On generating utility functions in stochastic multicriteria acceptability analysis

DOI10.1016/j.ejor.2019.04.031zbMath1431.91150OpenAlexW2941152894WikidataQ127984154 ScholiaQ127984154MaRDI QIDQ1999400

Rudolf Vetschera, Luís C. Dias

Publication date: 26 June 2019

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10316/92615

zbMATH Keywords

simulationmultiple criteria analysisutility functiondecision analysisstochastic multicriteria acceptability analysis (SMAA)

Mathematics Subject Classification ID

Decision theory (91B06) Utility theory (91B16)

Related Items (5)

Compromise solutions for stochastic multicriteria acceptability analysis with uncertain preferences and nonmonotonic criteria ⋮ A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples ⋮ Stochastic multi-attribute acceptability analysis with numerous alternatives ⋮ Time-consistent portfolio optimization ⋮ Choice-based preference disaggregation concerning vehicle technologies

Uses Software

• UTA Plus

Cites Work

• A survey on stochastic multicriteria acceptability analysis methods
• Scoring procedures for multiple criteria decision aiding with robust and stochastic ordinal regression
• Selection of a representative value function in robust multiple criteria ranking and choice
• Implementing stochastic multicriteria acceptability analysis
• Assessing a set of additive utility functions for multicriteria decision- making, the UTA method
• Deriving rankings from incomplete preference information: a comparison of different approaches
• Ordinal criteria in stochastic multicriteria acceptability analysis (SMAA)
• The shape of the utility or value function in stochastic multicriteria acceptability analysis
• Stochastic Multicriteria Acceptability Analysis (SMAA)
• SMAA-2: Stochastic Multicriteria Acceptability Analysis for Group Decision Making
• Determining the implementation order of a general plan by using a multicriteria method
• Decision Analysis with Incomplete Utility and Probability Information
• Robustness of Additive Value Function Methods in MCDM
• Group decision making with incomplete information: a dominance and quasi‐optimality volume‐based approach using Monte‐Carlo simulation

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