Outranking Methods

Outranking methods (Roy 1985) were developed to address some difficulties experienced with the MAUT approach in dealing with practical problems. Bouyssou (2001) notes that outranking methods do not require establishing trade-offs between criteria in order to derive overall preferences and that they are mostly non-compensatory. This implies for instance that a very weak performance on an important criterion (say, health effects) cannot be compensated by better performances on a number of less important criteria, as it could be in the case for MAUT methods.

Outranking methods may involve the use of a more general criterion model, called pseudo-criterion, which is characterized by two thresholds describing the concepts of indifference and strong preference. These thresholds are related in some cases to the uncertainty inherent in the evaluation of certain criteria. The analysis of options in outranking methods entails pairwise comparison of options on each criterion, and subsequently building an overall preference relation (also called outranking relation) aggregating these partial preferences. The underlying principle is 'democratic majority, without strong minority'. Accordingly, an option a outranks option b, or in other words a is at least as good as b, if a majority (or more important set) of criteria supports this assertion and if the opposition of the other criteria (their number or their importance) is 'not too strong' (Bouyssou 2001, pp. 249-250). The outranking relation can be further exploited to derive the best option(s) and issue a recommendation.

Some of the outranking methods, such as ELECTRE I-III (Roy and Bouyssou 1993) and PROMETHEE (Brans et al. 1984), also require assigning weights to criteria. However, for such methods weights represent the intrinsic importance of the evaluation criteria, instead of value trade-offs, as in the case of MAUT. Some outranking methods such as MELCHIOR (Leclercq 1984) or ELECTRE IV (Roy 1996) can also cope with situations when criteria weights cannot be assessed.

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