Techniques to model uncertain input data of multi-criteria decision-making problems: a literature review

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International Transactions in Operational Research
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Pelissari R.
Oliveira M.C.
Abackerli A.J.
Ben-Amor S.
Assumpcao M.R.P.
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© 2018 The Authors. International Transactions in Operational Research © 2018 International Federation of Operational Research SocietiesThere are a few studies in the literature regarding possible types of uncertainty in input data of multi-criteria decision making (MCDM) or multi-criteria decision analysis (MCDA) problems and the techniques employed to deal with each of them. Therefore, the aim of this study is to identify the different types of uncertainty that occur in input data of MCDM/MCDA problems and the most appropriate techniques to deal with each one of these uncertainty types. In this paper, a comprehensive literature review is presented in order to meet this objective. We selected and summarized 134 international journal articles. They were analyzed based on the type of data with uncertainty, the type of uncertainty, and the technique used to model it. We identified three distinct types of uncertainty in input data of MCDM/MCDA problems, namely (i) uncertainty due to ambiguity, (ii) uncertainty due to randomness, and (iii) uncertainty due to partial information. We identified a new generation of fuzzy approaches including Type-2, intuitionistic, and hesitant fuzzy sets (FSs), which are used to model these types of uncertainty alongside other approaches such as traditional FSs theory, probability theory, evidential reasoning theory, rough set theory, and grey numbers. Finally, a framework that indicates techniques used in different decision-making contexts under uncertainty is proposed.
Assuntos Scopus
imprecision , Multi Criteria Analysis , Partial information , Systematic literature review , uncertainty
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