Soft Computing for Smart Environments: Techniques and Applications

This book applies industrial engineering and computational intelligence to demonstrate intelligent machines that solve real-world problems in various smart environments. It discusses AI-based approaches and optimization techniques, decision analytics, and data science in applying soft computing techniques to smart environments, such as smart and sustainable cities, disaster response systems, and smart campuses.

Format: Hardback
Length: 262 pages
Publication date: 05 April 2023
Publisher: Taylor & Francis Ltd

This comprehensive book delves into the realm of intelligent machines, harnessing the power of industrial engineering and computational intelligence to solve real-world problems in diverse smart environments. It presents fundamental concepts and cutting-edge advancements in multi-criteria decision-making (MCDM) techniques, specifically tailored for smart environments. While managers and engineers frequently employ multi-criteria analysis for complex decision-making, many core problems remain challenging to model mathematically or have yet to be explored. Recognizing this gap, the book not only discusses AI-based approaches but also encompasses a wide range of optimization techniques, decision analytics, and data science, all applied within the context of soft computing techniques for specific smart environments, such as smart and sustainable cities, disaster response systems, and smart campuses. As a definitive resource, this state-of-the-art book will be invaluable for undergraduate and graduate students, researchers, practitioners, and decision-makers seeking advanced MCDM techniques to address management and engineering challenges in smart environments.

Introduction:
The world is rapidly evolving, driven by technological advancements and the increasing demand for smarter solutions to real-world problems. In this context, industrial engineering and computational intelligence play pivotal roles in developing intelligent machines that can solve complex challenges in various smart environments. This book aims to bridge the gap between these two disciplines and showcase intelligent machines that can effectively address the needs of smart cities, disaster response systems, and smart campuses.
Industrial Engineering and Computational Intelligence:
Industrial engineering focuses on optimizing processes, systems, and operations to improve efficiency, productivity, and quality. It employs various techniques, such as process modeling, simulation, and optimization, to address complex problems in manufacturing, logistics, and service industries. Computational intelligence, on the other hand, leverages advanced algorithms and machine learning to analyze large datasets, make predictions, and solve complex problems. It encompasses a wide range of techniques, including artificial neural networks, genetic algorithms, and fuzzy logic.
Multi-Criteria Decision-Making (MCDM):
MCDM is a fundamental concept in industrial engineering and computational intelligence that involves the evaluation of multiple criteria to make decisions. It is particularly relevant in smart environments where decision-makers need to consider a wide range of factors, such as environmental impact, economic feasibility, social welfare, and user preferences. This book provides an in-depth exploration of MCDM techniques, including the development of decision criteria, the application of optimization algorithms, and the evaluation of decision alternatives.
Smart Environments:
Smart environments are characterized by the integration of information and communication technologies (ICT) with physical infrastructure to enhance efficiency, sustainability, and user experience. Smart cities, for instance, use ICT to optimize traffic flow, reduce energy consumption, and improve public services. Disaster response systems rely on ICT to coordinate emergency response efforts and provide real-time information to disaster victims. Smart campuses use ICT to enhance learning experiences, improve administrative efficiency, and promote sustainability.
Optimization Techniques:
In smart environments, optimization techniques play a crucial role in addressing complex problems. These techniques include linear programming, nonlinear programming, dynamic programming, and stochastic programming. Linear programming is used to optimize resource allocation and scheduling problems, while nonlinear programming is used to handle complex optimization problems with non-linear constraints. Dynamic programming is used to optimize decision-making processes in real-time, while stochastic programming is used to handle uncertainty and risk in decision-making.
Decision Analytics:
Decision analytics is a branch of computational intelligence that involves the use of statistical and analytical methods to analyze data and make informed decisions. It is particularly useful in smart environments where decision-makers need to rely on data-driven insights to make timely and effective decisions. This book provides an introduction to decision analytics, including data mining, data visualization, and machine learning techniques.
Data Science:
Data science is a multidisciplinary field that combines statistics, mathematics, computer science, and domain knowledge to analyze and interpret data. It plays a critical role in smart environments, where large amounts of data are generated and analyzed to gain insights and make informed decisions. This book provides an introduction to data science, including data collection, data cleaning, data analysis, and data visualization techniques.
Applications of Soft Computing Techniques:
Soft computing techniques, such as fuzzy logic, neural networks, and genetic algorithms, are widely used in smart environments to address complex problems. These techniques are characterized by their ability to handle uncertainty, imprecision, and ambiguity in decision-making. This book discusses the application of soft computing techniques in smart environments, including smart grids, smart transportation, and healthcare.
Conclusion:
In conclusion, this comprehensive book delves into the realm of intelligent machines, harnessing the power of industrial engineering and computational intelligence to solve real-world problems in diverse smart environments. It presents fundamental concepts and cutting-edge advancements in multi-criteria decision-making techniques, specifically tailored for smart environments. By discussing AI-based approaches, optimization techniques, decision analytics, and data science, the book provides a comprehensive understanding of advanced MCDM techniques for management and engineering in relation to smart environments. As a definitive resource, this state-of-the-art book will be invaluable for undergraduate and graduate students, researchers, practitioners, and decision-makers seeking advanced MCDM techniques to address management and engineering challenges in smart environments.

Weight: 516g
Dimension: 158 x 235 x 21 (mm)
ISBN-13: 9781032413532