Fuzzy Logic and Application in Multi-Criteria Decision-Making (MCDM) II

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Logic".

Deadline for manuscript submissions: 28 June 2024 | Viewed by 3980

Special Issue Editors


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Guest Editor
Military Academy, University of Defence in Belgarde, Pavla Jurisica Sturma 33, 11000 Belgrade, Serbia
Interests: multi-criteria decision-making; fuzzy set theory; neuro-fuzzy systems; rough set theory; decision support systems
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Guest Editor
Brčko District of Bosnia and Herzegovina, Department of Public Safety, Bulevara mira 1, 76100 Brčko, Bosnia and Herzegovina
Interests: quantitative economics; operational management; marketing and tourism
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue on "Fuzzy Logic and Application in Multi-Criteria Decision-Making (MCDM)". It turned out that most of the problems dealing with multi-criteria decision making (MCDM) have a certain degree of uncertainty. Therefore, most research related to MCDM combines decision-making models with an aspect that deals with uncertainties. To solve the problems of uncertainty in decision making, researchers have developed various approaches, such as fuzzy sets, rough sets, gray sets, etc. One of the first and perhaps most common is fuzzy mathematics. Fuzzy mathematics, with its basic concepts, such as fuzzy logic, fuzzy sets, fuzzy algorithms, fuzzy semantics, fuzzy languages, etc., was developed by Lotfi Zadeh. This excellently laid the foundations of fuzzy mathematics that have ensured that it finds great application in practice and develops in many directions.

This Special Issue should provide an overview of research dealing with practical problem solving using fuzzy MCDM. In addition, it should be a platform for gathering the knowledge of theorists and practitioners in fuzzy logic and multi-criteria decision making, which would improve both areas.

This Special Issue on “Fuzzy logic and Application in Multi-Criteria Decision-Making (MCDM) II” aims to incorporate recent developments in applied science. Topics include, but are not limited to, the following:

  • Application of classical fuzzy number in MCDM;
  • Application of interval fuzzy number in MCDM;
  • Application of Z-number in MCDM;
  • Application of intuitionistic fuzzy numbers in MCDM;
  • Application of hesitant fuzzy numbers in MCDM;
  • Application of q-rung orthopair fuzzy numbers in MCDM;
  • Application Pythagorean fuzzy numbers in MCDM;
  • Application of spherical fuzzy numbers in MCDM;
  • Application of different fuzzy aggregation operators in MCDM;
  • Neuro-fuzzy models for MCDM;
  • Fuzzy system for MCDM;
  • Combination fuzzy numbers in MCDM;
  • Other new fuzzy numbers in MCDM.

Dr. Darko Božanić
Dr. Adis Puška
Guest Editors

Manuscript Submission Information

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Published Papers (3 papers)

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Research

20 pages, 346 KiB  
Article
Linear Programming-Based Fuzzy Alternative Ranking Order Method Accounting for Two-Step Normalization for Comprehensive Evaluation of Digital Economy Development in Provincial Regions
by Huiling Xiang, Hafiz Muhammad Athar Farid and Muhammad Riaz
Axioms 2024, 13(2), 109; https://doi.org/10.3390/axioms13020109 - 5 Feb 2024
Viewed by 952
Abstract
As digital technologies continue to reshape economic landscapes, the comprehensive evaluation of digital economy (DE) development in provincial regions becomes a critical endeavor. This article proposes a novel approach, integrating the linear programming method, fuzzy logic, and the alternative ranking order method accounting [...] Read more.
As digital technologies continue to reshape economic landscapes, the comprehensive evaluation of digital economy (DE) development in provincial regions becomes a critical endeavor. This article proposes a novel approach, integrating the linear programming method, fuzzy logic, and the alternative ranking order method accounting for two-step normalization (AROMAN), to assess the multifaceted facets of DE growth. The primary contribution of the AROMAN is the coupling of vector and linear normalization techniques in order to produce accurate data structures that are subsequently utilized in calculations. The proposed methodology accommodates the inherent uncertainties and complexities associated with the evaluation process, offering a robust framework for decision-makers. The linear programming aspect optimizes the weightings assigned to different evaluation criteria, ensuring a dynamic and context-specific assessment. By incorporating fuzzy logic, the model captures the vagueness and imprecision inherent in qualitative assessments, providing a more realistic representation of the DE’s multifaceted nature. The AROMAN further refines the ranking process, considering the interdependencies among the criteria and enhancing the accuracy of the evaluation. In order to ascertain the efficacy of the suggested methodology, a case study is undertaken pertaining to provincial areas, showcasing its implementation in the evaluation and a comparison of DE progress in various geographical settings. The outcomes illustrate the capacity of the model to produce perceptive and implementable insights for policymakers, thereby enabling them to make well-informed decisions and implement focused interventions that promote the expansion of the DE. Moreover, managerial implications, theoretical limitations, and a comparative analysis are also given of the proposed method. Full article
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21 pages, 342 KiB  
Article
Fermatean Fuzzy Fairly Aggregation Operators with Multi-Criteria Decision-Making
by Muhammad Haris Mateen, Ibrahim Al-Dayel and Turki Alsuraiheed
Axioms 2023, 12(9), 865; https://doi.org/10.3390/axioms12090865 - 7 Sep 2023
Cited by 2 | Viewed by 755
Abstract
A Fermatean fuzzy set (FRFS) is the extension of a fuzzy set, an intuitionistic fuzzy set, and a Pythagorean fuzzy set, and is used in different fields. Unlike other fuzzy structures, the sum of cubes of membership grades in FRFSs approximates a unit [...] Read more.
A Fermatean fuzzy set (FRFS) is the extension of a fuzzy set, an intuitionistic fuzzy set, and a Pythagorean fuzzy set, and is used in different fields. Unlike other fuzzy structures, the sum of cubes of membership grades in FRFSs approximates a unit interval, increasing uncertainty. In this study, we intend to provide unique operational rules and aggregation operators (AOs) inside a Fermatean fuzzy environment. To develop a fair remedy for the membership degree and non-membership degree features of “Fermatean fuzzy numbers (FRFNs)”, our solution introduces new neutral or fair operating principles, which include the concept of proportional distribution. Based on the suggested operating principles, we provide the “Fermatean fuzzy fairly weighted average operator and the Fermatean fuzzy fairly ordered weighted averaging operator”. Our suggested AOs provide more generalized, reliable, and exact data than previous techniques. Combining the recommended AOs with multiple decision-makers and partial weight information under FRFSs, we also devised a technique for “multi-criteria decision-making”. To illustrate the application of our novel method, we provide an example of the algorithm’s effectiveness in addressing decision-making challenges. Full article
20 pages, 855 KiB  
Article
Integrating Fuzzy Rough Sets with LMAW and MABAC for Green Supplier Selection in Agribusiness
by Adis Puška, Anđelka Štilić, Miroslav Nedeljković, Darko Božanić and Sanjib Biswas
Axioms 2023, 12(8), 746; https://doi.org/10.3390/axioms12080746 - 29 Jul 2023
Cited by 4 | Viewed by 1744
Abstract
The evolving customer demands have significantly influenced the operational landscape of agricultural companies, including the transformation of their supply chains. As a response, many organizations are increasingly adopting green supply chain practices. This paper focuses on the initial step of selecting a green [...] Read more.
The evolving customer demands have significantly influenced the operational landscape of agricultural companies, including the transformation of their supply chains. As a response, many organizations are increasingly adopting green supply chain practices. This paper focuses on the initial step of selecting a green supplier, using the case study of the Semberka Company. The objective is to align the company with customer requirements and market trends. Expert decision making, grounded in linguistic values, was employed to facilitate the transformation of these values into fuzzy numbers and subsequently derive rough number boundaries. Ten economic-environmental criteria were identified, and six suppliers were evaluated against these criteria. The fuzzy rough LMAW (Logarithm Methodology of Additive Weights) method was employed to determine the criteria weights, with emphasis placed on the quality criterion. The fuzzy rough MABAC (Multi-Attributive Border Approximation Area Comparison) method was then utilized to rank the suppliers and identify the top performer. The validity of the results was established through validation techniques and sensitivity analysis. This research contributes a novel approach to green supplier selection, employing the powerful tool of fuzzy rough sets. The flexible nature of this approach suggests its potential application in future investigations. The limitation of this study is more complicated calculations for the decision maker. However, this approach is adapted to human thinking and minimizes ambiguity and uncertainty in decision making, and in future research, it is necessary to combine this approach with other methods of multi-criteria analysis. Full article
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