Sponsor
Portland State University. Department of Engineering and Technology Management
First Advisor
Dundar F. Kocaoglu
Date of Publication
Winter 3-21-2016
Document Type
Dissertation
Degree Name
Doctor of Philosophy (Ph.D.) in Technology Management
Department
Engineering and Technology Management
Language
English
Subjects
Group decision making -- Mathematical models, Decision making -- Mathematical models, Multiple criteria decision making
DOI
10.15760/etd.2695
Physical Description
1 online resource (x, 161 pages)
Abstract
The objective of this research is to establish consistency thresholds linked to alpha (α) levels for HDM’s (Hierarchical Decision Model) judgment quantification method. Measuring consistency in order to control it is a crucial and inseparable part of any AHP/HDM experiment. The researchers on the subject recommend establishing thresholds that are statistically based on hypothesis testing, and are linked to the number of decision variables and (α) level. Such thresholds provide the means with which to evaluate the soundness and validity of an AHP/HDM decision. The linkage of thresholds to (α) levels allows the decision makers to set an appropriate inconsistency tolerance compatible with the situation at hand. The measurements of judgments are unreliable in the absence of an inconsistency measure that includes acceptable limits. All of this is essential to the credibility of the entire decision making process and hence is extremely useful for practitioners and researchers alike. This research includes distribution fitting for the inconsistencies. It is a valuable and interesting part of the research results and adds usefulness, practicality and insight. The superb fits obtained give confidence that all the statistical inferences based on the fitted distributions accurately reflect the HDM’s inconsistency measure.
Rights
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Persistent Identifier
http://archives.pdx.edu/ds/psu/16894
Recommended Citation
Abbas, Mustafa Sulaiman, "Consistency Analysis for Judgment Quantification in Hierarchical Decision Model" (2016). Dissertations and Theses. Paper 2699.
https://doi.org/10.15760/etd.2695