Abstract
In this paper we introduce an approach for solving multiattribute decision-making problems in which there are several decision-makers who individually and independently elicit their preferences. The preferences of each decision-maker are imprecise and represented by an imprecise additive multi-attribute utility function. We allow for incomplete information on the component utility functions and weights assessment, which leads to classes of utility functions and weight intervals, respectively. On the basis of this information, we introduce an approach for calculating the decision-maker group preferences using trapezoidal fuzzy numbers. The method consists of assigning trapezoidal fuzzy numbers to weights and component utilities and then, using an additive utility function to perform the evaluation process. The alternatives are then ranked by the trapezoidal fuzzy numbers representing them and the distances to some preset targets, i.e. the crisp maximum and minimum.
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Mateos, A., Jiménez, A. (2009). A Trapezoidal Fuzzy Numbers-Based Approach for Aggregating Group Preferences and Ranking Decision Alternatives in MCDM. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, JK., Sevaux, M. (eds) Evolutionary Multi-Criterion Optimization. EMO 2009. Lecture Notes in Computer Science, vol 5467. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01020-0_30
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DOI: https://doi.org/10.1007/978-3-642-01020-0_30
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