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Mathematical Problems in Engineering
Volume 2018, Article ID 2157937, 10 pages
Research Article

Robustness Analysis of an Outranking Model Parameters’ Elicitation Method in the Presence of Noisy Examples

1Postgraduate and Research Division, National Mexican Institute of Technology/Madero Institute of Technology, 89440 Ciudad Madero, TAMPS, Mexico
2Faculty of Civil Engineering, Autonomous University of Sinaloa, 80040 Culiacan, SIN, Mexico
3National Lab of Information Technology, Autonomous University of Juarez City, 32310 Ciudad Juárez, CHIH, Mexico

Correspondence should be addressed to Nelson Rangel-Valdez; xm.tycanoc@avlegnarn

Received 20 June 2017; Revised 19 November 2017; Accepted 10 December 2017; Published 3 January 2018

Academic Editor: Danielle Morais

Copyright © 2018 Nelson Rangel-Valdez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


One of the main concerns in Multicriteria Decision Aid (MCDA) is robustness analysis. Some of the most important approaches to model decision maker preferences are based on fuzzy outranking models whose parameters (e.g., weights and veto thresholds) must be elicited. The so-called preference-disaggregation analysis (PDA) has been successfully carried out by means of metaheuristics, but this kind of works lacks a robustness analysis. Based on the above, the present research studies the robustness of a PDA metaheuristic method to estimate model parameters of an outranking-based relational system of preferences. The method is considered robust if the solutions obtained in the presence of noise can maintain the same performance in predicting preference judgments in a new reference set. The research shows experimental evidence that the PDA method keeps the same performance in situations with up to 10% of noise level, making it robust.