Date of Award
September 2017
Degree Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Engineering
First Advisor
Dr. Nidal H. Abu Zahra
Committee Members
Hamid K. Seifoddini, Matthew E. Petering, Wilkistar A. Otieno, Xiaohang Yue
Keywords
Critical Factors of Global Manufacturing, Facility Location Problem, Similarity Coefficient Method
Abstract
This research introduces a similarity coefficient-based clustering algorithm to determine the best location for a petrochemical manufacturing facility. The most global petrochemical critical attributes have been selected from relevant literature about manufacturing activities. These critical attributes have been quantified by real world numbers from the World Bank database and have been employed in the proposed model of the research. The model of the research uses the selected critical attributes data and clusters a hundred countries in similar groups according to their attractiveness level to the petrochemical facility location.
The outcomes of the developed model are classifications that show the potential country for locating a petrochemical facility. Moreover, all countries have been ranked first according to their high potential cluster and within each cluster. These rankings also help to distinguish the candidate countries assigned to the same cluster.
Recommended Citation
Alarjani, Ali Saeed, "Introduction of Similarity Coefficient-based Clustering Algorithms to Global Petrochemical Facility Location" (2017). Theses and Dissertations. 1569.
https://dc.uwm.edu/etd/1569