Sodenkamp, Mariya: Models, methods and applications of group multiple-criteria decision analysis in complex and uncertain systems. 2013
Content
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- A fuzzy opportunity and threat aggregation approach in multicriteria decision analysis
- Abstract
- 1 Introduction
- 2 Review of relevant literature
- 3 The proposed methodology
- 3.1 Identify DMs and their voting power
- 3.2 Identify a finite set of alternatives
- 3.3 Identify relevant factors/sub-factors and group them into clusters
- 3.4 Establish a hierarchy/network of factor dependencies and define importance weights of its elements using the ANP
- 3.5 Develop a scoring system for subjective criteria, assign fuzzy scores to alternatives on each criterion and aggregate the group estimates
- 3.6 Normalize all estimates to obtain identical units of measurement
- 3.7 Defuzzify and integrate the weights and the scores
- 3.8 Aggregate crisp normalized factor estimates for each alternative
- 3.9 Calculate the entropy for all alternatives as a measure of judgment uncertainty
- 3.10 Identify the ideal alternative and define the position of the decision alternatives with respect to the ideal one
- 3.11 Classify and rank the alternatives and their groups using numerical information and diagrams, taking into consideration the level of uncertainty of their fuzzy characteristic
- 4 Pipeline route evaluation case study
- 4.1 Identify DMs and their voting power
- 4.2 Identify a finite set of alternatives
- 4.3 Identify relevant factors/sub-factors and group them into clusters
- 4.4 Establish a hierarchy/network of factor dependencies and define importance weights of its elements using the ANP
- 4.5 Develop scoring system for subjective criteria and assign fuzzy scores to alternatives on each criterion
- 4.6 Normalize all estimates to obtain identical units of measurement
- 4.7 Defuzzify and integrate the weights and the scores
- 4.8 Aggregate crisp normalized factor estimates for each alternative
- 4.9 Calculate the entropy for all alternatives as a measure of judgment uncertainty
- 4.10 Identify the ideal alternative and define the position of the decision alternatives with respect to the ideal one
- 4.11 Classify and rank the alternatives and their groups using numerical information and diagrams, taking into consideration the level of uncertainty of their fuzzy characteristic
- 5 Conclusions and future research directions
- Acknowledgments
- References