Multi-Attribute Decision-Making in Industrial Engineering

1. Auflage 2026
ISBN: 978-1-394-46503-3
Verlag: John Wiley & Sons Inc

Buch, Englisch, 288 Seiten

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

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 decision-making 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 real-world 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.

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.

Autoren/Hrsg.

Charkha, Pranav

Mahobiya, Vijayshri

Jaju, Santosh

Gupta, Vinit

Kunar, Sandip

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Wirtschaftswissenschaften Betriebswirtschaft Management Projektmanagement
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Verfahrenstechnik, Chemieingenieurwesen
• Wirtschaftswissenschaften Betriebswirtschaft Betriebswirtschaft: Theorie & Allgemeines
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung

Inhaltsverzeichnis

Preface xi
Acknowledgments xv

1 Introduction to Multi-Attribute Decision-Making Applications 1
Himanshu M. Shukla, Mangesh P. Joshi and Anant A. Deogaonkar

1.1 Introduction 2
1.2 Multi Attribute Decision Making: Key Concepts 4
1.3 Limitations of Traditional MADM Techniques 5
1.4 Emerging Trends in MADM 7
1.5 Integration of MADM in Emerging Application Areas 8
1.6 AIML Integration in MADM 10
1.7 Application of MADM in Big Data and Real-Time Analytics 11
1.8 Hybrid Decision Models through Integration of MADM 12
1.9 Sustainability and Environmental Impact in MADM 13
1.10 Human-Centric and Ethical Decision-Making in MADM 14
1.11 Blockchain and Distributed Ledger Technologies in MADM 16
1.12 Interdisciplinary Collaboration in MADM 17
1.13 Conclusion and Future Directions 17

2 COMPARALT – A Novel Approach for Multi-Criteria Decision Making 21
Atul Borade, Sudhanshu Ranjan Singh and Abhijeet K. Digalwar

2.1 Introduction 22
2.2 COMPARALT (Comparing and Ranking the Alternatives) 24
2.3 Real-World Applications 32
2.4 Results and Discussion 38
2.5 Conclusion 44

3 Multi-Attribute Decision-Making Applications in Optimizing Logistics and Supply Chain Management for Energy Storage Systems Techniques 49
Ravikant Nanwatkar, Deepak Watvisave, Pravin Nitnaware and Aparna Bagde

3.1 Introduction 50
3.2 Literature Review 57
3.3 MADM Techniques for Logistics and Supply Chain 60
3.4 Application Models and Framework 66
3.5 Challenges and Limitations 68
3.6 Future Directions 69
3.7 Conclusion 71

4 Enhancing Casting Quality through Machine Learning and Artificial Intelligence: Predicting and Controlling Defects in Sand Castings 75
Anjul Rai, Shubhrata Nagpal, Vijayshri Mahobia and Vishal Rajput

4.1 Introduction 76
4.2 Artificial Intelligence and Machine Learning Applications in Sand Casting 77
4.3 Machine Learning and Artificial Intelligence in Sand Castings for Defect Prediction 78
4.4 Evaluation of Classification Mode 84
4.5 Conclusions 85

5 Developing Analytical Hierarchy Process Framework as Multi Criteria Decision Making Tool for Analysing Green Supply Chain Management Practices 89
Prashantkumar Bajaj and Sanjay P. Shekhawat

5.1 Introduction 90
5.2 Literature Review 95
5.3 Analytic Hierarchy Process 98
5.4 Conclusion and Future Outlook 107

6 Multi-Attribute Decision Making in Human Factors and Ergonomics 111
Mangesh Joshi, Himanshu Shukla and Sanjay Nikhade

6.1 Introduction 112
6.2 Role of MADM in Human Factors and Ergonomics 114
6.3 Key Applications of MADM in Ergonomics 115
6.4 Potential Challenges of MADM in Ergonomics 127
6.5 Conclusion 128

7 Measuring Service Quality Using Multi Attribute Decision Making-Case of Healthcare Sector 133
Ganesh N. Akhade, R. R. Lakhe, S. B. Jaju and Mangesh Kale

7.1 Introduction 134
7.2 Review of Literature 135
7.3 Research Methodology 136
7.4 Result 141
7.5 Conclusion 144
7.6 Managerial Implications 144

8 Development and Implementation of a Quality Cost Management System in Advanced Manufacturing Environments 151
Yogesh Joshi, Sanjay Mantri, Pranav Charkha and Santosh Jaju

8.1 Introduction 152
8.2 COQ Practices in Advanced Manufacturing Environment 153
8.3 Methodology Used 154
8.4 Development and Implementation of Quality Cost Management System in Industry 156
8.5 QCMS Development 157
8.6 Rationale of Study 158
8.7 Extension of Study Period 162
8.8 Data Collection 162
8.9 Trend Analysis 163
8.10 Conclusion 166

9 Application of Analytic Hierarchy Process (AHP) for Identifying Optimal Lean Tools in Testing Laboratories 171
Vijayshri Mahobiya, Anjul Rai, Somdatta Karanjekar, Durwesh Jhodkar and Pranav Charkha

9.1 Introduction 172
9.2 Literature Review 172
9.3 Methodology Used 175
9.4 Data Collection 179
9.5 Result and Conclusion 189

10 Identification and Prioritizing the Performance Metrics for Textile Supply Chain: Multi-Attribute Decision Making Approach 195
Pranav Charkha, Sarita Charkha, Santosh Jaju and Sandip Kunar

10.1 Introduction 196
10.2 Brief Review of SCPM System 198
10.3 Application of MCDM Technique-Analytical Hierarchy Process 214
10.4 For Case Company 215
10.5 Implication from Case Studies 215
10.6 Conclusion 218
Appendix A: Performance Measures Identified in Literature Review and Categorized as Quantitative & Qualitative 222
Appendix B: Priority Weights for Sub-Attribute Under Each Perspective with Respect to Three Supply Chain Cyclic Processes in Case Company 226

11 Application of Multi-Attribute Decision-Making in Manufacturing Systems 227
Ravi Shankar Rai, Alok Kumar and Jonathan Joseph

11.1 Introduction 228
11.2 Fundamentals of MADM in Manufacturing 231
11.3 Classification of MADM Techniques in Manufacturing Systems 235
11.4 Applications of MADM in Manufacturing Systems 238
11.5 Case Studies and Real-World Applications 242
11.6 Challenges and Limitations of MADM in Manufacturing Systems 246
11.7 Emerging Trends and Future Directions 249
11.8 Conclusion 251

References 253
Index 259

Über Autor(innen)

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.