of failure mechanisms. Mean while, a set of procedures are proposed in second stage of the MPDM model to obtain required information for analysis purposes in third stage. The Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) will be integrated in the third stage of the MPDM model to provide preference order of the maintenance policies for particular virtual cell. In the end, the maintenance policy with highest ranking will be pointed as the optimal maintenance policy for respective virtual cell. The robustness of the MPDM model had been verified and validated through six case studies in a circuit board manufacturing plant. The results obtained from case studies had proven the robustness of the MPDM model in determining optimal maintenance policy for each virtual cell. Overall, the MPDM model has been proven capable in providing systematic way of maintenance policy decision making for multiple systems.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/1930
dc.subjectIntegration Of Clustering Concept And Fuzzy Topsisen_US
dc.subjectFor Maintenance Policy Decision Making Modelen_US
dc.titleIntegration Of Clustering Concept And Fuzzy Topsis For Maintenance Policy Decision Making Modelen_US
dc.typeThesisen_US
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