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Decision Support Systems in Smart Computing

Embracing Uncertainty and Challenges

Edited by Harish Garg
Series: Studies in Computational Intelligence and Smart Technologies
Copyright: 2026   |   Expected Pub Date:2026/08/30
ISBN: 9781394399413  |  Hardcover  |  
712 pages

One Line Description
This essential guide bridges theoretical and real-world execution, presenting actionable tools, cutting-edge methodologies, and proven case studies needed to design resilient decision systems that excel in today’s complex, uncertain world.

Audience
Academics, researchers, supply chain managers, sustainability consultants, healthcare professionals, and policymakers in fields like operations research, artificial intelligence, data science, industrial engineering, and environmental studies looking for practical implementations for fuzzy logic in complex, real-world decision scenarios.

Description
In today’s data-driven world, uncertainty is constant, and smart decision-making demands systems that can adapt, reason, and respond. This book explores how modern fuzzy logic, soft
computing, and multi-criteria decision-making methods are revolutionizing the way complex decisions are made in uncertain environments. It brings together global experts to present state-of-the-art methodologies like fuzzy AHP, TOPSIS, DEMATEL, picture fuzzy sets, and interval-valued fuzzy logic, applied across vital domains including smart cities, sustainable supply chains, digital healthcare, and urban infrastructure. Together, they explore both the theoretical
foundations and real-world applications of intelligent decision-making methodologies.
At the core of these contributions lies a common theme: the imperative to model, manage, and make informed decisions under conditions that are inherently imprecise, ambiguous, or conflicting conditions that traditional models often struggle to address. Ranging from foundational theories to advanced sector-specific applications, the book bridges the gap between research and real-world problem-solving, covering a diverse range of industries from urban planning and logistics to healthcare and environmental sustainability, demonstrating the growing reach and versatility of intelligent decision systems. Whether optimizing green logistics, evaluating public health strategies, or enabling intelligent urban planning, readers will discover actionable tools, models, and case studies that respond to today’s challenges. Ideal for researchers, practitioners, and policy professionals, this book is an essential guide to designing robust, adaptive decision support systems that thrive in complexity and uncertainty.
Readers will find the volume:
• Covers advanced fuzzy logic techniques with real-world applications in supply chain, healthcare, and smart cities;
• Features cutting-edge methodologies like fuzzy AHP, TOPSIS, and DEMATEL;
• Includes global case studies and practical decision-making tools for complex, uncertain environments;
• Bridges theory and practice for sustainable, data-driven decision support systems.

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Author / Editor Details
Harish Garg, PhD is an Associate Professor at the Thapar Institute of Engineering and Technology, Patiala, Punjab, India. He has internationally published more than ten books and 530 papers in refereed international journals. His research focuses on computational intelligence, multi-criteria decision making, evolutionary algorithms, reliability analysis, expert systems and decision support systems, computing with words, and soft computing.

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Table of Contents
Preface
Part I: Foundations and Methodologies in Fuzzy Decision-Making
1. A Methodology for A Posteriori Decision Making in Fuzzy
Linear Programming
Boris Pérez-Cañedo, Eduardo René Concepción-Morales and José Luis Verdegay
1.1 Introduction
1.2 List of Abbreviations
1.3 Preliminaries
1.4 Methodology for A Posteriori Decision-Making
1.4.1 Outline of the Proposed Methodology
1.4.2 Method for FLP
1.4.3 Alternative Solutions Generation
1.4.4 Handling the Unmodeled Objectives Via Fuzzy Propositions
1.4.5 Decision-Making
1.5 Illustrative Example
1.5.1 Determination of a Reference Solution
1.5.2 Alternative Solutions Generation
1.5.3 Handling the Unmodeled Objectives
1.5.4 Decision-Making
1.6 Conclusions and Future Work
Acknowledgments
References
2. Interval-Valued Proportional Spherical Fuzzy AHP and TOPSIS Methodology: A Case Study for Smart City Park Planning
Selcuk Cebi, Irem Ucal Sari, Başar Öztayşi, Sezi Çevik Onar and Cengiz Kahraman
List of Abbreviations
2.1 Introduction
2.2 Smart Computing, Methods, Technologies Literature Review
2.3 Smart Cities Literature Review
2.4 Preliminaries: Interval Valued Spherical Fuzzy Sets
2.5 IVPSF AHP and TOPSIS Methodology
2.5.1 Single-Valued Proportional Spherical Fuzzy Sets
2.5.2 Interval Valued Proportional Spherical Fuzzy Sets (IVPSFS)
2.5.3 IVPSF AHP
2.5.4 IVPSF TOPSIS
2.6 A Comparative Analysis of Free Park Space Identification
Methods
2.6.1 Determining Importance Degree of Free Parking Space Evaluation Criteria Using IVPSF AHP
2.6.2 Analysis of Free Park Space Identification Methods Using IVPSF TOPSIS
2.7 Conclusion
References
3. A Novel Measure and Modified Root Assessment Method for
Type-2 Intuitionistic Fuzzy Sets in Multi‑Standard Evaluation
Making for Solid Waste Management
Barathi Gnanavelu and Jagadeeswari Murugan
3.1 Introduction
3.1.1 Research Gap and Motivation Behind the Study
3.1.2 Objectives of the Study
3.2 Preliminary Considerations
3.2.1 Fuzzy Logic Concepts
3.2.2 Fuzzy Score Functions
3.3 Objective of Generalized Modified Score Function
3.4 A Multi-Criteria Decision-Making Problem Using Modified
RAM Procedure
3.4.1 Modified RAM Procedure
3.4.2 Multi-Standard Evaluation Making Problem Using Generalized Modified Score Function for Solid Waste Management
3.4.3 Comparison with Existing Methods
3.4.4 Merits of the Proposed Work
3.5 Conclusion
3.6 Findings and Future Work
3.7 Acknowledgment
References
4. Fuzzy Logic Systems for Decision‑Making Applications
Jitender Kaushal
4.1 Introduction
4.1.1 Objectives
4.2 Principles of Fuzzy Logic
4.2.1 Membership Functions
4.2.1.1 Triangular Membership Function
4.2.1.2 Trapezoidal Membership Function
4.2.1.3 Gaussian Membership Function
4.2.1.4 Sigmoidal Membership Function
4.3 Fuzzy Sets
4.4 Decision-Making Using Fuzzy Discrete Event Systems
4.4.1 Fuzzy Discrete Event Systems (FDES)
4.4.2 Mathematical Foundation of FDES
4.4.3 Fuzzy State Transition Model
4.5 FDES Model for Clinical Status and Quality of Life – An Example
4.5.1 Clinical Status Representation
4.5.2 Treatment Options and State Transition
4.5.3 Quality of Life Evaluation
4.5.4 Case Study
4.6 Fuzzy Decision-Making for Medical Treatment Selection Using Fuzzy TOPSIS
4.6.1 Fuzzy Decision Matrix
4.6.2 Results and Discussion
4.6.3 Comparative Analysis of Fuzzy TOPSIS with Analytical Hierarchy Process (AHP) for Medical Treatment Selection
4.6.4 Merits of the Proposed Work
4.7 Conclusions
Future Scope
References
Part II: Supply Chain, Logistics, and Industry Applications
5. Risk Prioritization and Mitigation in Cold Supply Chain
Distribution: A Best–Worst Method Approach
Irem Helvacioglu and Muhammet Gul
5.1 Introduction
5.2 Basic Preliminaries and Definitions
5.2.1 List of Abbreviations
5.3 Literature Review
5.4 Methodology
5.4.1 Basic Preliminaries
5.4.1.1 Multi-Criteria Decision-Making
5.4.1.2 Best–Worst Method
5.5 Results
5.6 Discussion
5.7 Conclusion
References
6. Fuzzy Machine Learning Integrated MCDM in Sustainable
Supplier Selection of Healthcare Industries
Nivetha Martin and Akbar Rezaei
6.1 Introduction
6.2 Review of Earlier Works
6.2.1 MCDM in Health Care Supplier Selection
6.2.2 Decision Trees in Health Care Systems and Supplier Selection
6.2.3 Existing Gaps and Novel Contributions
6.3 Preliminaries
6.3.1 Multi-Criteria Decision Making (MCDM)
6.3.2 Key Components of MCDM
6.3.3 General Steps in MCDM
6.3.4 Fuzzy Set
6.3.5 Fuzzy Logic
6.3.6 Linguistic Variable
6.4 Methodology
6.4.1 Phase I: Fuzzy Decision Trees in Criterion Reduction
6.4.2 Phase II: Fuzzy AROMAN in Ranking the Supplier
6.5 Hybrid Decision-Making Approach in Supplier Selection
6.5.1 Problem Description
6.5.2 Phase I Fuzzy Decision Trees in Criterion Reduction
6.5.3 Phase II: Fuzzy AROMAN in Ranking the Supplier
6.6 Results and Discussions
6.7 Industrial Implications
Conclusion
Bibliography
7. Green Supplier Selection Using a Preference Method: Complete Pre-Order and Geometrical Representation Analyses
Lazim Abdullah, Hazwani Hashim, Noor Azzah Awang, Norsyahidah Zulkifli and Nor Liyana Amalini Mohd Kamal
7.1 Introduction
7.2 Preference Functions
7.3 Criteria of Green Suppliers
7.3.1 Cost
7.3.2 Quality
7.3.3 Delivery
7.3.4 Services
7.3.5 Environmental Management System
7.3.6 Pollution Control
7.3.7 Green Packaging
7.3.8 Technology
7.3.9 Production Capacity
7.3.10 Strategic Alliance
7.4 Methodology
7.4.1 Decision Makers
7.4.2 Alternatives and Criteria
7.4.3 Data Collection
7.4.4 Evaluation Model: PROMETHEE Method
7.4.5 Preference Function
7.5 Implementation
7.6 Geometrical Representations
7.6.1 Graphical Analysis for Interactive Aid (GAIA) Plane
7.6.2 PROMETHEE Network
7.6.3 Walking Weights
7.7 Conclusion
References
8. Adopting Green Logistics in India: A Comparison of Manufacturing Sectors Using p,q-Quasirung Orthopair Fuzzy Group Decision Analysis
Sanjib Biswas, Sriparna Guha and Aparajita Sanyal
8.1 Introduction
8.2 Theoretical Background
8.2.1 The Task and Technology Fit (TTF) Model
8.2.2 p,q-Quasirung Orthopair Fuzzy Sets
8.3 Literature Investigation
8.4 Research Methodology
8.5 Findings
8.5.1 Comparison with Other MCDM Models
8.5.2 Sensitivity Analysis
8.6 Discussion
8.6.1 Research Implications
8.7 Conclusion
8.7.1 Limitations and Future Scopes
Declarations
Authors’ Contributions
References
9. Distance-Based Consensus in a Fuzzy Soft Group Decision‑Making and Its Application on Sustainable Supplier Selection
Soumi Manna, Tanushree Mitra Basu and Shyamal Kumar Mondal
9.1 Introduction
9.2 Some Basic Relevant Notions
9.2.1 Fuzzy Set Theory
9.2.2 Soft Set Theory
9.2.3 Fuzzy Soft Set Theory
9.3 Fuzzy Soft Set-Based Group Decision-Making
9.3.1 Mathematical Illustration of the Problem
9.3.2 Optimality Criteria
9.3.3 Solution Framework
9.3.3.1 Algorithm I (Measurement of Comprehensive Consensus Level)
9.3.3.2 Algorithm II (Improvement Process of Comprehensive Consensus Level)
9.3.3.3 Selection Process for Choosing the Best Alternative
9.4 A Case Study on Sustainable Supplier Selection in a Textile Industry
9.5 Comparative Discussion
9.6 Conclusion
Limitations
Future Research Work
9.7 Acknowledgement
References
Part III: Sustainable Development and Environmental Decision-Making
10. A HyF-VIKOR Method for Sustainable Transportation via a Novel Distance Measure
Palash Dutta and Alakananda Konwar
10.1 Introduction
10.2 Literature Review
10.2.1 Distance Measure
10.2.2 Application of the VIKOR Method
10.2.3 Electric Vehicle Charging Site (EVCS) Selection
10.2.4 Motivation of the Study
10.2.5 Objectives of the Study
10.3 Preliminaries
10.4 A Novel HyF Distance Measure
10.4.1 Existing Distance Measures
10.4.2 Proposed Novel Distance Measure on HyFSs
10.4.3 Comparative Study
10.5 Hyperbolic Fuzzy VIKOR Method
10.6 Application in Sustainable Transportation
10.6.1 Case Study on Electric Vehicle Charging Station Site Selection
10.6.2 Comparative Analysis and Discussion
10.7 Conclusion and Future Studies
References
6. Fuzzy Machine Learning Integrated MCDM in Sustainable
Supplier Selection of Healthcare Industries
Nivetha Martin and Akbar Rezaei
6.1 Introduction
6.2 Review of Earlier Works
6.2.1 MCDM in Health Care Supplier Selection
6.2.2 Decision Trees in Health Care Systems and Supplier Selection
6.2.3 Existing Gaps and Novel Contributions
6.3 Preliminaries
6.3.1 Multi-Criteria Decision Making (MCDM)
6.3.2 Key Components of MCDM
6.3.3 General Steps in MCDM
6.3.4 Fuzzy Set
6.3.5 Fuzzy Logic
6.3.6 Linguistic Variable
6.4 Methodology
6.4.1 Phase I: Fuzzy Decision Trees in Criterion Reduction
6.4.2 Phase II: Fuzzy AROMAN in Ranking the Supplier
6.5 Hybrid Decision-Making Approach in Supplier Selection
6.5.1 Problem Description
6.5.2 Phase I Fuzzy Decision Trees in Criterion Reduction
6.5.3 Phase II: Fuzzy AROMAN in Ranking the Supplier
6.6 Results and Discussions
6.7 Industrial Implications
Conclusion
Bibliography
7. Green Supplier Selection Using a Preference Method: Complete Pre-Order and Geometrical Representation Analyses
Lazim Abdullah, Hazwani Hashim, Noor Azzah Awang, Norsyahidah Zulkifli and Nor Liyana Amalini Mohd Kamal
7.1 Introduction
7.2 Preference Functions
7.3 Criteria of Green Suppliers
7.3.1 Cost
7.3.2 Quality
7.3.3 Delivery
7.3.4 Services
7.3.5 Environmental Management System
7.3.6 Pollution Control
7.3.7 Green Packaging
7.3.8 Technology
7.3.9 Production Capacity
7.3.10 Strategic Alliance
7.4 Methodology
7.4.1 Decision Makers
7.4.2 Alternatives and Criteria
7.4.3 Data Collection
7.4.4 Evaluation Model: PROMETHEE Method
7.4.5 Preference Function
7.5 Implementation
7.6 Geometrical Representations
7.6.1 Graphical Analysis for Interactive Aid (GAIA) Plane
7.6.2 PROMETHEE Network
7.6.3 Walking Weights
7.7 Conclusion
References
8. Adopting Green Logistics in India: A Comparison of Manufacturing Sectors Using p,q-Quasirung Orthopair Fuzzy Group Decision Analysis
Sanjib Biswas, Sriparna Guha and Aparajita Sanyal
8.1 Introduction
8.2 Theoretical Background
8.2.1 The Task and Technology Fit (TTF) Model
8.2.2 p,q-Quasirung Orthopair Fuzzy Sets
8.3 Literature Investigation
8.4 Research Methodology
8.5 Findings
8.5.1 Comparison with Other MCDM Models
8.5.2 Sensitivity Analysis
8.6 Discussion
8.6.1 Research Implications
8.7 Conclusion
8.7.1 Limitations and Future Scopes
Declarations
Authors’ Contributions
References
9. Distance-Based Consensus in a Fuzzy Soft Group Decision‑Making and Its Application on Sustainable Supplier Selection
Soumi Manna, Tanushree Mitra Basu and Shyamal Kumar Mondal
9.1 Introduction
9.2 Some Basic Relevant Notions
9.2.1 Fuzzy Set Theory
9.2.2 Soft Set Theory
9.2.3 Fuzzy Soft Set Theory
9.3 Fuzzy Soft Set-Based Group Decision-Making
9.3.1 Mathematical Illustration of the Problem
9.3.2 Optimality Criteria
9.3.3 Solution Framework
9.3.3.1 Algorithm I (Measurement of Comprehensive Consensus Level)
9.3.3.2 Algorithm II (Improvement Process of Comprehensive Consensus Level)
9.3.3.3 Selection Process for Choosing the Best Alternative
9.4 A Case Study on Sustainable Supplier Selection in a Textile Industry
9.5 Comparative Discussion
9.6 Conclusion
Limitations
Future Research Work
9.7 Acknowledgement
References
Part III: Sustainable Development and Environmental Decision-Making
10. A HyF-VIKOR Method for Sustainable Transportation via a Novel Distance Measure
Palash Dutta and Alakananda Konwar
10.1 Introduction
10.2 Literature Review
10.2.1 Distance Measure
10.2.2 Application of the VIKOR Method
10.2.3 Electric Vehicle Charging Site (EVCS) Selection
10.2.4 Motivation of the Study
10.2.5 Objectives of the Study
10.3 Preliminaries
10.4 A Novel HyF Distance Measure
10.4.1 Existing Distance Measures
10.4.2 Proposed Novel Distance Measure on HyFSs
10.4.3 Comparative Study
10.5 Hyperbolic Fuzzy VIKOR Method
10.6 Application in Sustainable Transportation
10.6.1 Case Study on Electric Vehicle Charging Station Site Selection
10.6.2 Comparative Analysis and Discussion
10.7 Conclusion and Future Studies
References
Part V: Healthcare and Medical Applications
16. A Novel Approach for Evaluating Complicated Pregnancies via Picture Fuzzy Distance Analysis
Chukwudi Obinna Nwokoro, Paul Augustine Ejegwa, Felix C.C. Wekere, Nneka Gabriel Job, Kelechi Cynthia Nwokoro and Faith-Michael Uzoka
16.1 Introduction
16.1.1 Background of the Study
16.1.2 Literature Review
16.1.3 Motivation and Contributions
16.2 Preliminaries
16.2.1 Picture Fuzzy Sets
16.2.2 Proposed Picture Fuzzy Distance Measure
16.2.2.1 Merits of the Proposed Measure
16.2.3 Established Distance Measures
16.3 Maternal Outcomes Analysis Based on Picture Fuzzy Distance Measures
16.4 Result and Discussion
16.4.1 Sensitivity Analysis
16.4.1.1 Medical Implications and Recommendations
16.4.2 Comparative Result
16.5 Conclusion
References
17. Investigating the Digital Healthcare System Based on Knowledge-Based Systems
Sanjeev Kumar, Geeta Tiwari, Anil Kumar, Prateek Kumar Singhal and Neeraj Tiwari
17.1 Introduction
17.1.1 Overview of Digital Healthcare Systems
17.1.2 Role of Knowledge-Based Systems in Modern Healthcare
17.1.3 Research Gaps and Merits of the Study
17.1.4 Major Contributions of the Study
17.1.5 Objectives and Scope of the Chapter
17.2 Foundation of Knowledge-Based Systems
17.2.1 Definition and Characteristics of Knowledge-Based Systems
17.2.2 Key Components of Knowledge-Based Systems
17.2.3 Types of Knowledge-Based Systems in Healthcare
17.3 Applications of Knowledge-Based Systems in Digital Healthcare
17.3.1 Disease Diagnosis and Treatment Recommendations
17.3.2 Personalized Patient Care and Management in Digital Healthcare Systems
17.3.3 Decision Support for Medical Professionals
17.3.4 Predictive Analytics in Healthcare
17.4 Technologies Enabling Knowledge-Based Healthcare Systems
17.4.1 Artificial Intelligence and Machine Learning
17.4.2 Natural Language Processing (NLP)
17.4.3 Big Data Analytics and Cloud Computing
17.4.4 Internet of Medical Things (IoMT)
17.5 Challenges in Implementing Knowledge-Based Digital Healthcare Systems
17.5.1 Data Privacy and Security Concerns
17.5.2 Handling Uncertainty and Incomplete Knowledge
17.5.3 Integration with Existing Healthcare Infrastructure
17.5.4 Ethical and Legal Implications
17.6 Case Studies and Real-World Applications
17.6.1 Implementations of KBS in Healthcare
17.6.2 Learning from Global Deployments
17.7 Future Directions and Innovations
17.7.1 Advancements in AI-Driven Knowledge Systems
17.7.2 Enhancing Interoperability and Scalability
17.7.3 Potential for Improved Patient Outcomes
17.8 Conclusion
References
Part VI: Education and Public Services
18. DSS for Educational Resource Management Based on a Multicriteria Approach: A Case Study for Madagascar National Education
Rôlin Gabriel Rasoanaivo and Pascale Zaraté
18.1 Introduction
18.2 Methodologies
18.2.1 Multi-Criteria Decision-Making Methods
18.2.1.1 Centroidous Method
18.2.1.2 CoCoFISo Method
18.2.2 Rank Aggregation Methods: MIRA
18.3 MADREN: From Modeling to Implementation
18.4 MADREN Experiment
18.4.1 Survey Data
18.4.2 Results and Analysis
18.4.2.1 DREN Evaluation Results by School Type and Location
18.4.2.2 DREN Rank Aggregating by MIRA Method 5
18.5 Discussion and Conclusion
18.5.1 Discussion
18.5.2 Conclusion
References
Part VII: Advanced Fuzzy and Soft Computing Techniques
19. Interval Valued Fermatean Fuzzy Emergency Decision-Making
Method with Weighted Distance-Based Approximation and Similarity Measures
Murat Kirişci
19.1 Introduction
19.1.1 Research Motivation
19.1.2 Objective of Study
19.1.3 Originality and Gaps
19.1.4 Contribution
19.2 Literature
19.3 Preliminaries
19.3.1 Similarity Measures
19.4 Proposed Methodology
19.5 Scenario-Based Application
19.6 Discussion
19.6.1 Comparative Analysis
19.6.2 Advantages of the Method
19.6.3 Limitations
19.7 Conclusion
References
20. An Innovative MADM Framework Utilizing Some Dombi Shapley Choquet Integral Operators with Picture Fuzzy Information
Pankaj Kakati
20.1 Background Study
20.2 Essential Concepts
20.3 The Innovative PFDGSCI and PFDGSGCI
20.3.1 Properties of PFDGSCI and PFDGSGCI
20.4 A Novel MADM Approach Based on PFDGSCI and PFDGSGCI
20.5 Detailed Depiction
20.5.1 A Comparative Analysis
20.5.2 Advantages of the Proposed Study
20.6 Conclusions
Acknowledgments
References
21. Fuzzy Approach to Replacement Problems Using Pentagonal
Fuzzy Numbers
Krishnaveni G., Sudha G., Melita Vinoliah E. and Balaganesan M.
21.1 Introduction
21.1.1 Research Gaps in Fuzzy Replacement Models
21.1.2 Motivation and Novelties of the Study
21.2 Operational Framework and Techniques
21.2.1 Pentagonal Fuzzy Number and Its Alternate Representation
21.2.2 Arithmetic Operations of PFNs
21.2.3 Ranking of PFN
21.3 Replacement Model with Pentagonal Fuzzy Numbers
21.3.1 Procedure to Find the Pentagonal Fuzzy Equipment Replacement Time
21.3.2 Process Flow for Determining Pentagonal Fuzzy Equipment Replacement Timing
21.4 Numerical Simulation
21.4.1 Results and Discussion
21.4.2 Comparative Analysis
21.5 Conclusion
References
Index

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