Numerous methods have been developed for multiple criteria analysis of a given set of discrete alternatives. Only a small fraction of them are suitable for users with limited modeling skills and for problems with a large number of alternatives and criteria, optionally organized into hierarchical structures. This paper presents new methods effectively supporting analysis of such problems, as well as using a very simple, but yet effective, way of interactive specification of preferences suitable also for a large number of diversified users without experience in analytical methods. The approach is based on the equitable aggregations concept and the developed pairwise outperformance aggregations considering inter-alternative relations. The methods have been implemented for, and applied to analyzing real-life problems, in particular multiple-criteria analysis of future energy technologies, which involves large numbers of both alternatives and criteria.

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Pairwise Outperformance Aggregations in Multiple-Criteria Analysis of Alternatives

  • Janusz Granat,
  • Marek Makowski

摘要

Numerous methods have been developed for multiple criteria analysis of a given set of discrete alternatives. Only a small fraction of them are suitable for users with limited modeling skills and for problems with a large number of alternatives and criteria, optionally organized into hierarchical structures. This paper presents new methods effectively supporting analysis of such problems, as well as using a very simple, but yet effective, way of interactive specification of preferences suitable also for a large number of diversified users without experience in analytical methods. The approach is based on the equitable aggregations concept and the developed pairwise outperformance aggregations considering inter-alternative relations. The methods have been implemented for, and applied to analyzing real-life problems, in particular multiple-criteria analysis of future energy technologies, which involves large numbers of both alternatives and criteria.