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This book is designed and developed to link the theory, emerging methods, and applications. The theory part focuses on explaining the background and concept of multi-attribute decision-making. The emerging methods part elaborates on frequently adopted and emerging approaches in multi-attribute decision-making. The applications part provides in-depth illustrations of various methods using numerical examples. The book:
The book is primarily written for graduate students and academic researchers in the fields of mechanical engineering, engineering mathematics, mathematics, engineering design, production engineering, manufacturing engineering, and industrial engineering.
Rajesh S. Prabhu Gaonkar is presently working as a Professor at the Department of Mechanical Engineering at Goa College of Engineering, India. He served as Professor on Deputation at the School of Mechanical Sciences, Indian Institute of Technology Goa, India during 2018-2024. He has published over 125 research papers in various national and international conferences and journals and co-authored the book Fuzzy Reliability - Concepts and Applications (2007) and monograph Six-Sigma: A Key to Enterprise Excellence (2008). He has six patents to his credit as of writing. He is recipient of state and national level awards - the Goa state award for meritorius teacher in technical education by the Government of Goa (2015), Dr. J._M. Mahajan award by the Indian Institution of Industrial Engineering (2016-2017), the Gomantak TV excellence award for engineer's role in education (2024), the Frederick Taylor award by the Indian Institution of Industrial Engineering for significant contribution in the field of industrial engineering (2025). His teaching areas include industrial engineering, optimization methods, reliability engineering/ reliability-based design, maintenance engineering and management, operations management, system modeling, and simulation. His research areas include applications of evolving uncertainty theories in reliability, maintenance engineering, and multi-attribute decision-making.
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