UTA Plus

This method may be used to solve problems of choice or of multi criteria classification on set A of actions. It constructs a utility function from a preorder defined by the user on a subset A’ of the reference actions. The procedure, based on a principle of ordinal regression, consist in solving a small linear program. The program provides piecewise linear functions of marginal utility that are as compatible as possible with the given preorder. The user can modify the marginal utility functions within limits given by a sensitivity analysis of the ordinal regression problem. There is a friendly user interface in which to make these changes. The utility function given by the user is then used to define a preorder on the set A of actions.


References in zbMATH (referenced in 122 articles )

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  1. Hu, Jian; Bansal, Manish; Mehrotra, Sanjay: Robust decision making using a general utility set (2018)
  2. Ishizaka, Alessio; Siraj, Sajid: Are multi-criteria decision-making tools useful? An experimental comparative study of three methods (2018)
  3. Karakaya, G.; Köksalan, M.; Ahipaşaoğlu, S.D.: Interactive algorithms for a broad underlying family of preference functions (2018)
  4. Sobrie, Olivier; Gillis, Nicolas; Mousseau, Vincent; Pirlot, Marc: UTA-poly and UTA-splines: additive value functions with polynomial marginals (2018)
  5. Zheng, Jun; Lienert, Judit: Stakeholder interviews with two MAVT preference elicitation philosophies in a swiss water infrastructure decision: aggregation using SWING-weighting and disaggregation using UTA$^\mathrmGMS$ (2018)
  6. Argyris, Nikolaos; French, Simon: Nuclear emergency decision support: a behavioural OR perspective (2017)
  7. Belahcene, Khaled; Labreuche, Christophe; Maudet, Nicolas; Mousseau, Vincent; Ouerdane, Wassila: Explaining robust additive utility models by sequences of preference swaps (2017)
  8. Benabbou, Nawal; Perny, Patrice; Viappiani, Paolo: Incremental elicitation of Choquet capacities for multicriteria choice, ranking and sorting problems (2017)
  9. Ciomek, Krzysztof; Kadziński, Miłosz; Tervonen, Tommi: Heuristics for selecting pair-wise elicitation questions in multiple criteria choice problems (2017)
  10. Ghaderi, Mohammad; Ruiz, Francisco; Agell, Núria: A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding (2017)
  11. Vetschera, Rudolf: Deriving rankings from incomplete preference information: a comparison of different approaches (2017)
  12. Covantes, Edgar; Fernández, Eduardo; Navarro, Jorge: Handling the multiplicity of solutions in a MOEA based PDA-THESEUS framework for multi-criteria sorting (2016)
  13. 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)
  14. Greco, Salvatore (ed.); Ehrgott, Matthias (ed.); Figueira, José Rui (ed.): Multiple criteria decision analysis. State of the art surveys. In 2 volumes (2016)
  15. Nishizaki, Ichiro; Hayashida, Tomohiro; Ohmi, Masakazu: Multiattribute decision analysis using strict preference relations (2016)
  16. Pigozzi, Gabriella; Tsoukiàs, Alexis; Viappiani, Paolo: Preferences in artificial intelligence (2016)
  17. Angilella, Silvia; Corrente, Salvatore; Greco, Salvatore: Stochastic multiobjective acceptability analysis for the Choquet integral preference model and the scale construction problem (2015)
  18. Angilella, Silvia; Mazzù, Sebastiano: The financing of innovative SMEs: a multicriteria credit rating model (2015)
  19. Beccacece, Francesca; Borgonovo, Emanuele; Buzzard, Greg; Cillo, Alessandra; Zionts, Stanley: Elicitation of multiattribute value functions through high dimensional model representations: monotonicity and interactions (2015)
  20. Deparis, Stéphane; Mousseau, Vincent; Öztürk, Meltem; Huron, Caroline: The effect of bi-criteria conflict on matching-elicited preferences (2015)

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