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Multi-Attribute Decision-Making in Industrial Engineering

Edited by Pranav Charkha, Vijayshri Mahobiya, Santosh Jaju, Vinit Gupta and Sandip Kunar
Series: Advances in Production Engineering
Copyright: 2026   |   Expected Pub Date:01/30/2026
ISBN: 9781119563495  |  Hardcover  |  
278 pages

One Line Description
Master the complexities of modern engineering management with this practical guide to multiattribute decision-making, providing the advanced models and multi-criteria frameworks needed to resolve conflicting objectives and optimize industrial outcomes.

Audience
Academics, researchers, industrial engineers, operations researchers, supply chain analysts, project managers, and decision scientists who seek to enhance decision-making processes using structured multi-attribute methodologies.

Description
Multi-attribute decision-making (MADM) is a foundational area within contemporary decision science. Its theoretical frameworks and methodological approaches have been widely applied across disciplines such as industrial engineering, military affairs, economics, and management. This book provides a comprehensive and practical guide to this critical area in modern engineering management. Covering topics such as decision theory, multi-criteria analysis, and advanced decision-making models, the book explores how MADM techniques can be applied to solve real-world problems related to production planning, supply
chain management, quality control, and operational efficiency. Whether dealing with conflicting objectives, limited resources, or complex trade-offs, this book serves as a valuable resource to enhance decisionmaking skills and optimize outcomes in industrial processes. With a clear focus on practical applications,
it bridges the gap between theory and practice, making it ideal for both academic and professional use in the fields of industrial engineering and operations management.
Readers will find the volume:
• Covers key Multiple Attribute Decision-Making methods like AHP, TOPSIS, and VIKOR with realworld industrial applications;
• Provides step-by-step examples, making complex concepts easy to understand and apply;
• Enhances productivity and accuracy in process optimization and resource allocation;
• Includes case studies to bridge theory and practical implementation.

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Author / Editor Details
Pranav Charkha, PhD is a Professor in the School of Engineering and Technology at Pimpri Chinchwad University, Pune, Maharashtra, India. He has published more than 40 research papers in international journals, five book chapters, four books, and five copyrights. His research focuses on supply chain management, additive manufacturing, and world-class manufacturing for Industry 4.0.

Vijayshri Mahobiya, PhD is the Head of the Mechanical Engineering Department at the Wainganaga College of Engineering and Management, Nagpur, Maharashtra, India. She has authored numerous papers in reputed international journals and holds two patents, showcasing her contributions to practical advancements in the field. Her research focus lies in the application of lean manufacturing principles to optimize testing laboratories.

Santosh Jaju, PhD is a Professor in the Department of Mechanical Engineering at the G.H. Raisoni College of Engineering, Nagpur, Maharashtra, India, with more than 22 years of teaching experience. He has to his credit more than 100 research papers published in national and international journals and conferences, one book, and six book chapters. His research focuses on quality cost, service quality, lean manufacturing, productivity improvement techniques, and industrial engineering.

Vinit Gupta, PhD is an Associate Professor at P.P. Savani University, Surat, Gujarat, India, with more than 14 years of experience. He has contributed several technical papers to prestigious peer-reviewed journals, published a book and several other book chapters, and is an active consultant to nearby industries. His
major research areas include materials and material applications and mechanism design and control.

Sandip Kunar, PhD is an Associate Professor in the Department of Mechanical Engineering at Aditya Engineering College, Andhra Pradesh, India. He has published more than 60 research papers in various reputed international journals, national and international conference proceedings, 60 book chapters, 20
books, and five patents. His research interests include non-conventional machining processes, micromachining processes, advanced manufacturing technology, and industrial engineering.

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Table of Contents
Preface
Acknowledgments
1. Introduction to Multi-Attribute Decision-Making Applications
Himanshu M. Shukla, Mangesh P. Joshi and Anant A. Deogaonkar
1.1 Introduction
1.2 Multi Attribute Decision Making: Key Concepts
1.3 Limitations of Traditional MADM Techniques
1.4 Emerging Trends in MADM
1.5 Integration of MADM in Emerging Application Areas
1.6 AIML Integration in MADM
1.7 Application of MADM in Big Data and Real-Time Analytics
1.8 Hybrid Decision Models through Integration of MADM
1.9 Sustainability and Environmental Impact in MADM
1.10 Human-Centric and Ethical Decision-Making in MADM
1.11 Blockchain and Distributed Ledger Technologies in MADM
1.12 Interdisciplinary Collaboration in MADM
1.13 Conclusion and Future Directions
References
2. COMPARALT – A Novel Approach for Multi-Criteria Decision Making
Atul Borade, Sudhanshu Ranjan Singh and Abhijeet K. Digalwar
2.1 Introduction
2.2 COMPARALT (Comparing and Ranking the Alternatives)
2.2.1 Steps of COMPARALT
2.3 Real-World Applications
2.4 Results and Discussion
2.5 Conclusion
References
3. Multi-Attribute Decision-Making Applications in Optimizing Logistics and Supply Chain Management for Energy Storage Systems Techniques
Ravikant Nanwatkar, Deepak Watvisave, Pravin Nitnaware and Aparna Bagde
3.1 Introduction
3.1.1 Problem Statement
3.1.2 Research Objectives
3.1.3 Research Gap
3.2 Literature Review
3.3 MADM Techniques for Logistics and Supply Chain
3.3.1 AHP (Analytic Hierarchy Process)
3.3.2 TOPSIS Stands for Technique of Order Preference Similarity to Ideal Solution
3.3.3 Multiple Criteria Decision Analysis (MCDA)
3.3.4 VIKOR (Vlse Kriterijumska Optimizacija Ia Compromise Solution)
3.3.5 Other MADM Tools
3.3.5.1 OPTIMET
3.3.5.2 SMART (Simple Multi-Attribute Rating Technique)
3.3.5.3 BWM (Best-Worst Method)
3.4 Application Models and Framework
3.5 Challenges and Limitations
3.6 Future Directions
3.7 Conclusion
Bibliography
4. Enhancing Casting Quality through Machine Learning and Artificial Intelligence: Predicting and Controlling Defects in Sand Castings
Anjul Rai, Shubhrata Nagpal, Vijayshri Mahobia and Vishal Rajput
4.1 Introduction
4.2 Artificial Intelligence and Machine Learning Applications in Sand Casting
4.3 Machine Learning and Artificial Intelligence in Sand Castings for Defect Prediction
4.4 Evaluation of Classification Mode
4.5 Conclusions
References
5. Developing Analytical Hierarchy Process Framework as Multi Criteria Decision Making Tool for Analysing Green Supply Chain Management Practices
Prashantkumar Bajaj and Sanjay P. Shekhawat
5.1 Introduction
5.1.1 Multi Criteria Decision Making (MCDM) Overview
5.1.2 Qualitative Approach
5.1.3 Quantitative Approach
5.1.4 AHP and Its Frame Work
5.1.5 Key Characteristics of AHP
5.2 Literature Review
5.2.1 Multi Criteria Decision Making Method (MCDM)
5.2.2 Research Gaps
5.3 Analytic Hierarchy Process
5.3.1 Analytic Hierarchy Process Framework Analysis
5.3.2 Selection of Factors Based on AHP Analysis
5.4 Conclusion and Future Outlook
References
6. Multi-Attribute Decision Making in Human Factors and Ergonomics
Mangesh Joshi, Himanshu Shukla and Sanjay Nikhade
6.1 Introduction
6.1.1 Scope of the Chapter
6.2 Role of MADM in Human Factors and Ergonomics
6.3 Key Applications of MADM in Ergonomics
6.3.1 Workplace/Workstation Design
6.3.2 Product Usability Assessment
6.3.3 Safety and Risk Management
6.3.4 Human-Machine Interaction
6.3.5 Healthcare Ergonomics
6.4 Potential Challenges of MADM in Ergonomics
6.5 Conclusion
References
7. Measuring Service Quality Using Multi Attribute Decision Making-Case of Healthcare Sector
Ganesh N. Akhade, R. R. Lakhe, S. B. Jaju and Mangesh Kale
7.1 Introduction
7.2 Review of Literature
7.3 Research Methodology
7.3.1 Survey Location
7.3.2 Survey Instrument
7.3.3 Sampling and Sampling Size
7.3.4 Sample Profile
7.4 Result
7.4.1 Gap Analysis
7.4.2 Reliability Analysis
7.4.3 Impact of Service Quality Dimensions
7.5 Conclusion
7.6 Managerial Implications
References
8. Development and Implementation of a Quality Cost Management System in Advanced Manufacturing Environments
Yogesh Joshi, Sanjay Mantri, Pranav Charkha and Santosh Jaju
8.1 Introduction
8.2 COQ Practices in Advanced Manufacturing Environment
8.3 Methodology Used
8.3.1 Mathematical Tools Used for Analysis
8.4 Development and Implementation of Quality Cost Management System in Industry
8.4.1 Methodology for Case Study
8.5 QCMS Development
8.5.1 Pre-Processor
8.5.2 Processor
8.5.3 Post Processor
8.6 Rationale of Study
8.6.1 Manufacturing Process
8.6.2 COQ Model and Cost Categories
8.7 Extension of Study Period
8.8 Data Collection
8.9 Trend Analysis
8.10 Conclusion
Bibliography
9. Application of Analytic Hierarchy Process (AHP) for Identifying Optimal Lean Tools in Testing Laboratories
Vijayshri Mahobiya, Anjul Rai, Somdatta Karanjekar, Durwesh Jhodkar and Pranav Charkha
9.1 Introduction
9.2 Literature Review
9.3 Methodology Used
9.3.1 Identification of Lean Wastes for Testing Laboratory
9.3.2 Identification of Lean Tools for Testing Laboratory
9.3.3 Selections of Criteria
9.4 Data Collection
9.5 Result and Conclusion
References
10. Identification and Prioritizing the Performance Metrics for Textile Supply Chain: Multi-Attribute Decision Making Approach
Pranav Charkha, Sarita Charkha, Santosh Jaju and Sandip Kunar
10.1 Introduction
10.1.1 Aim of the Study
10.2 Brief Review of SCPM System
10.2.1 Introduction
10.2.1.1 Identification of Performance Parameters and Development of Rough
SCPM Framework
10.2.2 Modified Delphi Method
10.2.2.1 Development of First Round Questions
10.2.2.2 Development of Second Round Questionnaire
10.2.2.3 Development of Third Round of Questionnaire
10.2.3 Identification of Case Company
10.2.3.1 Respondent Identification from Case Companies
10.3 Application of MCDM Technique-Analytical Hierarchy Process
10.4 For Case Company
10.5 Implication from Case Studies
10.5.1 Analysis of Case Company 1
10.6 Conclusion
References
Appendix A: Performance Measures Identified in Literature Review and Categorized as Quantitative & Qualitative
Appendix B: Priority Weights for Sub-Attribute Under Each Perspective with Respect to Three Supply Chain Cyclic Processes in Case Company
11. Application of Multi-Attribute Decision-Making in Manufacturing Systems
Ravi Shankar Rai, Alok Kumar and Jonathan Joseph
11.1 Introduction
11.1.1 Overview of Multi-Attribute Decision Making
11.1.2 Importance of MADM in Manufacturing Systems
11.1.3 Objectives and Chapter Structure
11.2 Fundamentals of MADM in Manufacturing
11.2.1 Definitions and Key Concepts
11.2.2 Types of MADM Techniques
11.2.3 Criteria for Manufacturing System Decision Making
11.3 Classification of MADM Techniques in Manufacturing Systems
11.4 Applications of MADM in Manufacturing Systems
11.4.1 Production Planning and Scheduling
11.4.2 Facility Layout and Location
11.4.3 Supply Chain and Logistics
11.4.4 Quality Management
11.4.5 Sustainability and Green Manufacturing
11.5 Case Studies and Real-World Applications
11.6 Challenges and Limitations of MADM in Manufacturing Systems
11.6.1 Data Collection and Criteria Weighting
11.6.2 Handling Subjective Judgments
11.6.3 Computational Complexity
11.7 Emerging Trends and Future Directions
11.7.1 MADM Based on Integrating Artificial Intelligence
11.7.2 IoT Based Real-Time Decision
11.7.3 Hybrid MADM Models for Complex Systems
11.8 Conclusion
References
Index

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