Abstract
A new robustness analysis framework is proposed where robustness of a solution in a decision aiding process is measured as the distance from that solution to an expected outcome, chosen by the decision-aiding analyst. The framework is explained by the application of the TODIM method of multicriteria decision aiding to the problem of predicting rental ranges for properties in a Chilean city. Therefore, the robustness concern concentrates on changes in criteria weights as well as in trade-off rates, as they are defined in the method. Two main contributions are introduced: a local robustness measure, defined in terms of a distance among rankings; and a global robustness measure, as an adaptation of the minimax-regret rule to select a global robust solution, i.e. a ranking produced by TODIM.
Additional information
Notes on contributors
Javier Pereira
Javier PEREIRA. Professor of Multicriteria Decision Analysis at Diego Portales University, Santiago de Chile; Doctor of Sciences of Management and Diploma of Advanced Studies at LAMSADE, University Paris Dauphine, France. Main research interest includes multicriteria decision aid.
Luiz Flavio Autran Monteiro Gomes
Luiz Flavio Autran Monteiro GOMES. Professor of Management in Ibmec, Rio de Janeiro, Brazil; Alexander von Humboldt Visiting Researcher, Universitaet Stuttgart, Germany; Doctor of Philosophy, University of California at Berkeley, U.S.A.; Master of Science, Michigan State University; Civil Engineer, Pontifical Catholic University of Rio de Janeiro, Brazil. Main research interest includes multicriteria decision aid.
Fernando Paredes
Fernando PAREDES. Professor of Operations Research, at the School of Industrial Engineering, Diego Portales University, Chile; Doctor in Sciences, Systems Engineering and Computing, Federal University of Rio de Janeiro, Brazil; Master in Mathematics, Santa María University, Valparaíso, Chile. Main research interests include mathematical programming, multicriteria decision aid.