Highlights the contributions of different optimization techniques, decision analytics (predictive, prescriptive, and descriptive), multi-criteria decision making "Helps develop intelligent machines to provide solutions to real-world problems, which are not modelled or are too difficult to model mathematically in hospital management systems " Discusses machine learning-based analytics such as GAN networks, autoencoders, computational imaging, quantum computing will be rigorously applied to smart cloud computing Explores evolutionary algorithms that demonstrate their ability as robust approaches to cope with the fundamental steps of image processing, image analysis, and computer vision pipeline (e.g., restoration, segmentation, registration, classification, reconstruction, or tracking), Creates a bridge between Industrial Engineering concepts and Computational Intelligence for designing complex and convoluted hospital management problems

This book applies both industrial engineering and computational intelligence to demonstrate intelligent machines that solve real-world problems in various smart environments. The title presents fundamental concepts and the latest advances in Multi-Criteria Decision Making (MCDM) techniques and their application to smart environments. Though managers and engineers often use multi-criteria analysis in making complex decisions, many core problems are too difficult to model mathematically or have simply not yet been modelled. In response, as well as AI-based approaches, this book covers various optimization techniques, decision analytics and data science in applying soft computing techniques to a defined set of smart environments, including smart and sustainable cities, disaster response systems and smart campuses. This state-of-the-art book will be essential reading for both undergraduate and graduate students, researchers, practitioners and decision makers interested in advanced MCDM techniques for management and engineering in relation to smart environments.

Multi-Criteria Decision-Making for Renewable Energy: Methods, Applications, and Challenges brings together the latest fuzzy and soft computing methods, models, and algorithms as applied to the field of renewable energy and supported by specific application examples and case studies. The book begins by approaching renewable energy sources, challenges and factors that affect their development, as well as green renewable energy sites and the utilization of fuzzy multi-criteria decision-making (MCDM) techniques in these broad contexts, as well as utilization in addressing the various environmental, economic, and social barriers to ensuring the sustainability of energy resources. Detailed chapters focus on the application of multi-criteria decision-making methods for planning, modeling and prioritization in specific areas of renewable energy, including solar energy, wind farms, solar-powered hydrogen production plants, biofuel production, energy storage, hydropower, and marine energy. Finally, future opportunities and research directions are explored.