Optimization Techniques for Sustainable Environment Under Uncertainty (Engineering Optimization: Methods and Applications) - Hardcover

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Dr. Ritu Arora is a Professor in the Department of Mathematics at Keshav Mahavidyalaya, University of Delhi. She has over 20 years of teaching experience and earned her Masters and Ph.D. in Mathematics from the University of Delhi. Her research focuses on Mathematical Programming/Optimization and its application to various allocation problems. Dr. Arora is the author of the book "Linear Programming and Game Theory" Narosa Publications and has published over 25 research papers in reputable National and International Journals.

Dr. Shalini Arora is a Professor in the Department of Mathematics and Head of the Applied Sciences and Humanities Department at Indira Gandhi Delhi Technical University for Women (IGDTUW). With over 20 years of teaching experience, she completed her Master's and Ph.D. in Mathematics from IIT Delhi. Her research interests include Mathematical Programming, Allocation Problems such as Transportation and Assignment Problems, Combinatorial optimization, and more. Dr. Arora has published over 30 research papers in both International and national journals. She is a recipient of the ‘Young Scientist Award’ by the SERC division of DST.

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The book's objective is to develop mathematical structures that can be applied to real-life problems with sustainable goals. It focuses on the impact of sustainable living on social, economic, and environmental aspects, aiming to create optimization techniques that minimize emissions and maximize green energy. These optimization problems may include sustainable transport, cities, economic development, living, and tourism. The book brings together researchers, academics, and professionals from various fields to find optimal or satisfactory solutions for various environmentally friendly sustainable problems. It aims to achieve this not only with existing optimization techniques, but also with novel approaches such as lexicographic optimization, heuristic approaches, DEA, and genetic algorithms. The goal is to develop practical algorithms and methods applicable to these problems under uncertain circumstances and explore the potential for improving the efficiency of existing algorithms.