This book collects chapters which discuss interdisciplinary solutions to complex problems by using different approaches in order to save money, time and resources. The book presents the results on the recent advancements in artificial intelligence, computational intelligence, decision-making problems, emerging problems and practical achievements in the broad knowledge management field. q-ROFS is one of the hot topics for all the researchers, industrialists as well as academicians. This book is of interest to professionals and researchers working in the field of decision making and computational intelligence, as well as postgraduate and undergraduate students studying applications of fuzzy sets.
The book helps solve different kinds of the decision-making problems such as medical diagnosis, pattern recognition, construction problems and technology selection under the uncertain fuzzy environment. Containing 19 chapters, the book begins by giving a topology of the q-ROFSs and their applications. It then progresses in a logical fashion, dedicating a chapter to each approach, including the generalized information measures for q-ROFSs, implementation of q-ROFSs to medical diagnosis, inventory model, multi-attribute decision-making and approaches to real-life industrial problems such as green campus transportation, social responsibility evaluation pattern and extensions of the q-ROFSs.
Author(s): Harish Garg
Publisher: Springer
Year: 2022
Language: English
Pages: 557
City: Singapore
Preface
Contents
About the Editor
1 q-Rung Orthopair Fuzzy Supra Topological Applications in Data Mining Process
1.1 Introduction
1.2 Preliminary
1.3 q-Rung Orthopair Fuzzy Supra Topological Spaces
1.4 Mappings of q-Rung Orthopair Fuzzy Spaces
1.5 Algorithm for Data Mining Problem Via q-Rung Orthopair Fuzzy Supra Topology
1.6 Numerical Example
1.7 Conclusion and Future Work
References
2 q-Rung Orthopair Fuzzy Soft Topology with Multi-attribute Decision-Making
2.1 Introduction
2.2 Some Elementary Models
2.3 q-Rung Orthopair Fuzzy Soft Sets
2.4 q-Rung Orthopair Fuzzy Soft Topology
2.4.1 q-ROFS Separation Axioms
2.5 Multi-attribute Decision-Making
2.5.1 Numerical Application
2.5.2 Generalized Choice Value Method
2.6 Conclusion
References
3 Decision-Making on Patients' Medical Status Based on a q-Rung Orthopair Fuzzy Max-Min-Max Composite Relation
3.1 Introduction
3.2 Preliminaries
3.2.1 q-Rung Orthopair Fuzzy Sets
3.3 q-Rung Orthopair Fuzzy Max-Min-Max Composite Relation
3.3.1 Numerical Application
3.4 Application of qROFMMMCR in Disease Diagnosis
3.4.1 qROFMMMCR in Terms of Patients and Diseases with Respect to Symptoms
3.4.2 Experiment of Disease Diagnosis
3.5 Conclusion
References
4 Soergel Distance Measures for q-Rung Orthopair Fuzzy Sets and Their Applications
4.1 Introduction
4.2 Background
4.2.1 q-Rung Orthopair Fuzzy Sets
4.2.2 Some Existing Information Measures for q-ROFSs
4.3 Soergel Distance Measures for q-ROFSs and Their Validation/Efficiency
4.3.1 Twelve Types of Soergel Distance Measures for q-ROFSs
4.3.2 Twelve Types of Weighted Soergel Distance Measures for q-ROFSs
4.3.3 The Validation/Efficiency of SoDMs and SoSMs for q-ROFSs
4.4 Applications of SoDMs
4.4.1 Proposed Decision-Making Method
4.4.2 Illustrative Examples
4.5 Comparison Analysis
4.6 Sensitivity Analysis and Advantages of SoDMs
4.6.1 Sensitivity Analysis of SoDMs for the Value of q
4.6.2 Advantages of Proposed Approaches
4.6.3 Limitations of Proposed Approaches
4.7 Conclusion
References
5 TOPSIS Techniques on q-Rung Orthopair Fuzzy Sets and Its Extensions
5.1 Introduction
5.2 Preliminaries
5.2.1 TOPSIS
5.3 TOPSIS Techniques on q-ROFS
5.4 Combined Weighting TOPSIS MADM Using q-ROHFS
5.5 TOPSIS Techniques on q-ROFSfS
5.6 Applications
5.7 Conclusions
References
6 Knowledge Measure-Based q-Rung Orthopair Fuzzy Inventory Model
6.1 Introduction
6.1.1 Literature Review
6.1.2 Research Gap and the Contribution
6.2 Preliminaries
6.3 Model Formulation
6.3.1 Case (I): Replacement of the Faulty Option by Warranty Claiming and Repair Option
6.3.2 Case (ii): Replacement of the Faulty Option by Warranty Claiming and the Emergency Purchase Option
6.3.3 Inventory Model with q-Rung Orthopair Fuzzy Variables
6.3.4 Vendor’s Optimal Policy
6.4 Numerical Computation
6.4.1 Sensitive Analysis
6.4.2 Comparison Study
6.5 Conclusion
Appendix
References
7 Higher Type q-Rung Orthopair Fuzzy Sets: Interval Analysis
7.1 Introduction
7.2 Basic Concepts of q-RIVOFSs
7.3 Some Novel Measures for q-RIVOFSs
7.3.1 Cross-Entropy Measure for q-RIVOFSs
7.3.2 Hausdorff Distance for q-RIVOFSs
7.4 Multi-Attribute Decision-Making Method Under q-RIVOF Circumstances
7.4.1 TODIM Method with q-RIVOFSs
7.5 Illustrative Example
7.5.1 Case Description
7.5.2 Illustration of the Proposed Q-RIVOFS-TODIM Approach
7.5.3 Sensitivity Analysis
7.5.4 Comparative Analysis
7.6 Conclusion
References
8 Evidence-Based Cloud Vendor Assessment with Generalized Orthopair Fuzzy Information and Partial Weight Data
8.1 Introduction
8.2 Literature Review
8.2.1 CV Selection Using Decision Models
8.2.2 GOFS-Based Decision Approaches
8.3 A New Scientific Framework for CV Selection
8.3.1 Preliminaries
8.3.2 Mathematical Model with GOFS
8.3.3 Evidence-Based Ranking Algorithm with GOFS
8.4 Real Case Example—Selection of CVs
8.5 Comparative Analysis
8.6 Conclusion
References
9 Supplier Selection Process Based on CODAS Method Using q-Rung Orthopair Fuzzy Information
9.1 Introduction
9.2 q-Rung Orthopair Fuzzy Sets (q-ROFS)
9.2.1 Algebraic Operations q-ROFS
9.3 Combinative Distance-Based Assessment (CODAS)
9.3.1 Steps for the CODAS Method
9.4 CODAS and q-Rung Orthopair Fuzzy Sets for the Supplier Selection Process
9.5 Case Numeric
9.6 Discussions
9.7 Conclusions
References
10 Group Decision-Making Framework with Generalized Orthopair Fuzzy 2-Tuple Linguistic Information
10.1 Introduction
10.2 Preliminaries
10.2.1 The 2-Tuple Linguistic Representation Model
10.2.2 The MSM Operator and its Weighted Form
10.3 The GOFTLMSM Aggregation Operator and its Weighted Form
10.3.1 The GOFTLMSM Operator
10.3.2 The GOFTLWMSM Operator
10.4 The GOFTLDMSM Aggregation Operator and its Weighted Form
10.4.1 The GOFTLDMSM Operator
10.4.2 The GOFTLWDMSM Operator
10.5 An MAGDM Model with GOFTL Information
10.6 Illustrative Example and Discussion
10.6.1 Evaluation Process of the Proposed Method
10.6.2 Sensitivity Analysis
10.6.3 Comparative Analysis
10.6.4 Advantages and Superiorities of the Proposed Work
10.7 Conclusions
References
11 3PL Service Provider Selection with q-Rung Orthopair Fuzzy Based CODAS Method
11.1 Introduction
11.2 Literature Survey
11.3 q-ROF CODAS Method
11.3.1 q-Rung Orthopair Fuzzy Sets
11.3.2 q-ROF CODAS Methodology
11.4 Case Study
11.5 Conclusion
References
12 An Integrated Proximity Indexed Value and q-Rung Orthopair Fuzzy Decision-Making Model for Prioritization of Green Campus Transportation
12.1 Introduction
12.2 Literature Review
12.3 Case Study
12.3.1 Definition of Alternatives and Criteria
12.4 Preliminaries
12.5 Proposed Methodologies
12.5.1 Proximity Indexed Value (PIV) Method
12.5.2 Proposed q-ROF PIV Method
12.6 Experimental Results
12.7 Discussion
12.8 Conclusion
References
13 Platform-Based Corporate Social Responsibility Evaluation with Three-Way Group Decisions Under Q-Rung Orthopair Fuzzy Environment
13.1 Introduction
13.2 Preliminaries
13.2.1 q-rung Orthopair Fuzzy Sets (q-ROFSs)
13.2.2 Three Way Decisions (TWDs)
13.3 CSR Evaluation Method Based on TWDs with q-ROFSs
13.3.1 Information Fusion Method
13.3.2 CSR Classification of Platform-Based Enterprises with TWDs
13.4 An Illustrative Example
13.4.1 Decision Analysis with Our Proposed Method
13.4.2 Comparative Experiment
13.4.3 Sensitivity Analysis
13.5 Conclusions
References
14 MARCOS Technique by Using q-Rung Orthopair Fuzzy Sets for Evaluating the Performance of Insurance Companies in Terms of Healthcare Services
14.1 Introduction
14.2 Preliminaries
14.3 Q-ROF-MARCOS Method
14.4 Analysis of Healthcare Services Under Q-ROFS-MARCOS Technique
14.5 Sensitivity Analysis
14.6 Conclusion
References
15 Interval Complex q-Rung Orthopair Fuzzy Aggregation Operators and Their Applications in Cite Selection of Electric Vehicle
15.1 Introduction
15.2 Preliminaries
15.3 Interval Complex q-Rung Orthopair Fuzzy Sets
15.4 Interval Complex q-Rung Orthopair Fuzzy Aggregate Operators for MADM Problems
15.5 The MADM Model Based on IVCq-ROFWA and IVCq-ROFGA Operators
15.6 An Illustrative Example for the Validation of the Proposed MADM Model
15.7 Conclusion and Future Work
References
16 A Novel Fermatean Fuzzy Analytic Hierarchy Process Proposition and Its Usage for Supplier Selection Problem in Industry 4.0 Transition
16.1 Introduction
16.2 Supplier Selection in Industry 4.0 Transition
16.3 Preliminaries: Fermatean Fuzzy Sets
16.4 A Novel Fermatean Fuzzy AHP Extension
16.5 An Application in Turkey
16.6 Discussion and Concluding Remarks
References
17 Pentagonal q-Rung Orthopair Numbers and Their Applications
17.1 Introduction
17.2 Preliminary
17.3 Pentagonal q-Rung Orthopair Numbers
17.4 Multi-criteria Decision-Making Method Based on Pq-RO-Numbers
17.5 Conclusion
References
18 q-Rung Orthopair Fuzzy Soft Set-Based Multi-criteria Decision-Making
18.1 Introduction
18.2 q-ROFSSs
18.2.1 Weighted SM for q-ROFSSs
18.3 MCDM Using q-Rung Orthopair Fuzzy Soft Information
18.4 MCDM with TOPSIS Approach Based on q-ROFSSs
18.5 MCDM Using q-ROFS VIKOR Method
18.6 Practical implementation of proposed SM related to COVID-19
18.7 Conclusion
References
19 Development of Heronian Mean-Based Aggregation Operators Under Interval-Valued Dual Hesitant q-Rung Orthopair Fuzzy Environments for Multicriteria Decision-Making
19.1 Introduction
19.2 Preliminaries
19.2.1 DHq-ROFS
19.2.2 IVDHq-ROFS
19.2.3 Operations on IVDHq-ROFNs
19.2.4 HM Operator
19.2.5 GHM Operator
19.3 HM-Based IVDHq-ROF Aggregation Operators and Its Properties
19.3.1 IVDHq-ROFHM Operator
19.3.2 IVDHq-ROFWHM Operator
19.3.3 IVDHq-OFGHM Operator
19.3.4 IVDHq-ROFWGHM Operator
19.4 Approach to MCDM with HM-Based IVDHq-ROF Information
19.5 Illustrative Example
19.5.1 Description of the Problem
19.5.2 The Influence of the HM Parameters, and ψ, on the Ranking Results
19.5.3 The Influence of the Rung Parameter, q, on the Ranking Results
19.6 Comparative Analyses
19.7 Conclusions
References
