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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 9059

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Special Issue Editors

Dr. Mijanur Rahaman Seikh

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Guest Editor

Department of Mathematics, Kazi Nazrul University, Asansol 713340, India
Interests: fuzzy sets; decision making; game theory; soft computing

Prof. Dr. Oscar Castillo

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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
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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

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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.

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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

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Published Papers (10 papers)

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Research

49 pages, 9100 KiB

Open AccessArticle

A Sustainable Supply Chain Model with Variable Production Rate and Remanufacturing for Imperfect Production Inventory System under Learning in Fuzzy Environment

by Basim S. O. Alsaedi

Mathematics 2024, 12(18), 2836; https://doi.org/10.3390/math12182836 - 12 Sep 2024

Viewed by 240

Abstract

In the present paper, a sustainable supply chain model is investigated with a variable production rate and remanufacturing for the production of defective items under the effect of learning fuzzy theory, where the lower and upper variations in fuzzy demand rate are affected by learning parameters and backorders are also allowed. Our proposed model reveals a springy manufacturing inventory organization that makes various types of items, and imperfect items can be created through the method of manufacturing things in a fuzzy environment. When the screening process is completed, defective items are remanufactured immediately, and a limited financial plan and space limitations are assumed concerning the product assembly. We minimized the total fuzzy inventory cost with different distributions (beta, triangular, double triangular, uniform, and χ² (chi−square)) concerning the production rate, lot size, and backorder under learning in a fuzzy environment where the costs of screening, manufacturing, carrying, carbon emissions, backorders, and remanufacturing are included. The Kuhn–Tucker optimization technique is applied to solve non-linear equations that are based on some distributions. Numerical examples, sensitivity analysis, managerial insights and observations, limitations, future work, and applications are provided for the validation of our proposed model, and the industrial scope of this proposed work is included. Full article

(This article belongs to the Special Issue Advances and Application of Fuzzy Sets, Decision Making and Soft Computing)

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16 pages, 644 KiB

Open AccessArticle

Large-Scale Group Decision-Making Method with Public Participation and Its Application in Urban Management

by Xiuhong Niu, Yongming Song and Zhongwen Xu

Mathematics 2024, 12(16), 2528; https://doi.org/10.3390/math12162528 - 15 Aug 2024

Viewed by 594

Abstract

Civic participation is of great significance to urban management decision-making. In order to facilitate citizens to participate in city management decision-making, this paper proposes a large-scale group decision-making (LSGDM) method based on multi-granular probabilistic linguistic preference relations (MG-PLPRs). First, each decision maker selects a language terms set from the multi-granularity language terms set to represent individual preference relations, and the MG-PLPRs are obtained by statistical calculation to represent sub-group’s preferences information. Then, an optimization model based on the expected consistency of PLPR and consensus measure of groups is established for achieving consensus-reaching processes, which can ensure satisfactory individual consistency and group consensus. Finally, the validity and applicability of the proposed method is verified by a case of a city “shared garden” site selection with the participation of citizens. Full article

(This article belongs to the Special Issue Advances and Application of Fuzzy Sets, Decision Making and Soft Computing)

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41 pages, 7710 KiB

Open AccessArticle

A Sustainable Inventory Model with Advertisement Effort for Imperfect Quality Items under Learning in Fuzzy Monsoon Demand

by Osama Abdulaziz Alamri, Navneet Kumar Lamba, Mahesh Kumar Jayaswal and Mandeep Mittal

Mathematics 2024, 12(15), 2432; https://doi.org/10.3390/math12152432 - 5 Aug 2024

Viewed by 536

Abstract

In this paper, we proposed a sustainable inventory model with a learning effect for imperfect quality items under different kinds of fuzzy environments like crisp, general fuzzy, cloudy fuzzy, and monsoon fuzzy. We divided the mathematical model into three parts under the learning effect according to the real-time fuzzy components (crisp, cloudy, and monsoon environments) of the demand rate of the items. We minimized the total inventory cost with respect to cycle length in each environment under the proposed assumptions. The non-linear optimization technique is applied for the algorithm and the solution method to find the decision variable. Finally, we compared the total inventory cost under different fuzzy environments and our finding is that the fuzzy monsoon environment is a more effective fuzzy environment than crisp and cloudy fuzzy environments. We have presented a numerical example for the validation of the proposed model and have shown the impact of the inventory input parameters on the cycle length and total inventory fuzzy cost. The managerial insights and future scope of this proposed study have been shown in the sensitivity analysis and conclusion. The limitations, application, future extension and scope, and social implementation have been shown in this research study. Full article

(This article belongs to the Special Issue Advances and Application of Fuzzy Sets, Decision Making and Soft Computing)

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30 pages, 10891 KiB

Open AccessArticle

A Fuzzy-Based Approach for Flexible Modeling and Management of Freshwater Fish Farming

by Ahmed M. Gadallah, Sameh A. Elsayed, Shaymaa Mousa and Hesham A. Hefny

Mathematics 2024, 12(13), 2146; https://doi.org/10.3390/math12132146 - 8 Jul 2024

Viewed by 812

Abstract

Most populated developing countries having water resources, like Egypt, are interested in aquaculture since it supplies around 30% of the cheap protein consumed by customers. Increasing the production of aquaculture, specifically fish farming, in such countries represents an essential need. One candidate water resource for freshwater fish farming in Egypt is the Nile River (1530 km long). Yet, this represents a challenging task due to the existing variations in its water quality (WQ) parameters, such as dissolved oxygen, acidity, and temperature, at different sites. Climate change and pollution negatively affect many water quality parameters. This work provides a fuzzy-based approach for modeling WQ requirements for a set of fish types and evaluates the suitability of a water site for farming them. Thus, it greatly helps managing and planning fish farming in a set of water sites. It benefits from the flexibility of fuzzy logic to model the farming requirements of each fish type. Consequently, it evaluates and clusters the water sites with respect to their degrees of suitability for farming various fish types. The illustrative case study considers 27 freshwater sites spread along the Nile River and 17 freshwater fish types. The result incorporates a set of suitable clusters and a set of unsuitable ones for farming each fish type. It greatly helps managing and planning fish farming, to maximize the overall productivity and prevent probable catastrophic damage. In addition, it shows how to enhance each unsuitable site. We believe that eliminating the causes of pollution in the polluted freshwater sites along a water source could cause a significant boom in the cultivation of multiple freshwater fish types. Full article

(This article belongs to the Special Issue Advances and Application of Fuzzy Sets, Decision Making and Soft Computing)

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23 pages, 2040 KiB

Open AccessArticle

Leveraging Blockchain for Maritime Port Supply Chain Management through Multicriteria Decision Making

by Claudia Durán, Amir Karbassi Yazdi, Iván Derpich and Yong Tan

Mathematics 2024, 12(10), 1511; https://doi.org/10.3390/math12101511 - 13 May 2024

Viewed by 1352

Abstract

This research investigates the optimal integration of Blockchain Technology (BT) in Supply Chain Management (SCM) within Chile’s maritime ports. Utilizing fuzzy Logarithmic Methodology of Additive Weights (LMAW) and Double Normalization-based Multiple Aggregation Methods (DNMA), the study systematically identifies, prioritizes, and ranks key factors influencing BT adoption in SCM. The study’s findings highlight crucial factors like enhanced transaction security, good supply chain practices, and risk management. Furthermore, it ranks the application of ports as prime candidates for BT integration. The research contributes theoretically by developing a hybrid model combining MCDA methods, and practically by guiding the strategic application of BT in the maritime logistics sector, aligning with the principles of Industry 5.0. This paper presents a novel approach that explores the utilization of BT in maritime supply chain management, incorporating MCDA in a vague environment. The research gap of this study lies in defining new contexts in both theoretical and practical literature reviews for extending the use of BT in SCM in the ports of Chile, according to Industry 5.0, to increase the efficiency and effectiveness of all aspects of operations in these places. The contribution of this research is applying hybrid MCDA methods in an uncertain environment to assist decision-makers (DMs) in better implementing BT in SCM in Chilean ports, according to Industry 5.0. Full article

(This article belongs to the Special Issue Advances and Application of Fuzzy Sets, Decision Making and Soft Computing)

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43 pages, 6191 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 804

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 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)

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28 pages, 875 KiB

Open AccessArticle

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 795

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.

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

Open AccessArticle

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 3 | Viewed by 862

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 987

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 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

I_{1}

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(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

Open AccessArticle

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 - 2 Sep 2023

Cited by 2 | Viewed by 990

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 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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