Produção Cientifica

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https://dspace.mackenzie.br/handle/10899/33872

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Navegando Produção Cientifica por Autor "Abackerli A.J."

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  • Artigo
    A new FlowSort-based method to deal with information imperfections in sorting decision-making problems
    Pelissari R.; Oliveira M.C.; Ben Amor S.; Abackerli A.J. (2019)
    © 2019 Elsevier B.V.Most real-life decision-making problems can be modeled as sorting problems. As a result, a number of multi-criteria methods have been developed to deal with these types of problems. One of these methods is the FlowSort, which is a multi-criteria sorting method based on the PROMETHEE methodology whose parameters and required input data should be defined precisely, by quantitative and crisp values. This requirement is often difficult to meet, since imperfect data such as interval data, linguistic variables, stochastic and incomplete data are more natural to use in real-life problems. FlowSort-based methods, such as the Fuzzy-Flowsort method, were designed to deal with some, but not all, imperfect data types. In addition, none of the FlowSort-based methods is able to deal with criteria weights elicitation, even though this has been one of the main issues and difficulties in the multi-criteria decision-making field. In this context, our aim is to propose a new method for sorting decision-making problems capable of dealing with multiple imperfect data (interval, stochastic data and linguistic variables) and with criteria weight elicitation. We apply the SMAA method (Stochastic Multicriteria Acceptability Analysis) to the Fuzzy-FlowSort method. A numerical application is presented to illustrate the applicability of the proposed method. Then, two SMAA-FFS comparisons with existing FlowSort-based methods to deal with imperfect data are performed. For each of them, the results and some discussions are presented, and the conclusions point to a consistent model, independent of the imperfect data types used.
  • Artigo
    Techniques to model uncertain input data of multi-criteria decision-making problems: a literature review
    Pelissari R.; Oliveira M.C.; Abackerli A.J.; Ben-Amor S.; Assumpcao M.R.P. (2021)
    © 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.