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Advances and Application of Fuzzy Sets, Decision Making and Soft...
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Advances and Application of Fuzzy Sets, Decision Making and Soft Computing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 3352

Special Issue Editors

Dr. Mijanur Rahaman Seikh

E-Mail Website
Guest Editor
Department of Mathematics, Kazi Nazrul University, Asansol 713340, India
Interests: fuzzy sets; decision making; game theory; soft computing
Prof. Dr. Oscar Castillo

E-Mail Website
Guest Editor
Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22414, Mexico
Interests: type-2 fuzzy logic; fuzzy control; neuro-fuzzy; genetic-fuzzy hybrid approaches
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of fuzzy set theory is still in its infancy, but it has undergone numerous diversifications. Academics and practitioners have granted considerable attention to fuzzy sets theory, which has been applied in a range of fields. Furthermore, fuzzy information is capable of representing uncertain data, and its several extensions can be applied to a vast range of multi-criteria evaluation and decision-making problems. Many domains, including the sciences, engineering, economics, and management, have also incorporated fuzzy systems and fuzzy set extensions into their decision-making processes.

Soft computing, on the other hand, is a relatively novel paradigm that offers tools and techniques for handling incomplete and/or uncertain information regarding real-world problems. The theory of uncertainty depends heavily on fuzzy sets. For complicated linguistic and numerical modelling applications, fuzzy sets and soft computing offer a wide array of theoretical and practical tools.

This Special Issue aims to publish original or review articles that focus on the current advances in, methodologies, and applications of fuzzy set extensions. Special attention will be paid to research works that address practical problems with regard to the application of decision-making methods in fuzzy modeling, fuzzy information fusion, multi-criteria decision making, and soft computing. Contributions that attend to the following topics are particularly welcome: :

Dr. Mijanur Rahaman Seikh
Prof. Dr. Oscar Castillo
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 set theory
  • extensions and generalizations of fuzzy sets
  • aggregation operators
  • decision making
  • fuzzy game theory and its applications
  • multi-criteria decision analysis
  • group decision making analysis
  • large-scale decision-making
  • uncertainty modeling
  • soft computing
  • applications of soft computing techniques

Published Papers (5 papers)

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Research

43 pages, 1179 KiB  
Article
A Sustainable Supply Chain Model with Low Carbon Emissions for Deteriorating Imperfect-Quality Items under Learning Fuzzy Theory
by Basim S. O. Alsaedi and Marwan H. Ahelali
Mathematics 2024, 12(8), 1237; https://doi.org/10.3390/math12081237 - 19 Apr 2024
Viewed by 250
Abstract
In this paper, we develop a two-level supply chain model with low carbon emissions for defective deteriorating items under learning in fuzzy environment by using the double inspection process. Carbon emissions are a major issue for the environment and human life when they [...] Read more.
In this paper, we develop a two-level supply chain model with low carbon emissions for defective deteriorating items under learning in fuzzy environment by using the double inspection process. Carbon emissions are a major issue for the environment and human life when they come from many sources like different kinds of factories, firms, and industries. The burning of diesel and petrol during the supply of items through transportation is also responsible for carbon emissions. When any company, firm, or industry supplies their items through a supply chain by using of transportation in the regular mode, then a lot of carbon units are emitted from the burning of petrol and diesel, etc., which affects the supply chain. Carbon emissions can be controlled by using different kinds of policies issued by the government of a country, and lots of companies have implemented these policies to control carbon emissions. When a seller delivers a demanded lot size to the buyer, as per demand, and the lot size has some defective items, as per consideration, the demand rate is uncertain in nature. The buyer inspects the received whole lot and divides it into two categories of defective and no defective deteriorating items, as well as immediately selling at different price. The fuzzy concept nullifies the uncertain nature of the demand rate. This paper covers two models, assuming two conditions of quality screening under learning in fuzzy environment: (i) the buyer shows the quality screening and (ii) the quality inspection becomes the seller’s responsibility. The carbon footprint from the transporting and warehousing the deteriorating items is also assumed. The aim of this study is to minimize the whole inventory cost for supply chains with respect to lot size and the number of orders per production cycle. Jointly optimizing the delivery lot size and number of orders per production cycle will minimize the whole fuzzy inventory cost for the supply chain and also reduce the carbon emissions. We take two numerical approaches with authentic data (from the literature reviews) for the justification of the proposed model 1 and model 2. Sensitivity observations, managerial insights, applications of these proposed models, and future scope are also included in this paper, which is more beneficial for firms, the industrial sector, and especially for online markets. The impact of the most effective parameters, like learning effect, fuzzy parameter, carbon emissions parameter, and inventory cost are shown in this study and had a positive effect on the total inventory cost for the supply chain. Full article
(This article belongs to the Special Issue Advances and Application of Fuzzy Sets, Decision Making and Soft Computing)
28 pages, 875 KiB  
Article
Fuzzy Divergence Measure Based on Technique for Order of Preference by Similarity to Ideal Solution Method for Staff Performance Appraisal
by Mohamad Shahiir Saidin, Lai Soon Lee, Hsin-Vonn Seow and Stefan Pickl
Mathematics 2024, 12(5), 714; https://doi.org/10.3390/math12050714 - 28 Feb 2024
Viewed by 466
Abstract
Fuzzy set theory has extensively employed various divergence measure methods to quantify distinctions between two elements. The primary objective of this study is to introduce a generalized divergence measure integrated into the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [...] Read more.
Fuzzy set theory has extensively employed various divergence measure methods to quantify distinctions between two elements. The primary objective of this study is to introduce a generalized divergence measure integrated into the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. Given the inherent uncertainty and ambiguity in multi-criteria decision-making (MCDM) scenarios, the concept of the fuzzy α-cut is leveraged. This allows experts to establish a broader spectrum of rankings, accommodating fluctuations in their confidence levels. To produce consistent criteria weights with the existence of outliers, the fuzzy Method based on the Removal Effects of Criteria (MEREC) is employed. To showcase the viability and effectiveness of the proposed approach, a quantitative illustration is provided through a staff performance review. In this context, the findings are compared with other MCDM methodologies, considering correlation coefficients and CPU time. The results demonstrate that the proposed technique aligns with current distance measure approaches, with all correlation coefficient values exceeding 0.9. Notably, the proposed method also boasts the shortest CPU time when compared to alternative divergence measure methodologies. As a result, it becomes evident that the proposed technique yields more sensible and practical results compared to its counterparts in this category. Full article
(This article belongs to the Special Issue Advances and Application of Fuzzy Sets, Decision Making and Soft Computing)
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20 pages, 375 KiB  
Article
Choquet-like Integrals with Multi-Neighborhood Approximation Numbers for Novel Covering Granular Reduction Methods
by Jingqian Wang, Songtao Shao and Xiaohong Zhang
Mathematics 2023, 11(22), 4650; https://doi.org/10.3390/math11224650 - 15 Nov 2023
Cited by 2 | Viewed by 600
Abstract
Covering granular reduction is an important issue in multi-covering information systems. The main methods to solve this problem are set operators. How to solve this problem by quantitative analysis is an interesting topic. Furthermore, as a type of nonlinear fuzzy aggregation function (which [...] Read more.
Covering granular reduction is an important issue in multi-covering information systems. The main methods to solve this problem are set operators. How to solve this problem by quantitative analysis is an interesting topic. Furthermore, as a type of nonlinear fuzzy aggregation function (which is a quantitative tool), Choquet-like integrals with fuzzy measures are widely used in many files. However, the corresponding fuzzy measures in Choquet-like integrals are given by man, not by data. In this work, we present two types of multi-neighborhood approximation numbers in multi-covering information systems, which are used to establish Choquet-like integrals. Furthermore, they are applied to deal with the problem of granular reduction in multi-covering information systems. First, the notions of lower and upper multi-neighborhood approximation numbers are presented in a multi-covering information system, as well as their properties. Furthermore, some conditions under which multi-covering information systems induce the same lower and upper multi-neighborhood approximation numbers are presented. Second, two covering granular reduction methods based on multi-neighborhood approximation numbers are presented in multi-covering information systems. Third, multi-neighborhood approximation numbers are used to establish Choquet-like integrals, which are applied in covering granular reduction. Finally, these methods are compared with existing methods through experiments, which are used to demonstrate the effectiveness and benefits of our methods. Full article
(This article belongs to the Special Issue Advances and Application of Fuzzy Sets, Decision Making and Soft Computing)
22 pages, 2790 KiB  
Article
An Integrated Group Decision-Making Framework for the Evaluation of Artificial Intelligence Cloud Platforms Based on Fractional Fuzzy Sets
by Saleem Abdullah, Saifullah and Alaa O. Almagrabi
Mathematics 2023, 11(21), 4428; https://doi.org/10.3390/math11214428 - 25 Oct 2023
Viewed by 665
Abstract
Due to the rapid development of machine learning and artificial intelligence (AI), the analysis of AI cloud platforms is now a key area of research. Assessing the wide range of frameworks available and choosing the ideal AI cloud providers that may accommodate the [...] Read more.
Due to the rapid development of machine learning and artificial intelligence (AI), the analysis of AI cloud platforms is now a key area of research. Assessing the wide range of frameworks available and choosing the ideal AI cloud providers that may accommodate the demands and resources of a company is mandatory. There are several options, all having their own benefits and limitations. The evaluation of artificial intelligence cloud platforms is a multiple criteria group decision-making (MCGDM) process. This article establishes a collection of Einstein geometric aggregation operators (AoPs) and a novel Fractional Fuzzy VIKOR and Fractional Fuzzy Extended TOPSIS based on the entropy weight of criteria in fractional fuzzy sets (FFSs) for this scenario. The FFSs provide an evaluation circumstance containing more information, which makes the final decision-making results more accurate. Finally, this framework is then implemented in a computational case study for the evaluation of artificial intelligence cloud platforms and comparison of this model with other existing approaches, such as the extended GRA approach, to check the consistency and accuracy of the proposed technique. The most optimal artificial intelligence cloud platform is I1 Full article
(This article belongs to the Special Issue Advances and Application of Fuzzy Sets, Decision Making and Soft Computing)
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18 pages, 791 KiB  
Article
Fixed/Preassigned-Time Stochastic Synchronization of Complex-Valued Fuzzy Neural Networks with Time Delay
by Mairemunisa Abudusaimaiti, Abuduwali Abudukeremu and Amina Sabir
Mathematics 2023, 11(17), 3769; https://doi.org/10.3390/math11173769 - 02 Sep 2023
Cited by 2 | Viewed by 749
Abstract
Instead of the separation approach, this paper mainly centers on studying the fixed/preassigned-time (FXT/PAT) synchronization of a type of complex-valued stochastic fuzzy cellular neural networks (CVSFCNNs) with time delay based on the direct method. Firstly, some basic properties of the sign function in [...] Read more.
Instead of the separation approach, this paper mainly centers on studying the fixed/preassigned-time (FXT/PAT) synchronization of a type of complex-valued stochastic fuzzy cellular neural networks (CVSFCNNs) with time delay based on the direct method. Firstly, some basic properties of the sign function in complex fields and some generalized FXT/PAT stability lemmas for nonlinear stochastic differential equations are introduced. Secondly, by designing two delay-dependent complex-valued controllers with/without a sign function, sufficient conditions for CVSFCNNs to achieve FXT/PAT synchronization are obtained. Finally, the feasibility of the theoretical results is verified through a numerical example. Full article
(This article belongs to the Special Issue Advances and Application of Fuzzy Sets, Decision Making and Soft Computing)
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