An Efficient Ranking Analysis In Multi-Criteria Decision Making

This study is conducted with the aims to develop a new ranking method for multi-criteria decision making problem with conflicting criteria. Such a problem has a set of Pareto solutions, where the act of improving a value of one solution will result in depreciating some of the others. Thus, in this t...

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Bibliographic Details
Main Author: Nor Izzati, Jaini
Format: Thesis
Language:English
Published: 2017
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/20186/1/An%20efficient%20ranking%20analysis%20in%20multi-criteria%20decision%20making.wm.pdf
http://umpir.ump.edu.my/id/eprint/20186/
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Institution: Universiti Malaysia Pahang
Language: English
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Summary:This study is conducted with the aims to develop a new ranking method for multi-criteria decision making problem with conflicting criteria. Such a problem has a set of Pareto solutions, where the act of improving a value of one solution will result in depreciating some of the others. Thus, in this type of problem, there is no unique solution. However, out of many available options, the Decision Maker eventually has to choose only one solution. With this problem as the motivation, the current study develops a compromise ranking algorithm, namely a trade-off ranking method. The trade-off ranking method able to give a trade-off solution with the least compromise compared to other choices as the best solution. The properties of the algorithm are studied in the thesis on several test cases. The proposed method is compared against several multi-criteria decision making methods with ranking based on the distance measure, which are the TOPSIS, relative distance and VIKOR. The sensitivity analysis and uncertainty test are carried out to examine the methods robustness. A critical criteria analysis is also done to test for the most critical criterion in a multi-criteria problem. The decision making method is considered further in a fuzzy environment problem where the fuzzy trade-off ranking is developed and compared against existing fuzzy decision making methods.