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Using assignment examples to infer weights for ELECTRE TRI method: Some experimental results

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Date
2001
Dewey
Recherche opérationnelle
Sujet
Numerical experiments; Weight elicitation; Preference disaggregation; Sorting problematic
Journal issue
European Journal of Operational Research
Volume
130
Number
2
Publication date
04-2001
Article pages
263-275
Publisher
Elsevier
DOI
http://dx.doi.org/10.1016/S0377-2217(00)00041-2
URI
https://basepub.dauphine.fr/handle/123456789/3927
Collections
  • LAMSADE : Publications
Metadata
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Author
Mousseau, Vincent
Figueira, José
Naux, Jean-Philippe
Type
Article accepté pour publication ou publié
Abstract (EN)
Given a finite set of alternatives A, the sorting (or assignment) problem consists in the assignment of each alternative to one of the pre-defined categories. In this paper, we are interested in multiple criteria sorting problems and, more precisely, in the existing method ELECTRE TRI. This method requires the elicitation of preferential parameters (weights, thresholds, category limits,…) in order to construct a preference model which the decision maker (DM) accepts as a working hypothesis in the decision aid study. A direct elicitation of these parameters requiring a high cognitive effort from the DM (V. Mosseau, R. Slowinski, Journal of Global Optimization 12 (2) (1998) 174), proposed an interactive aggregation–disaggregation approach that infers ELECTRE TRI parameters indirectly from holistic information, i.e., assignment examples. In this approach, the determination of ELECTRE TRI parameters that best restore the assignment examples is formulated through a nonlinear optimization program. In this paper, we consider the subproblem of the determination of the weights only (the thresholds and category limits being fixed). This subproblem leads to solve a linear program (rather than nonlinear in the global inference model). Numerical experiments were conducted so as to check the behaviour of this disaggregation tool. Results showed that this tool is able to infer weights that restores in a stable way the assignment examples and that it is able to identify “inconsistencies” in the assignment examples.

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