Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Integrating statistical correlation with discrete multi-criteria decision-making

Integrating statistical correlation with discrete multi-criteria decision-making This paper analyses two hypotheses that considers a correlation between the number of alternatives and the number of criteria considered in a multiple criteria decision-making (MCDM) problem with the minimum percentage change required in the lowest criterion weight to change the outcome of a method. Two MCDM methods are considered, the analytical hierarchy process (AHP) and the preference ranking organisation method for enrichment of evaluations II (PROMETHEE II) were applied to the same sets of criteria weights and performance measures. More than two thousand randomly generated sets of criteria weights and performance measures are considered. The minimum percentage change in the lowest criterion weight required to change the outcome of a method is calculated. Pearson's r parametric test is used to test the hypotheses. Results from parametric test were statistically significant and shows a weak negative correlation for Hypothesis 1 and weak positive correlation for Hypothesis 2. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Information and Decision Sciences Inderscience Publishers

Integrating statistical correlation with discrete multi-criteria decision-making

Loading next page...
 
/lp/inderscience-publishers/integrating-statistical-correlation-with-discrete-multi-criteria-5xthPE0Tob
Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1756-7017
eISSN
1756-7025
DOI
10.1504/IJIDS.2021.113599
Publisher site
See Article on Publisher Site

Abstract

This paper analyses two hypotheses that considers a correlation between the number of alternatives and the number of criteria considered in a multiple criteria decision-making (MCDM) problem with the minimum percentage change required in the lowest criterion weight to change the outcome of a method. Two MCDM methods are considered, the analytical hierarchy process (AHP) and the preference ranking organisation method for enrichment of evaluations II (PROMETHEE II) were applied to the same sets of criteria weights and performance measures. More than two thousand randomly generated sets of criteria weights and performance measures are considered. The minimum percentage change in the lowest criterion weight required to change the outcome of a method is calculated. Pearson's r parametric test is used to test the hypotheses. Results from parametric test were statistically significant and shows a weak negative correlation for Hypothesis 1 and weak positive correlation for Hypothesis 2.

Journal

International Journal of Information and Decision SciencesInderscience Publishers

Published: Jan 1, 2021

There are no references for this article.