Multiple Criteria Decision Making: Techniques, Analysis and Applications

ISBN-13: 9789811674136 / Angielski / Twarda / 2022

Kulkarni, Anand J.

Opis

The book discusses state-of-the-art applications and methodologies of the Multiple Criteria Decision Making (MCDM) techniques and approaches. The book focuses on critical literature, underlying principles of methods and models, solution approaches, testing and validation, real-world applications, case studies, etc. The book helps evaluate strategic decision-making through advanced MCDM and integrated approaches of AI, big data, and IoT to provide realistic and robust solutions to the current problems. The book will be a guideline to the potential MCDM researchers about the choice of approaches for dealing with the complexities and modalities. The contributions of the book help readers to explore new avenues leading towards multidisciplinary research discussions. This book will be interesting for engineers, scientists, and students studying/working in the related areas.

Spis treści

A Fuzzy Based Multi-Criteria Decision Making Approach for the Selection of Digital Image Forensic Tools.- Analysis of fuzzy AHP and fuzzy TOPSIS methods for the prioritization of the software requirements.- MIVES - A multi-attribute value function based methodology for sustainability assessment.- DEX (Decision EXpert): A Qualitative Hierarchical Multi-Criteria Method.- Base-criterion Method.

O autorze

Anand J Kulkarni holds a Ph.D. in Distributed Optimization from Nanyang Technological University, Singapore, MS in Artificial Intelligence from the University of Regina, Canada, Bachelor of Engineering from Shivaji University, India, and Diploma from the MSBTE, Mumbai. He worked as a Post Doctorate Research Fellow at Odette School of Business, University of Windsor, Canada. Dr. Kulkarni has worked with Symbiosis International University, Pune, India for over six years. Currently, he is a Professor & Associate Director at the Institute of AI at MITWPU. His research interests include optimization algorithms, multi-agent systems, complex systems, swarm optimization, and self-organizing systems. Anand pioneered socio-inspired optimization methodologies such as Cohort Intelligence, Ideology Algorithm, Expectation Algorithm, and Socio Evolution & Learning Optimization Algorithm. He is the founder and chairman of Optimization and Agent Technology Research Lab and has over 70 research papers in journals and conferences, 04 authored and 08 edited books to his credit. Dr. Kulkarni is the lead editor for the Springer and Taylor and Francis book series. He regularly writes on Artificial Intelligence in several newspapers and magazines. Dr. Kulkarni has delivered expert research talks in many countries such as the USA, Canada, Singapore, Malaysia, India, and France.