Multi-Criteria Decision Aid Methods in Fuzzy Decision Problems – Part III

Dear Colleagues, 

After the success of two parts of the Special Issue entitled “Multi-Criteria Decision Aid Methods in Fuzzy Decision Problems” (https://www.mdpi.com/journal/symmetry/special_issues/Multi_Criteria_Decision_fuzzy_decision, https://www.mdpi.com/journal/symmetry/special_issues/Multi-Criteria_fuzzy_decision_II), we are glad to announce the Special Issue “Multi-Criteria Decision Aid Methods in Fuzzy Decision Problems—Part III”.

In both previous editions of the Special Issue in 2018–2019 and 2019–2021, we cooperated with excellent scholars/scientific groups and published important high-level articles (almost 70 in total) which have seen frequent citation, according to the data from the Web of Science (currently over 460 citations in total) and Scopus (currently over 510 citations in total).

Our goal is to increase knowledge and develop the decision sciences in the field of uncertain and imprecise information. The research can make a great contribution to management, sustainability, energy, marketing, e-commerce, etc. Thus, we will continue the Special Issue in this third part of “Multi-Criteria Decision Aid Methods in Fuzzy Decision Problems”. 

The notion of symmetry is of particular importance in Multi-Criteria Decision Aid (MCDA). Symmetry, asymmetry, and antisymmetry are basic characteristics of binary relations used when modelling the decision-maker’s preferences. Moreover, the notion of symmetry has appeared in many articles about the fuzzy set theory which is employed in MCDA methods.

Fuzzy set theory makes it possible to capture uncertainty, imprecision, inaccurate definition of a decision problem, and, as a consequence, fuzzing the problem. Fuzzing may include input data representing alternatives, weights of criteria, or the decision-maker’s preferences. The first type of fuzzing takes place when the decision-maker is not able to precisely evaluate the weights of criteria or the consequences of individual alternatives. The fuzzy MCDA methods deal with this kind of fuzzing. Next, the fuzzing of the decision-maker’s preferences takes place when they cannot determine to what degree (if at all) one alternative is better than another with regard to a defined criterion. The decision-maker’s fuzzy preferences are used in some outranking MCDA methods. Furthermore, the fuzzy outranking methods deal with both types of fuzzing. 

This Special Issue invites contributions dealing with:

• The preparation and development of MCDA methods capable of capturing the fuzziness (uncertainty, imprecision, and inaccurate definition) of the decision problem;
• The application of MCDA methods in fuzzy decision problems in the area of management, logistics, sustainability, marketing, finance, electronic markets, social media, and others.

Survey and theoretical articles, as well as application papers, are welcome. 

Dr. Paweł Ziemba
Prof. Dr. Samarjit Kar
Guest Editors

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