Journal
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS
Volume 39, Issue 3, Pages 545-554Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCA.2009.2014555
Keywords
Imprecise/incomplete/partial information; Multiattribute utility theory (MAUT)/multiattribute value theory; (MAVT); multicriteria decision analysis; ordinal information; simulation
Funding
- Portuguese Foundation for Science and Technology (FCT/FEDER) [POCI/EGE/58371/2004]
- Fundação para a Ciência e a Tecnologia [POCI/EGE/58371/2004] Funding Source: FCT
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In the context of additive multiattribute aggregation, we address problems with ordinal information, i.e., considering a ranking of the weights (the scaling coefficients). Several rules for ranking alternatives in these situations have been proposed and compared, such as the rank-order-centroid weight, minimum value, central value, and maximum regret rules. This paper compares these rules, together with two rules that had never been studied (quasi-dominance and quasi-optimality) that use a tolerance parameter to extend the concepts of dominance and optimality. Another contribution of this paper is the study of the behavior of these rules in the context of selecting a subset of the most promising alternatives. This study intends to provide guidelines about which rules to choose and how to use them (e.g., how many alternatives to retain and what tolerance to use), considering the contradictory goals of keeping a low number of alternatives yet not excluding the best one. The comparisons are grounded on Monte Carlo simulations.
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