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Against Artificial Complexification: Crisp vs. Fuzzy Information in the TOPSIS Method

David A. Pelta, María T. Lamata, José L. Verdegay, Carlos Cruz, Ana Salas
2021 Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)   unpublished

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The question of whether the use of crisp or fuzzy input information in the TOPSIS method produces a different ranking is explored. Using a basic representation of fuzziness through triangular fuzzy numbers, a set of randomly generated fuzzy and crisp multicriteria decision problems are solved. Then, the corresponding rankings are compared and variations in the top alternative are studied. The results show that changes in the top alternative are minor. This situation, coupled with the fact that
more » ... he "true" ranking is unknown and that more complex models of "fuzzy" information require a huge amount of precise information from the decision maker side, raise the discussion of whether in this specific context a "complexification" of the input data makes sense.
doi:10.2991/asum.k.210827.046 fatcat:4zziwdrw6baclfjvi4kpb5egg4
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David A. Pelta, María T. Lamata, José L. Verdegay, Carlos Cruz, Ana Salas. "Against Artificial Complexification: Crisp vs. Fuzzy Information in the TOPSIS Method." Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP) (2021)