| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 747.95 KB | Adobe PDF |
Authors
Advisor(s)
Abstract(s)
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.
Description
Keywords
Imprecise/incomplete/partial information Multiattribute utility theory (MAUT)/multiattribute value theory (MAVT) multicriteria decision analysis ordinal information simulation