This book addresses the different problems, practices, challenges and opportunities in sustainable resource management with the help of decision-making techniques to showcase the relevance of computational modelling approaches in sustainable management and Industry 4.0. It aims to address the inherent complexity of managing ecosystems, particularly with respect to involvement of multi-stakeholders, lack of information and uncertainties. Critical analyses are made to point out the need for, and propose a call to, a new way of thinking about sustainable resource management. This book will be useful for academicians, researchers, and industrialists in the field of industrial and production engineering.
Dr. Irfan Ali received B.Sc., M.Sc., M.Phil., and Ph.D. degrees from Aligarh Muslim University. He is currently working as an Assistant Professor with the Department of Statistics and Operations Research, Aligarh Muslim University. He received the Post Graduate Merit Scholarship Award during M.Sc. (statistics) and the UGC-BSR Scholarship awarded during the Ph.D. (statistics) programs in 2013. His research interests include applied statistics, survey sampling, supply chain networks and management, mathematical programming, and multiobjective optimization. He has supervised M.Sc., M.Phil., and Ph.D. students in operations research. He has completed a research project UGC–Start-Up Grant Project, UGC, New Delhi, India. He has published more than 75 research articles in reputed journals and serves as a Reviewer for several journals. He is currently editing two books to be published by Taylor France and Springer Nature. He is a Lifetime Member of various professional societies: Operational Research Society of India, Indian Society for Probability and Statistics, Indian Mathematical Society, and The Indian Science Congress Association. He delivered invited talks in several universities and institutions. He also serves as an Associate Editor for some journals.
Dr. Prasenjit Chatterjee is currently the Dean (Research and Consultancy) at MCKV Institute of Engineering, West Bengal, India. He has over 100 research papers in various international journals and peer-reviewed conferences including International Journal of Production Research, International Journal of Intelligent Systems, Expert Systems with Applications, Operations Management Research, Applied Soft Computing, Computers and Industrial Engineering, Socio-Economic Planning Sciences, Management Decision, Clean Technologies and Environmental Policy, Journal of Cleaner Production, Journal of Natural Fibers, Benchmarking: an International Journal, OPSEARCH, International Journal of Advanced Manufacturing Technology, Materials and Design, Robotics and Computer Integrated Manufacturing to name a few. He has authored and edited more than 15 books on intelligent decision-making, supply chain management, optimization techniques, risk and sustainability modelling. He has received numerous awards including Best Track Paper Award, Outstanding Reviewer Award, Best Paper Award, Outstanding Researcher Award and University Gold Medal. Dr. Chatterjee is the Editor-in-Chief of Journal of Decision Analytics and Intelligent Computing. He has also been the Guest Editor of several special issues in different SCIE / Scopus / ESCI (Clarivate Analytics) indexed journals. He is the Lead Series Editor of "Disruptive Technologies and Digital Transformations for Society 5.0", Springer. He is also the Lead Series Editor of "Concise Introductions to AI and Data Science", Scrivener - Wiley; AAP Research Notes on Optimization and Decision Making Theories; Frontiers of Mechanical and Industrial Engineering, Apple Academic Press, co-published with CRC Press, Taylor and Francis Group and “River Publishers Series in Industrial Manufacturing and Systems Engineering”. Dr. Chatterjee is one of the developers of two multiple-criteria decision-making methods called Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) and Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI).
Dr. Ali Akbar Shaikh is an Assistant Professor of Mathematics at The University of Burdwan, West Bengal, India. Earlier, he was a Postdoctoral Fellow at the School of Engineering and Sciences of Tecnológico de Monterrey, México. He has obtained the award SNI of level 1 (out of 0–3) presented by the National System of Researchers of México from Government of México in 2017. He obtained his PhD and MPhil in Mathematics from The University of Burdwan, and MSc in Applied Mathematics from University of Kalyani, India. He has published more than 55 research papers in different international journals of repute. His research interests include inventory control, interval optimisation, and particle swarm optimisation.
Dr. Neha Gupta is presently working as an Assistant Professor at Amity School of Business, AUUP. She obtained her M.Sc. (Operations Research), M.Phil. and PhD (Operations Research) from Aligarh Muslim University, Aligarh (INDIA). Her broad research area includes optimization and decision sciences. She is an Editorial Member of International Journal of Mathematics and Systems Science and International Journal of Data Mining, Modelling & Management, Inderscience Publications. Recently She has edited a special issue for Journal of Revenue and Pricing Management, Springer, and authored two books for international publishers like Taylor and Francis. She is a life member of the Operational Research Society of India and has more than 30 publications in journals of national and international repute. She has participated in several conferences of national and international level.
Dr. Ali AlArjani is an Assistant Professor at the Industrial Engineering Department at Prince Sattam bin Abdulaziz University, Alkharj, KSA. He did a Bachelor of Science degree in Mechanical Engineering from King Fahad University for Petroleum and Minerals, and master and PhD in Industrial Engineering from the USA. Dr. AlArjani’s research background is in industrial optimization, mathematical modelling, energy, data clustering analysis and machine learning classification and prediction applications.
