Similar Journals
|
Granular Computing
Number of Followers: 0  Hybrid journal (It can contain Open Access articles)ISSN (Print) 2364-4966 - ISSN (Online) 2364-4974 Published by Springer-Verlag  [2468 journals]
|
- Correction to: Weighted hesitant fuzzy soft set and its application in
group decision making-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
PubDate: 2023-11-01
-
- Correction: Multi-criteria decision-making based on novel fuzzy
generalized divergence and knowledge measures-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
PubDate: 2023-11-01
-
- Novel fuzzy knowledge and accuracy measures with its applications in
multi-criteria decision-making-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Entropy is regarded as a numerical representation of the degree of uncertainty concerning a random variable. However, the knowledge associated with any fuzzy set is quantified by a knowledge measure. A fuzzy Knowledge measure is viewed as dual of fuzzy information measure. The solution of a multi-criteria decision- making (MCDM) issue obtained using traditional fuzzy VIKOR approach is not closest to the best ideal solution. This is due to the fact that the defining function in traditional fuzzy VIKOR approach does not follow the axioms of a dissimilarity measure. Consequently, the existing idea of using dissimilarity measures in fuzzy VIKOR approach is controversial. The main aim of this study is to provide a novel fuzzy knowledge measure and modify the traditional fuzzy VIKOR approach. In the present study, a novel Knowledge measure in fuzzy settings is proposed. Its utility and validity are tested using numerical examples. We examine its benefits from a number of angles, including the computation of attribute weights, the computation of ambiguity, and the proper handling of the structured linguistic variables. In addition, from proposed knowledge measure, three new measures, namely, Accuracy measure, Knowledge measure and Information measure are deduced in fuzzy environment. We apply the proposed fuzzy accuracy measure in VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) approach in place of distance measure. We give the application of proposed approach in solving the multi criteria decision-making problems. We demonstrate the practical applications and validate the proposed approach using a numerical example, especially the selection of the best teacher. Furthermore, a flowchart is given to demonstrate the proposed approach. The findings of proposed approach are then compared to those of the existing approaches to more properly reflect the capability and effectiveness of the proposed approach. The scope for further research is also mentioned at the end of the paper.
PubDate: 2023-11-01
-
- A decision-making mechanism for multi-attribute group decision-making
using 2-tuple linguistic T-spherical fuzzy maximizing deviation method-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Hospital performance evaluation is vital for effective hospital management as it provides valuable information about a hospital’s condition and enables adaptable implementation based on various attributes. In this research, a multi-attribute group decision-making (MAGDM) method using a 2-tuple linguistic T-spherical fuzzy set (2TLT-SFS) is proposed in the context of the cognitive information presented in the hospital evaluation process. The T-spherical fuzzy set is the most advanced generalization of the q-rung orthopair fuzzy set (q-ROFS) which is capable of handling the uncertainty, fuzziness and ambiguity in terms of four parameters: positivity (yes), negativity (no), impartiality (abstain), and denial (non-acceptance). The 2-tuple linguistic terminology is used to measure the validity of ambiguous data. We propose the 2TLT-SF Hamy mean (2TLT-SFHM) operator, 2TLT-SF weighted Hamy mean (2TLT-SFWHM) operator, 2TLT-SF dual Hamy mean (2TLT-SFDHM) operator and 2TLT-SF weighted dual Hamy mean (2TLT-SFWDHM) operator by combining the 2TLT-SFS and HM operator. Then, based on the proposed maximizing deviation method, a new optimization model is built that is able to exploit expert preference to find the best objective weights among attributes. Next, we extend the TOPSIS (technique for establishing order preference by similarity to the ideal solution) method to the 2TLT-SF-TOPSIS version which not only accounts for human cognition’s inherent uncertainty but also allows experts a wider context to express their decision. Finally, we give a case study about the selection of key performance indicators for hospital performance evaluation to support our proposed method. The findings from parameter analysis and comparative analysis demonstrate the method’s efficacy and reliability. The outcomes demonstrate that our approach successfully handles the assessment and choice of key performance indicators for hospital performance evaluation and captures the relationship between any number of attributes.
PubDate: 2023-11-01
-
- Improved rough approximations based on variable J-containment
neighborhoods-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Classic generalized rough set model in neighborhood systems provides a more general framework for depicting approximations, while it may meet the non-reflexive situations. Some scholars put forward different neighborhoods, such as adhesion neighborhoods (briefly, \(P_{j}\) -neighborhoods), containment neighborhoods (briefly, \(C_{j}\) -neighborhoods), and \(E_{j}\) -neighborhoods. However, not all of them are reflexive. Moreover, the granularity of \(P_{j}\) -neighborhoods and \(C_{j}\) -neighborhoods are too fine, and that of \(E_{j}\) -neighborhoods too coarse. To solve the problem, we aim to design a novel construction approach of neighborhoods, called variable j-containment neighborhoods (briefly, \(V_{j}^{\beta }\) -neighborhoods), which satisfies the reflexivity and the granularity so flexible that the neighborhood space can adjust the granularity to meet the needs of problems. We generalize three kinds of rough approximations in \(V_{j}^{\beta }\) -neighborhood spaces and discuss their properties. What’s more, we analyze the topology structures relying on \(V_{j}^{\beta }\) -neighborhood spaces and compare our proposed approach with the existing approaches. By selecting the appropriate parameter \(\beta\) , our neighborhood system is more flexible in adjusting the granularity to fit problem requirements. And illustrative examples demonstrate the advantages of the proposed rough set model to attribute reduction in incomplete information systems.
PubDate: 2023-11-01
-
- An efficient spherical fuzzy MEREC–CoCoSo approach based on novel score
function and aggregation operators for group decision making-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The major objective of the current investigation is to build an integrated multiple criteria group decision-making (MCGDM) methodology based on combined compromise solution (CoCoSo) and spherical fuzzy set for determining the optimal solar power station. To begin with, an innovative spherical fuzzy score function is brought forward to strengthen the efficiency of the comparison for spherical fuzzy number (SFN). Secondly, several newly operational laws for SFN are defined and some novel aggregation operation based on them are propounded. The corresponding excellent properties of the novel operators are also explored at length. Further, the spherical fuzzy method on the removal effects of criteria (MEREC) technique is presented by the proposed score function to work out the importance of the criteria. Lastly, an MCGDM approach is propounded based on improved spherical fuzzy CoCoSo to obtain the ranking of the solar power station locations. The feasibility and practicability of the proposed SF-MEREC–CoCoSo method are investigated through the comparison study with the extant methods. The sensibility analysis is also executed to discuss the robustness and stability of the propounded methodology.
PubDate: 2023-11-01
-
- Intuitionistic fuzzy entropy-based knowledge and accuracy measure with its
applications in extended VIKOR approach for solving multi-criteria
decision-making-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The study of unclear phenomena has been facilitated by fuzzy sets. Fuzzy set extensions have allowed for a more detailed investigation of these kinds of research. Finding quantitative measures for ambiguity and other characteristics of these occurrences thus becomes a challenge. As a fuzzy set extension, several researchers proposed intuitionistic fuzzy (IF) sets and used them in many contexts since they were first described by Atanassov. One such use is to solve multi-criteria decision-making issues. This study measure the amount of knowledge linked with an IF-set. An IF-knowledge measure is proposed. Using numerical examples, its utility and validity are examined. Besides this, the IF-accuracy measure, IF-information measure, similarity measure, and dissimilarity measure, are the four new measures that are derived from the proposed IF-knowledge measure. All these measures are checked for their validation and their properties are discussed. Pattern detection is taken as an application of the proposed accuracy measure. Finally, a modified VIKOR approach depending upon the proposed similarity and dissimilarity measure is proposed to deal with an MCDM issue in an intuitionistic fuzzy environment. The efficiency of the proposed approach is demonstrated by using a numerical example. A comparative study is also provided to assess the feasibility of the proposed approach.
PubDate: 2023-11-01
-
- Pythagorean fuzzy cognitive analysis for medical care and treatment
decisions-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The medical industry has employed a variety of decision-making methods to help medical professionals. The use of fuzzy techniques is necessitated by the fact that medical data is often ambiguous. This study desires to show the decision-making application of a novel Pythagorean Fuzzy Cognitive Map (PFCM) in the treatment of pregnant women with heart disease. The PFCM integrates the principles of Pythagorean fuzzy sets with cognitive maps, resulting in a better intuitive model for human understanding. PFCM combines Pythagorean Fuzzy TOPSIS and Fuzzy Cognitive Maps, determining weights for expert opinions and criteria. It yields a fuzzy cognitive map with weighted linkages to visualize relationship strengths. To measure the impact of the PFCM, we conduct a hypothetical case study in which women were assumed to have cardiovascular disease. We gathered input values, diagnosis, and prognosis data and used them to design an algorithm that demonstrates the complete working of the system. After completing the algorithm, we validate the model using some example values and compared the accuracy obtained with other techniques. Our findings show that the PFCM is a highly accurate and effective tool for decision-making in the treatment of pregnant women with heart disease. The present study offers new insights into the use of Pythagorean fuzzy cognitive maps and their potential for improving decision-making in healthcare.
PubDate: 2023-11-01
-
- Novel fuzzy similarity measures and their applications in pattern
recognition and clustering analysis-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Fuzzy similarity measures are utilized to match two or more records and are essential to deal with data classification and pattern-matching problems. We have noticed that the existing studies on similarity measures in the classical fuzzy framework have certain issues, for example, inappropriate identification of structured linguistic variables, inappropriate classification results, etc. In this paper, we propose three new fuzzy similarity measures based on continuous functions and realize their advantages in connection with their application to pattern recognition and cluster analysis. The validity of clusters is also identified using the concept of cluster validity index. The experimental results demonstrate that the proposed similarity measures show higher accuracy in the identification of structured linguistic variables and a higher degree of confidence in the classification of unknown patterns. Several application examples with artificial and real data are utilized to demonstrate the credibility and advantages of the proposed similarity measures.
PubDate: 2023-11-01
-
- Solution of the Pythagorean fuzzy wave equation with Pythagorean fuzzy
Fourier sine transform-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The main objective of this research article is to study the analytical solution of the Pythagorean fuzzy wave equation under the generalized Hukuhara partial differentiability using the Pythagorean fuzzy Fourier sine transform. Some concepts of multivariate Pythagorean fuzzy-valued functions and their gH-partial differentiability along with integrability are given. The notions of Pythagorean fuzzy Fourier sine transform and Pythagorean fuzzy Fourier inverse sine transform are introduced along with some fundamental properties. Furthermore, a new Pythagorean fuzzy wave equation model is developed under gH-differentiability using the Pythagorean fuzzy Fourier sine transform. Some numerical examples are solved with the proposed method and their solutions are displayed graphically to verify and support theoretical results. A practical application of the Pythagorean fuzzy wave equation to magnetic resonance imaging is also described.
PubDate: 2023-11-01
-
- Multiple attribute decision-making based on a prospect theory-based TOPSIS
method for venture capital selection with complex information-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Venture capitalists are heavily influenced by their psychological state and intuition when making investment decisions in uncertain investment environments. To choose promising enterprises in China, it is necessary to understand how to consider both the uncertainty of venture capital and constrained rationality of venture capitalists. Considering the risk of information ambiguity and psychological factors of venture capitalists in a complex environment, a multiple-attribute decision-making (MADM) approach combining TOPSIS and prospect theory is investigated under a probabilistic linguistic q-Rung ortho-pair fuzzy (PLq-ROF) situation. First, a modified weight determination rule is proposed to derive the attribute importance owing to the different preferences of venture capitalists on the attribute sets. Second, psychological risk factors of venture capitalists are introduced into decision-making with the PLq-ROFS, and its prospect value function is also defined. Furthermore, the PLq-ROFS assessment is converted into a value matrix, and based on this, the ratio of gains to losses for each enterprise is computed to determine the ranking of the enterprises. Finally, the application process of the proposed method is demonstrated using Ali Capital as an example to select the most profitable enterprise for venture capitalists. The effectiveness and the applicability of the proposed method are further illustrated by a comparison with existing methods.
PubDate: 2023-11-01
-
- Multi-attribute group decision-making based on bipolar n,m-rung orthopair
fuzzy sets-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The n,m-rung orthopair fuzzy set theory has appeared as a fresh mathematical tool for dealing with uncertainty in several fields of the real world. The n,m-rung orthopair fuzzy sets were introduced to make uncertain data from broadly applicable real-world decision-making scenarios tractable analytically. In this regard, n,m-rung orthopair fuzzy sets outperform intuitionistic, Pythagorean, Fermatean, and q-rung orthopair fuzzy sets in terms of flexibility and dependability. Bipolar fuzzy sets are useful tools for handling fuzziness and bipolarity. The bipolar n,m-rung orthopair fuzzy set (Bn,m-ROFS) model is presented in this study as a generic extension of two powerful existing models, namely the bipolar intuitionistic fuzzy set and the bipolar Pythagorean fuzzy set models. This suggested set completely reflects both quantitative and qualitative evaluations and fully exploits the n,m-rung orthopair fuzzy set. Moreover, subset, equal, complement, intersection, and union are some key characteristics of the proposed Bn,m-ROFS model that are examined along with numerical examples. Additionally, certain fundamental operations are established here, including the bipolar n,m-rung orthopair fuzzy weighted average operator and the bipolar n,m-rung orthopair fuzzy weighted geometric operator. Furthermore, a Bn,m-ROFS application is investigated to handle various multiattribute decision-making situations, such as the choice of the best manager. The suggested methodology is backed up by an algorithm. Finally, the comparison of the indicated hybrid model with a known model, such as the bipolar Pythagorean fuzzy set, is offered.
PubDate: 2023-11-01
-
- Group decision-making method with Pythagorean fuzzy rough number for the
evaluation of best design concept-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract A rough set is important for the reduction of attributes of an information system, since it approximates a subset of a universal set based on some binary connection. A Pythagorean fuzzy set, on the other hand, provides specific information about the extent to which a statement is true or false. Both of these theories address various types of uncertainty and can be combined to maximize their combined advantages. In the current study, we aim to build a broader structure of fuzzy rough numbers, known as Pythagorean fuzzy rough numbers. The proposed framework addresses some of the limitations of traditional fuzzy rough numbers and provides a more practical and effective solution for dealing with uncertainty in decision-making. Our main objective is to develop a new method based on Pythagorean fuzzy rough numbers, the Analytic Hierarchy Process (AHP), and the Technique of Ranking by Similarity to Ideal Solution (TOPSIS) method. The suggested approach is tested on a case study which involves the selection of a product design concept for a new heat exchanger. The Pythagorean fuzzy rough AHP technique is implemented to assess the significance of each factor in the design process, and the Pythagorean fuzzy rough TOPSIS approach is used to rank the design concepts in order of their overall quality. The outcomes demonstrates that the suggested methodology efficiently assesses design concepts, making it helpful for the design industry in decision-making process. This study highlights the importance of integrating the AHP-TOPSIS method based on Pythagorean fuzzy rough numbers for a comprehensive evaluation of design concepts, taking into account both qualitative and quantitative factors to aid complex decision-making processes. The proposed method is thoroughly compared with some other existing decision-making methods.
PubDate: 2023-11-01
-
- A priority-based heuristic approach for solving flexible flow-shop with
parallel machine scheduling in a fuzzy environment-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The prime objective of the manufacturing industry is to meet the non-decreasing demand of customers with quality products. Evaluation of optimal job sequence can largely increase the productivity, thereby fulfilling the aim of an enterprise. Usually, determining the optimal job sequence is an arduous task due to the fact that unit increase in an input exponentially increases the problem size. Scheduling problems is a case of non-deterministic polynomial (NP) hard problem which implies it is impractical to compute optimal job sequence within feasible time. Therefore, in the present study, a novel two-phase heuristic algorithm is proposed for multi-stage scheduling problem. The first phase of the proposed model is to compute the job and machine priority. Job priority is the measure of the total work remaining and time taken for processing and completion of the job. On the other hand, machine priority determines the machine that shall take the job for processing. The job prioritization is computed by hybridizing the completion time (CT), processing time (PT) and total work remaining (TWR) for a job. Whereas, machines in each stage of the multi-stage scheduling problem is prioritized by a novel multi-criteria decision-making (MCDM) method which is based on the concept of risk minimization. In the proposed MCDM model, the risk is defined as the loss for choosing an unreliable machine to process a job. The second phase of the proposed method involves assigning of the jobs to the machines based on their priority. The potentiality of a proposed algorithm lies in the practicality and robustness of the model. Hence, it is applied in a flexible flow-shop scheduling (FFSS) problem of a medium-sized manufacturing industry. The performance of the model is statistically tested by Wilcoxon signed-rank test on the basis of make-span and execution. Finally, the proposed approach is validated by comparing the result with some benchmark problems from the literatures.
PubDate: 2023-11-01
-
- Connectivity index of directed rough fuzzy graphs and its application in
traffic flow network-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The directed rough fuzzy graph (DRFG) is a fusion of rough and fuzzy theory, as it deals with incomplete and vague information simultaneously. Connection or the strength of connectivity \((\mathcal{S}\mathcal{C})\) is vital in the realm of circuits or networks that are linked to the real world. As a result, \(\mathcal{S}\mathcal{C} \) is one of the most essential aspects of a directed rough fuzzy network system. The neighborhood connectivity index \((\mathcal {NCI})\) is one such parameter that has a variety of applications in network theory. In this study, our main objective is to present a new topological index \(\mathcal {NCI}\) based on DRFGs to solve complicated problems. Motivated by the modeling of networks, the strength of vertices to their neighboring vertices, and the efficiency of DRFGs to solve complex problems, we aim to study the NCI of DRFGs. In this paper, we successfully introduce a notion NCI based on DRFGs to deal with the uncertainties that arise in real-world problems. Based on the strength of vertices to neighboring vertices, we provide several lower and upper bounds for the \(\mathcal {NCI}\) of DRFGs with reference to other graph invariants such as the number of vertices, edges, and degree distance. When we study \(\mathcal {NCI}\) in operations for DRFGs with a large number of vertices, the degree of vertices in a DRFG provides a confusing picture. Therefore, a mechanism to determine the \(\mathcal {NCI}\) for DRFG operations is therefore required. Therefore, generalized formulas for the \(\mathcal {NCI}\) of DRFGs obtained by operations such as union, composition, and Cartesian product are also developed. An algorithm for obtaining the \(\mathcal {NCI}\) of DRFGs is also proposed. Further, an application of the \(\mathcal {NCI}\) of DRFGs in traffic flow networks was discussed to identify the busiest intersection using the proposed algorithm. Finally, we illustrate a comparative analysis and analysis table of the established approach ( \(\mathcal {NCI}\) ) with existing techniques (connectivity index \((\mathcal{C}\mathcal{I})\) and Wiener index \((\mathcal{W}\mathcal{I})\) ) are shown to demonstrate the validity of the presented approach.
PubDate: 2023-11-01
-
- Multi-criteria group decision-making for energy production from municipal
solid waste in Iran based on spherical fuzzy sets-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Energy production from the waste is an innovative idea to be implemented that can significantly raise one’s energy production capacity via some adaptable waste-to-energy technology. This study ponders over the municipal solid waste management problem to dispose off the waste in a sanitary, beneficial and environmental friendly manner to reduce the large volume of waste. To target the waste management problem, this article designs a multi-criteria group decision-making technique to capture all the elements of the considered problem within the governance of a decision-making team. The proposed approach, spherical fuzzy ELECTRE III method, has the supplementary potential to delicately treat the pseudo criteria for the authentic investigation of the outranking relations unlike the traditional variants of ELECTRE methods which proceed on the concept of concordance and discordance sets. The proposed technique is privileged to derive the preferences in terms of indifference, strong and weak preference relations by carefully examining the explicit behavior of each criterion in accordance to indifference, preference and veto threshold values. Nextly, this study addresses the case of municipal waste management of the Azerbaijan region in Iran and investigates different waste-to-energy technologies by the proposed SF-ELECTRE III method. To enlighten the consistency of proposed approach, a comparative study with existing SF-PROMETHEE methods is conducted that includes the numerical results and logical arguments.
PubDate: 2023-10-07
-
- Multi-criteria group decision-making based on spherical fuzzy rough
numbers-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Multi-criteria group decision-making (MCGDM) relies heavily on the individual assessments of decision-makers, which can introduce subjectivity and uncertainty that can significantly affect the accuracy of results. Although a great deal of research has been done on managing subjectivity and uncertainty, it is still an uphill battle. Although fuzzy sets are able to capture individual uncertainty, they cannot handle the subjectivity between different decision-makers. Rough sets, on the other hand, are a realistic mathematical framework with the unique ability to handle subjective and ambiguous data without any additional modifications. Inspired by the gain of spherical fuzzy sets, which have the advantage of absorbing neutral opinions in addition to positive and negative membership degrees, can effectively represent uncertainty, and are inspired by the advantages of fuzzy rough numbers in manipulating subjectivity, this paper introduces a new concept of spherical fuzzy roughness to improve the advantages of MCGDM in managing uncertainty and subjectivity by combining the advantages of spherical fuzzy numbers and rough numbers. First, we propose a spherical fuzzy rough number to incorporate the individual judgments made by decision-makers about the importance of criteria and the potential of alternatives. We then propose an AHP-TOPSIS technique based on integrated spherical fuzzy rough number as an effective method for manipulating uncertainty and subjectivity in group decision-making settings. Furthermore, to examine the validity and effectiveness of the proposed technique, a real-life case study is conducted. This case study analyzes the bus system in Istanbul, with a particular emphasis on reducing carbon dioxide emissions and improving air quality by replacing diesel buses with electric buses. This study also involves a comprehensive comparative analysis and sensitivity analysis with the existing techniques to assess the authenticity and consistency of the proposed technique.
PubDate: 2023-09-26
-
- Multi-criteria group decision-making based on the combination of dual
hesitant fuzzy sets with soft expert sets for the prediction of a local
election scenario-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Several existing group decision-making strategies and their hybrid models, such as soft expert sets (SESs), dual hesitant fuzzy sets (DHFSs), and hesitant fuzzy SESs, have proven to be valuable in resolving issues of many daily-life situations involving uncertainty. However, the hybridization of different existing uncertainty theories, particularly DHFSs and SESs, remains unexplored in the literature. To further enhance decision-making capabilities, the specific goal of this study is to extend the DHFS model to its extreme within the soft expert framework. For achieving this, we combine DHFSs with SESs as a new hybrid model called dual hesitant fuzzy soft expert sets (DHFSESs) to better handle uncertain situations in group decision-making. Furthermore, we investigate some basic properties and operations of the developed model and explain these notions with corresponding numerical examples, such as subset relation, complement, union, intersection, the ’OR’ operation, the ’AND’ operation, and level SESs of the developed DHFSESs. Additionally, we verify that the presented model obeys commutative, associative, and De Morgan’s laws. To demonstrate the practicality of the DHFSES model, we solve a real-world multi criteria group decision-making problem (prediction of results in an upcoming local election scenario) by incorporating dual hesitant fuzzy soft expert knowledge that is supported by an algorithm. Finally, we evaluate the authenticity and feasibility of the proposed DHFSES model by comparing it with some preexisting approaches, including dual hesitant fuzzy soft sets and hesitant fuzzy SESs, to validate its advantages over them.
PubDate: 2023-09-13
DOI: 10.1007/s41066-023-00414-w
-
- Information flow-based fuzzy cognitive maps with enhanced interpretability
-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Fuzzy Cognitive Maps (FCMs) are a graph-based methodology successfully applied for knowledge representation of complex systems modelled through an interactive structure of nodes connected with causal relationships. Due to their flexibility and inherent interpretability, FCMs have been used in various modelling and prediction tasks to support human decisions. However, a notable limitation of FCMs is their susceptibility to inadvertently capturing spurious correlations from data, undermining their prediction accuracy and interpretability. In addressing this challenge, our primary contribution is the introduction of a novel framework for constructing FCMs using the Liang-Kleeman Information Flow (L-K IF) analysis, a quantitative causality analysis rigorously derived from first principles. The novelty of the proposed approach lies in the identification of actual causal relationships from the data using an automatic causal search algorithm. These relationships are subsequently imposed as constraints in the FCM learning procedure to rule out spurious correlations and improve the aggregate predictive and explanatory power of the model. Numerical simulations validate the superiority of our method against state-of-the-art FCM-based models, thereby bolstering the reliability, accuracy, and interpretability of FCMs.
PubDate: 2023-09-07
DOI: 10.1007/s41066-023-00417-7
-
- Fermatean fuzzy multi-criteria decision-making based on Spearman rank
correlation coefficient-
Free pre-print version: Loading...Rate this result: What is this?Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Real-world decision-making challenges tend to evolve into more intricate scenarios over time. In this context, the Fermatean fuzzy set emerges as an efficient and convenient framework, adept at illustrating the uncertainties inherent in multi-criteria decision-making (MCDM) problems. To address decision-making challenges intertwined with uncertainties, the fundamental objective of this study is to develop a Fermatean fuzzy MCDM tool. This tool aims to expand users’ scope to articulate their opinions and viewpoints. As a preliminary step, the study begins by elucidating the computation of the degree of proximity between the optimal alternative and its counterparts. This article presents the concept, representation, and pertinent characteristics of the Spearman rank correlation coefficient (CC) within the context of Fermatean fuzzy sets. Subsequent to this, a multi-criteria decision-making technique, fortified by incorporating Fermatean fuzzy operators (FFOs), is formulated based on the proposed Spearman rank CC. Ultimately, we demonstrate the significance and efficacy of the introduced approach by showcasing its application in a real-world context, specifically within the domain of supplier selection decision-making. The results revealed that the primary advantage of the provided decision rule lies in its potential to effectively reduce production costs and streamline complexity within the context of supplier selection problems, both in theory and practical application. We demonstrate the superiority of the proposed method in delivering reliable outcomes through a comprehensive analysis of FFOs and a comparative assessment against established techniques. A concrete case study is employed to firmly establish the stability and credibility of FFOs when combined with the Spearman rank CC. Additionally, this study carefully conducts a comprehensive comparative evaluation, comparing the previously developed methods with the newly proposed approach. This reinforces the robustness and authenticity of the FFO-based methodology and highlights its unique and practical nature.
PubDate: 2023-09-06
DOI: 10.1007/s41066-023-00421-x
-
