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Sodenkamp, Mariya: Models, methods and applications of group multiple-criteria decision analysis in complex and uncertain systems. 2013
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A soft multi-criteria decision analysis model with application to the European Union enlargement
Abstract
Introduction
Literature review
Mathematical model and procedure
The pilot study
Conclusions
Acknowledgements
Appendix: The European Commission enlargement strategy and progress reports
References
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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
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