Multiple Criteria Decision Making Methods with Multi-polar Fuzzy Information: Algorithms and Applications (Studies in Fuzziness and Soft Computing, 430)

✍ Scribed by Muhammad Akram, Arooj Adeel

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 564
• Category: Library

✦ Synopsis

This book presents an extension of fuzzy set theory allowing for multi-polar information, discussing its impact on the theoretical and practical development of multi-criteria decision making. It reports on set of hybrid models developed by the authors, and show how they can be adapted, case by case, to the lack of certainty under a variety of criteria. Among them, hybrid models combining m-polar fuzzy sets with rough, soft and 2-tuple linguistic sets, and m-polar hesitant fuzzy sets and hesitant m-polar fuzzy are presented, together with some significant applications. In turn, outranking decision-making techniques such as m-polar fuzzy ELECTRE I, II, III and IV methods, as well as m-polar fuzzy PROMETHEE I and II methods, are developed. The efficiency of these decision-making procedures, as well as other possible extensions studied by the authors, is shown in some real-world applications. Overall, this book offers a guide on methodologies to deal with the multi-polarity and fuzziness of the real-world problems, simultaneously. By including algorithms and computer programming codes, it provides a practice-oriented reference guide to both researchers and professionals working at the interface between computational intelligence and decision making.

✦ Table of Contents

Foreword
Preface
Contents
About the Authors
List of Figures
List of Tables
1 Hybrid Multi-polar Fuzzy Models
1.1 Introduction
1.2 m-Polar Fuzzy Sets
1.3 Rough m-Polar Fuzzy Sets
1.3.1 Selection of Flats ch1DAk2
1.3.2 Selection of Employees for Promotion and Bonus
1.4 m–Polar Fuzzy Soft Sets
1.4.1 Selection of an Employee in an Organization
1.4.2 Selection of Suitable Site for a Resort
1.5 Similarity Measure for m-Polar Fuzzy Sets
1.5.1 Pattern Recognition Problem
1.5.2 Medical Diagnosis of Anemia
1.5.3 Medical Diagnosis of Dengue Fever
1.6 m-Polar Fuzzy Rough Sets
1.6.1 Selection of Prints and Shades for Variety of Fabrics
1.6.2 Selection of Features for Different Models of Mobiles
1.7 m-Polar Fuzzy Soft Rough Sets
1.7.1 Selection of a Hotel
1.7.2 Selection of a Place
1.7.3 Selection of a House
1.8 Soft m-Polar Fuzzy Rough Sets
1.8.1 Comparison of Popular Mobile Phones for Selection
1.8.2 Selection of a Site for Construction of a Grid Station
1.8.3 Comparison of Patients for Recovery of Heart Disease
1.9 Conclusion
References
2 TOPSIS and ELECTRE-I Methods Under Multi-polar Fuzzy Linguistic Sets
2.1 Introduction
2.2 Multi-criteria Decision Making Methods
2.3 m–Polar Fuzzy ELECTRE-I Method
2.3.1 Selection of a Suitable Location for a Diesel Power Plant
2.3.2 Selection of a Site for the Airport
2.3.3 Performance Evaluation of Physical Sciences Instructor
2.4 Contribution, Sensitivity and Comparison Analysis
2.5 The Concept of m-Polar Fuzzy Linguistic Variable
2.6 m–Polar Fuzzy Linguistic ELECTRE-I Approach for MCDM
2.6.1 Salary Analysis of Companies
2.7 m–Polar Fuzzy Linguistic ELECTRE-I Method for MCGDM
2.7.1 Selection of Most Corrupted Country
2.8 Discussion of the Proposed Approach
2.9 m–Polar Fuzzy Linguistic TOPSIS Method for MCGDM
2.9.1 Models Ranking According to their Appearance
2.9.2 Ranking of High Speed Racing Cars
2.10 Comparison Analysis of Proposed Approach
2.11 Conclusion
References
3 Introducing Hesitancy: TOPSIS and ELECTRE-I Models
3.1 Introduction
3.2 m-Polar Hesitant Fuzzy Set
3.2.1 Basic Operations of m–Polar Hesitant Fuzzy Set
3.2.2 Comparison Laws of m–Polar Hesitant Fuzzy Elements
3.3 An m–Polar Hesitant Fuzzy TOPSIS Approach
3.3.1 Selection of a Perfect Brand Name
3.3.2 Selection of Suitable Product Design for a Company
3.4 An m-Polar Hesitant Fuzzy ELECTRE-I Approach
3.4.1 Selection of Bricks for Construction
3.5 Hesitant m–Polar Fuzzy Set
3.5.1 Basic Operations for Hesitant m–Polar Fuzzy Set
3.5.2 Comparison Laws of Hesitant m–Polar Fuzzy Set
3.6 Hesitant m-Polar Fuzzy TOPSIS Approach
3.6.1 Comparison of Top Five Populous Countries
3.6.2 Comparison of Different Types of Textiles or Clothing
3.7 Hesitant m-Polar Fuzzy ELECTRE-I Approach
3.7.1 Site Selection for Farming Purposes
3.8 Conclusion
References
4 Extended ELECTRE I, II Methods with Multi-polar Fuzzy Sets
4.1 Introduction
4.2 An m–Polar Fuzzy ELECTRE I Method
4.3 Case Study: Selection of Best Insulating Scheme for Exterior Wall
4.4 The m–Polar Fuzzy ELECTRE II Method
4.5 A Case Study: Selection of Appropriate Location for Nuclear Power Plant
4.5.1 Available Alternatives
4.5.2 Selection of m–Polar Criteria
4.5.3 Stepwise Procedure
4.6 Comparison Analysis
4.7 Conclusion
References
5 Enhanced ELECTRE III Method with Multi-polar Fuzzy Sets
5.1 Introduction
5.2 An m–Polar Fuzzy ELECTRE III Method
5.3 Case Study: Selection of Best Hazardous Waste Carrier Firm
5.4 Comparative Study
5.4.1 Comparison with m–Polar Fuzzy ELECTRE I Method
5.4.2 Comparison with m–Polar Fuzzy ELECTRE II Method
5.4.3 Discussion
5.5 Insights of m–Polar Fuzzy ELECTRE III Method
5.6 Conclusion
References
6 Extended ELECTRE IV Method with Multi-polar Fuzzy Sets
6.1 Introduction
6.2 An m–Polar Fuzzy ELECTRE IV Method
6.3 Case Study: Islamic Azad University Qazvin Branch Innovation Park Project, Iran
6.4 Comparison Analysis
6.4.1 Discussion
6.5 Conclusion
References
7 Extended PROMETHEE Method Under Multi-polar Fuzzy Sets
7.1 Introduction
7.2 Basic Concept
7.3 Analytical Hierarchy Process
7.4 m–Polar Fuzzy PROMETHEE Method
7.4.1 Ranking the Sites of Hydroelectric Power Stations
7.4.2 Criteria Weights by AHP
7.4.3 Ranking Through m–Polar Fuzzy PROMETHEE
7.5 Comparative Analysis
7.5.1 With Usual Criterion Preference Function
7.5.2 m–Polar Fuzzy ELECTRE I
7.6 Conclusion
References
8 Aggregation Operators for Decision Making with Multi-polar Fuzzy Sets
8.1 Introduction
8.2 m–Polar Fuzzy Dombi Aggregation Operators
8.2.1 m–Polar Fuzzy Dombi Arithmetic Aggregation Operators
8.2.2 m–Polar Fuzzy Dombi Geometric Aggregation Operators
8.3 m–Polar Fuzzy Hamacher Aggregation Operators
8.3.1 m–Polar Fuzzy Hamacher Arithmetic Aggregation Operators
8.3.2 m–Polar Fuzzy Hamacher Geometric Aggregation Operators
8.4 MCDM Methods Using Dombi and Hamacher Aggregation Operators
8.4.1 Agriculture Land Selection
8.4.2 Performance Evaluation of Commercial Banks
8.4.3 Assessment of Health Care Waste Treatments Alternatives
8.4.4 Selection of a Best Company for Investment
8.4.5 Selection of Most Affected Country by Human Trafficking
8.5 Comparison Analysis and Discussion
8.5.1 Effectiveness Test
8.6 Conclusion
References
9 2-Tuple Linguistic Multi-polar Fuzzy Hamacher Aggregation Operators
9.1 Introduction
9.2 Preliminaries
9.3 2-Tuple Linguistic m–Polar Fuzzy Hamacher Aggregation Operators
9.4 2-Tuple Linguistic m–Polar Fuzzy Hamacher Geometric Aggregation Operators
9.5 Mathematical Approach for MADM Using 2-Tuple Linguistic m–Polar Fuzzy Information
9.6 Best Location for the Thermal Power Station: Case Study ch9no1
9.6.1 Influence of the Parameter λ on Decision Making Results
9.7 Comparative Analysis
9.7.1 Comparison with Existing Techniques
9.7.2 Discussion
9.8 Conclusions
References
10 Hybrid Models Based on Multi-polar Fuzzy Soft Sets
10.1 Introduction
10.2 m–Polar Fuzzy Soft Expert Sets
10.3 Mathematical Approach for MCGDM with m–Polar Fuzzy Information
10.4 Applications
10.4.1 Selection of a Suitable Site for a Dam
10.4.2 Country Most Affected by Human Trafficking
10.5 Comparative Analysis
10.6 m–Polar Fuzzy N–Soft Sets
10.7 m–Polar Fuzzy N–Soft Rough Sets
10.8 Applications
10.8.1 Selection of a Restaurant
10.8.2 Selection of a Hotel
10.8.3 Selection of a Resort
10.8.4 Selection of a Laptop
10.9 Discussion
10.10 Conclusion
References
Index