Название: Decision Support Systems in Smart Computing: Embracing Uncertainty and Challenges
Автор: Harish Garg
Издательство: Wiley-Scrivener
Год: 2026
Страниц: 695
Язык: английский
Формат: pdf
Размер: 52.4 MB

This essential guide bridges theoretical and real-world execution, presenting actionable tools, cutting-edge methodologies, and proven case studies needed to design resilient decision systems that excel in today’s complex, uncertain world.

In today’s data-driven world, uncertainty is constant, and smart decision-making demands systems that can adapt, reason, and respond. This book explores how modern fuzzy logic, soft computing, and multi-criteria decision-making methods are revolutionizing the way complex decisions are made in uncertain environments. It brings together global experts to present state-of-the-art methodologies like fuzzy AHP, TOPSIS, DEMATEL, picture fuzzy sets, and interval-valued fuzzy logic, applied across vital domains including smart cities, sustainable supply chains, digital healthcare, and urban infrastructure. Together, they explore both the theoretical foundations and real-world applications of intelligent decision-making methodologies.

At the core of these contributions lies a common theme:the imperative to model, manage, and make informed decisions under conditions that are inherently imprecise, ambiguous, or conflicting conditions that traditional models often struggle to address. Ranging from foundational theories to advanced sector-specific applications, the book bridges the gap between research and real-world problem-solving, covering a diverse range of industries from urban planning and logistics to healthcare and environmental sustainability, demonstrating the growing reach and versatility of intelligent decision systems. Whether optimizing green logistics, evaluating public health strategies, or enabling intelligent urban planning, readers will discover actionable tools, models, and case studies that respond to today’s challenges. Ideal for researchers, practitioners, and policy professionals, this book is an essential guide to designing robust, adaptive decision support systems that thrive in complexity and uncertainty.

The literature identifies a wide range of technologies employed in smart computing applications. These applications typically integrate sensor-based systems, the Internet of Things (IoT), edge computing, cyber-physical systems (CPS), Machine Learning, cognitive computing, Big Data analytics, cloud computing, blockchain and Artificial Intelligence (AI). Sensor-based systems (SBS) are computational systems that rely on sensors to collect data from their environment in real time. SBS processes sensory input—such as temperature, motion, pressure, location, or biometric signals—to monitor, analyze, and often react to changes in their surroundings. They form the foundational layer for many smart applications by enabling context-awareness, automation, and intelligent decision-making.

Machine Learning is a computational approach that enables systems to automatically identify patterns in data and make decisions or predictions without the need for explicit programming for each specific task. Machine Learning enables smart computing systems by finding patterns in data, and making intelligent decisions autonomously. It equips smart computing with the ability to evolve and improve based on experience. As a result, systems become more accurate, personalized, and efficient over time.

Readers will find the volume:

Covers advanced fuzzy logic techniques with real-world applications in supply chain, healthcare, and smart cities;
Features cutting-edge methodologies like fuzzy AHP, TOPSIS, and DEMATEL;
Includes global case studies and practical decision-making tools for complex, uncertain environments;
Bridges theory and practice for sustainable, data-driven decision support systems.

Audience:
Academics, researchers, supply chain managers, sustainability consultants, healthcare professionals, and policymakers in fields like operations research, Artificial Intelligence, Data Science, industrial engineering, and environmental studies looking for practical implementations for fuzzy logic in complex, real-world decision scenarios.