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ConclusionsThreekindsofevaluationswereconsideredinthispaper:deterministic,stochastico...
Conclusions
Three kinds of evaluations were considered in this paper: deterministic, stochastic or fuzzy with relation to each attribute. The mixed-data dominances (MDk) were defined and suggested to model the preferences with relation to each attribute. The global preferences on the set of alternatives are approximated by means of mixed-data multi-attribute dominance rules (MMDR) for a reduced number of attributes. The rules
represent a preference model by the DM which can be applied to a new set of potential alternatives. This methodology was applied to solve the ranking project problem, from the best to the worst. For doing this, we adopted the DRSA of Greco et al. (1999). 展开
Three kinds of evaluations were considered in this paper: deterministic, stochastic or fuzzy with relation to each attribute. The mixed-data dominances (MDk) were defined and suggested to model the preferences with relation to each attribute. The global preferences on the set of alternatives are approximated by means of mixed-data multi-attribute dominance rules (MMDR) for a reduced number of attributes. The rules
represent a preference model by the DM which can be applied to a new set of potential alternatives. This methodology was applied to solve the ranking project problem, from the best to the worst. For doing this, we adopted the DRSA of Greco et al. (1999). 展开
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