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Handling The Multiplicity of Solutions in a Moea Based Pda-Theseus Framework for Multi-Criteria Sorting

Covantes E., Fernández E., Navarro J.

Foundations of Computing and Decision Sciences

2016

Vol. 41, No. 4

213--235

This paper proposes the combination of the THESEUS multi-criteria sorting method with an evolutionary optimization-based preference-disaggregation analysis. The main features of the combined method are studied by performing an extensive computer experiment that explores many models of preferences and sizes of problems as well as different degrees of decision-maker involvement. As a result of the experiment, the effectiveness of the combined framework and the importance of the decision-maker’s involvement are characterized.

A genetic algorithm for deriving final ranking from a fuzzy outranking relation

Leyva-Lopez J. C., Fernandez-Gonzales E.

Foundations of Computing and Decision Sciences

1999

Vol. 24, No. 1

33-47

The multiple criteria aggregation methods allow us to construct a prescription (or solution) from a set of alternatives based on the preferences of a Decision Maker or a group of Decision Makers. In some approaches, the prescription is immediately deduced from the aggregation preferences process. When the aggregation model of preferences is based on the outranking approach, a special treatment is required, but some non rational violations of the explicit global model of preferences could happen. In this paper a new genetic algorithm which allows to exploit a known fuzzy outranking relation is introduced with the purpose of constructing a prescription for ranking problems. The performance of our algorithm is evaluated on a set of test problems. Computational results show that the genetic a1gorithrn-based heuristic is capable of producing high-quality prescriptions.