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Advances in Fuzzy Decision Theory and Applications, 2nd Edition
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Advances in Fuzzy Decision Theory and Applications, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 8303

Special Issue Editors

Prof. Jun Ye

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Ningbo University, Ningbo 315211, China
Interests: fuzzy theory and applications; soft computing; decision making theory and method
Special Issues, Collections and Topics in MDPI journals
Dr. Yanhui Guo

E-Mail Website
Guest Editor
Department of Data Analytics, University of Illinois Springfield, Springfield, IL 62703, USA
Interests: image processing; medical image processing; pattern recognition; computer vision; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is a particular level of incompleteness and uncertainty in complicated decision-making problems. Thus, the fuzzy set proposed by Zadeh has been widely used in the field of decision making. Due to the need for better and detailed membership functions in real decision-making problems, classical fuzzy sets have been extended to type-2 fuzzy sets, hesitant fuzzy sets, multivalued fuzzy sets, cubic sets, intuitionistic fuzzy sets, etc. Each of them is attracting significant attention in decision making, with these fuzzy theories being used in various decision-making applications. In recent years, new progress and achievements have been made in various decision-making problems through the use of various fuzzy theories.

The focus of this Special Issue is the extension and applications of advanced fuzzy theory to solve various decision-making problems. Articles submitted to this Special Issue can also be concerned with various advanced fuzzy theories, fuzzy decision theories and methods, and applications in decision making. We invite researchers to contribute original research articles and review articles, which will stimulate continuous research on various fuzzy theories, fuzzy decision theories and methods, and applications to solve various decision-making problems.

Prof. Jun Ye
Dr. Yanhui Guo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fuzzy theory
  • fuzzy decision theory and method
  • decision making
  • multicriteria decision making
  • group decision making
  • engineering and scientific applications

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Related Special Issue

Published Papers (6 papers)

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Research

29 pages, 2136 KiB  
Article
A Possible Degree-Based D–S Evidence Theory Method for Ranking New Energy Vehicles Based on Online Customer Reviews and Probabilistic Linguistic Term Sets
by Yunfei Zhang and Gaili Xu
Mathematics 2025, 13(4), 583; https://doi.org/10.3390/math13040583 - 10 Feb 2025
Viewed by 444
Abstract
As people’s environment awareness increases and the “double carbon” policy is implemented, the new energy vehicle (NEV) becomes a popular form of transformation and more and more car manufacturers start to produce NEVs. Thus, how to choose an appropriate type of NEVs from [...] Read more.
As people’s environment awareness increases and the “double carbon” policy is implemented, the new energy vehicle (NEV) becomes a popular form of transformation and more and more car manufacturers start to produce NEVs. Thus, how to choose an appropriate type of NEVs from many brands is an interesting topic for customers, which can be regarded as a multiple-attribute decision-making (MADM) problem because customers often concern several different factors such as the price, endurance mileage, appearance and so on. This paper proposes a possible degree-based D–S evidence theory method for helping customers select a proper type of NEVs in the probabilistic linguistic environment. In order to derive decision information reflecting customer demands, online customer reviews (OCRs) are crawled from multiple websites and converted into five-granularity probabilistic linguistic term sets (PLTSs). Afterwards, by maximizing deviation and minimizing the information uncertainty, a bi-objective programming model is built to determine attribute weights. Furthermore, a possible degree-based D–S evidence theory method in the PLTS environment is proposed to rank alternatives in each website. For fusing these ranking results, a 0–1 programming model is set up by maximizing the consensus between the comprehensive ranking and individual ones in each website. At length, a case study of selecting a type of NEVs is provided to show the application and validity of the proposed method. Full article
(This article belongs to the Special Issue Advances in Fuzzy Decision Theory and Applications, 2nd Edition)
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32 pages, 1191 KiB  
Article
AI-Driven LOPCOW-AROMAN Framework and Topological Data Analysis Using Circular Intuitionistic Fuzzy Information: Healthcare Supply Chain Innovation
by Muhammad Riaz, Freeha Qamar, Sehrish Tariq and Kholood Alsager
Mathematics 2024, 12(22), 3593; https://doi.org/10.3390/math12223593 - 16 Nov 2024
Cited by 1 | Viewed by 1835
Abstract
Artificial intelligence (AI) stands out as a significant technological innovation, driving progress in diverse areas such as big data analysis, supply chain management, energy efficiency, sustainable development, etc. The present study investigates how AI could contribute to the sustainability of the healthcare supply [...] Read more.
Artificial intelligence (AI) stands out as a significant technological innovation, driving progress in diverse areas such as big data analysis, supply chain management, energy efficiency, sustainable development, etc. The present study investigates how AI could contribute to the sustainability of the healthcare supply chain (HSC) and managing medical needs. Medical organizations can boost the logistics of their tasks, reduce pharmaceutical trash, and strengthen revenue projections through the adoption of AI tools. This study aims to provide a structured evaluation of AI-driven solutions for enhancing healthcare supply chain robustness, especially under conditions of uncertainty and complex logistics demands. To determine the investment value of AI applications in HSC management, the current research adopted a revolutionary multi-criteria decision-making (MCDM) methodology tailored to the healthcare sector’s unique demands, including six critical factors. In light of these criteria, six highly technologically advanced AI-based solutions are examined. The implementation of a circular intuitionistic fuzzy set (CIFS) in the instance discussed provides a versatile and expressive way to describe vague and uncertain information. This study leverages the CIF topology to address data complexities and uncover the underlying structural features of a large dataset. At the outset, we adopted the LOPCOW approach, which includes logarithmic variation to assign weights to criteria, whereas the AROMAN method utilizes a powerful two-step normalization technique to rank alternatives, hence guaranteeing a trustworthy and accurate appraisal. A substantial degree of robustness was confirmed by the technique following a comparison of the operators as well as sensitivity testing. Full article
(This article belongs to the Special Issue Advances in Fuzzy Decision Theory and Applications, 2nd Edition)
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25 pages, 2889 KiB  
Article
The T-Spherical Fuzzy Einstein Interaction Operation Matrix Energy Decision-Making Approach: The Context of Vietnam Offshore Wind Energy Storage Technologies Assessment
by Nhat-Luong Nhieu
Mathematics 2024, 12(16), 2498; https://doi.org/10.3390/math12162498 - 13 Aug 2024
Viewed by 1052
Abstract
Fuzzy multi-criteria decision making (FMCDM) is a critical field that addresses the inherent uncertainty and imprecision in complex decision scenarios. This study tackles the significant challenge of evaluating energy storage technologies (ESTs) in Vietnam’s offshore wind sector, where traditional decision-making models often fall [...] Read more.
Fuzzy multi-criteria decision making (FMCDM) is a critical field that addresses the inherent uncertainty and imprecision in complex decision scenarios. This study tackles the significant challenge of evaluating energy storage technologies (ESTs) in Vietnam’s offshore wind sector, where traditional decision-making models often fall short due to their inability to handle fuzzy data and complex criteria interactions effectively. To overcome these limitations, the novel T-spherical fuzzy Einstein interaction operation matrix energy decision-making approach is introduced. This methodology integrates T-spherical fuzzy sets with matrix energy concepts and Einstein interaction operations, thereby eliminating the need for traditional aggregation processes and criteria weight determinations. My approach provides a structured evaluation of ESTs, highlighting that hydrogen storage, among others, demonstrates significant potential for high energy capacity and long-term storage. The findings not only underscore the robustness of this new method in managing the complexities of renewable energy assessment but also offer a comprehensive tool for selecting the most suitable ESTs to support Vietnam’s energy transition strategies. This study recognizes limitations related to data dependency, which could affect the generalizability of the results. Future research is suggested to expand the ESTs considered and integrate extensive real-world operational data, aiming to deepen the exploration of economic impacts and long-term viability of these technologies. This revised approach emphasizes both the challenge of evaluating ESTs under uncertain conditions and my innovative solution, enhancing the relevance and applicability of the findings. Full article
(This article belongs to the Special Issue Advances in Fuzzy Decision Theory and Applications, 2nd Edition)
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24 pages, 3736 KiB  
Article
Personnel Selection in a Coffee Shop Company Based on a Multi-Criteria Decision-Aiding and Artificial Intelligence Approach
by Diego Alonso Gastélum-Chavira, Denisse Ballardo-Cárdenas and Ernesto León-Castro
Mathematics 2024, 12(14), 2196; https://doi.org/10.3390/math12142196 - 12 Jul 2024
Cited by 1 | Viewed by 1527
Abstract
Human capital management is a strategic element for companies in a globalized world. Therefore, they must use strategies and methods to recruit and select personnel assertively to focus their training, strengthening, and business growth efforts. Personnel selection can be seen as a decision [...] Read more.
Human capital management is a strategic element for companies in a globalized world. Therefore, they must use strategies and methods to recruit and select personnel assertively to focus their training, strengthening, and business growth efforts. Personnel selection can be seen as a decision problem and can be addressed in a multi-criteria decision-making context. This work aims to present the selection process of a barista in a Mexican coffee shop. The baristas could be the face of the company to customers, and they could significantly impact their overall experience. The personnel selection process included eleven candidates and three criteria. This process was performed using the ELECTRE-III to model the preferences of a decision-maker and RP2-NSGA-II+H, a multi-objective evolutionary algorithm that exploits fuzzy outranking relations to derive multi-criteria rankings. The ordering obtained with the algorithm did not have any inconsistency concerning the integral preference model, and it allowed for the selection of a candidate to occupy the barista position. The results show the relevance of combining preference modeling with multi-criteria analysis methods for decision-making and artificial intelligence techniques. Full article
(This article belongs to the Special Issue Advances in Fuzzy Decision Theory and Applications, 2nd Edition)
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20 pages, 1340 KiB  
Article
A Consensus-Based 360 Degree Feedback Evaluation Method with Linguistic Distribution Assessments
by Chuanhao Fan, Jiaxin Wang, Yan Zhu and Hengjie Zhang
Mathematics 2024, 12(12), 1883; https://doi.org/10.3390/math12121883 - 17 Jun 2024
Cited by 1 | Viewed by 1391
Abstract
The 360 degree feedback evaluation method is a multidimensional, comprehensive assessment method. Evaluators may hesitate among multiple evaluation values and be simultaneously constrained by the biases and cognitive errors of the evaluators, evaluation results are prone to unfairness and conflicts. To overcome these [...] Read more.
The 360 degree feedback evaluation method is a multidimensional, comprehensive assessment method. Evaluators may hesitate among multiple evaluation values and be simultaneously constrained by the biases and cognitive errors of the evaluators, evaluation results are prone to unfairness and conflicts. To overcome these issues, this paper proposes a consensus-based 360 degree feedback evaluation method with linguistic distribution assessments. Firstly, evaluators provide evaluation information in the form of linguistic distribution. Secondly, utilizing an enhanced ordered weighted averaging (OWA) operator, the model aggregates multi-source evaluation information to handle biased evaluation information effectively. Subsequently, a consensus-reaching process is established to coordinate conflicting viewpoints among the evaluators, and a feedback adjustment mechanism is designed to guide evaluators in refining their evaluation information, facilitating the attainment of a unanimous evaluation outcome. Finally, the improved 360 degree feedback evaluation method was applied to the performance evaluation of the project leaders in company J, thereby validating the effectiveness and rationality of the method. Full article
(This article belongs to the Special Issue Advances in Fuzzy Decision Theory and Applications, 2nd Edition)
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18 pages, 4727 KiB  
Article
Protecting Infrastructure Networks: Solving the Stackelberg Game with Interval-Valued Intuitionistic Fuzzy Number Payoffs
by Yibo Dong, Jin Liu, Jiaqi Ren, Zhe Li and Weili Li
Mathematics 2023, 11(24), 4992; https://doi.org/10.3390/math11244992 - 18 Dec 2023
Cited by 1 | Viewed by 1155
Abstract
Critical infrastructure is essential for the stability and development of modern society, and a combination of complex network theory and game theory has become a new research direction in the field of infrastructure protection. However, existing studies do not consider the fuzziness and [...] Read more.
Critical infrastructure is essential for the stability and development of modern society, and a combination of complex network theory and game theory has become a new research direction in the field of infrastructure protection. However, existing studies do not consider the fuzziness and subjective factors of human judgment, leading to challenges when analyzing strategic interactions between decision makers. This paper employs interval-valued intuitionistic fuzzy numbers (IVIFN) to depict the uncertain payoffs in a Stackelberg game of infrastructure networks and then proposes an algorithm to solve it. First, we construct IVIFN payoffs by considering the different complex network metrics and subjective preferences of decision makers. Next, we propose a lexicographic algorithm to solve this game based on the concept of a strong Stackelberg equilibrium (SSE). Finally, we conduct experiments on target scale-free networks. Our results illustrate that in an SSE, for the defender in a weak position, it is better to defend nodes with high degrees. The experiments also indicate that taking fuzziness into account leads to higher SSE payoffs for the defender. Our work aims to solve a Stackelberg game with IVIFN payoffs and apply it to enhance the protection of infrastructure networks, thereby improving their overall security. Full article
(This article belongs to the Special Issue Advances in Fuzzy Decision Theory and Applications, 2nd Edition)
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