VIP Analysis

Additive aggregation with variable interdependent parameters: the VIP analysis software. We consider the aggregation of multicriteria performances by means of an additive value function under imprecise information. The problem addressed here is the way an analysis may be conducted when the decision makers are not able to (or do not wish to) fix precise values for the importance parameters. These parameters can be seen as interdependent variables that may take several values subject to constraints. Firstly, we briefly classify some existing approaches to deal with this problem. We argue that they complement each other, each one having its merits and shortcomings. Then, we present a new decision support software—VIP analysis—which incorporates approaches belonging to different classes. It proposes a methodology of analysis based on the progressive reduction of the number of alternatives, introducing a concept of tolerance that lets the decision makers use some of the approaches in a more flexible manner.

References in zbMATH (referenced in 17 articles , 1 standard article )

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  1. Harju, Mikko; Liesiö, Juuso; Virtanen, Kai: Spatial multi-attribute decision analysis: axiomatic foundations and incomplete preference information (2019)
  2. Vetschera, Rudolf: Deriving rankings from incomplete preference information: a comparison of different approaches (2017)
  3. de Almeida, Adiel Teixeira; de Almeida, Jonatas Araujo; Costa, Ana Paula Cabral Seixas; de Almeida-Filho, Adiel Teixeira: A new method for elicitation of criteria weights in additive models: flexible and interactive tradeoff (2016)
  4. Gouveia, M. C.; Dias, L. C.; Antunes, C. H.; Mota, M. A.; Duarte, E. M.; Tenreiro, E. M.: An application of value-based DEA to identify the best practices in primary health care (2016)
  5. Greco, Salvatore (ed.); Ehrgott, Matthias (ed.); Figueira, José Rui (ed.): Multiple criteria decision analysis. State of the art surveys. In 2 volumes (2016)
  6. Kadziński, Miłosz; Michalski, Marcin: Scoring procedures for multiple criteria decision aiding with robust and stochastic ordinal regression (2016)
  7. Graf, Christoph; Six, Magdalena: The effect of information on the quality of decisions (2014)
  8. Punkka, Antti; Salo, Ahti: Scale dependence and ranking intervals in additive value models under incomplete preference information (2014)
  9. Vetschera, Rudolf; Sarabando, Paula; Dias, Luis: Levels of incomplete information in group decision models -- a comprehensive simulation study (2014)
  10. Graf, Christoph; Vetschera, Rudolf; Zhang, Yingchao: Parameters of social preference functions: measurement and external validity (2013)
  11. Wang, Jiamin: Robust optimization analysis for multiple attribute decision making problems with imprecise information (2012)
  12. Sarabando, Paula; Dias, Luís C.: Simple procedures of choice in multicriteria problems without precise information about the alternatives’ values (2010)
  13. Alencar, Luciana Hazin; de Almeida, Adiel Teixeira: Multicriteria decision group model for the selection of suppliers (2008)
  14. Greco, Salvatore; Mousseau, Vincent; Słowiński, Roman: Ordinal regression revisited: Multiple criteria ranking using a set of additive value functions (2008)
  15. Liesiö, Juuso; Mild, Pekka; Salo, Ahti: Preference programming for robust portfolio modeling and project selection (2007)
  16. Dias, Luis C.; Clímaco, João N.: Dealing with imprecise information in group multicriteria decisions: a methodology and a GDSS architecture (2005)
  17. Dias, L. C.; Clímaco, J. N.: Additive aggregation with variable interdependent parameters: the VIP analysis software (2000)