Sustainability issues have gained more importance in contemporary globalization, pushing decision makers to find a systematic mathematical approach to conduct analyses of this real-world problem. The growing complexity in modern social-economics or engineering environments or systems has forced researchers to solve complicated problems by using multi-criteria decision-making (MCDM) approaches. However, traditional MCDM research mainly focuses on reaching the highest economic value or efficiency, and issues related to sustainability are still not closely explored. Advanced Multi-Criteria Decision Making for Addressing Complex Sustainability Issues discusses and addresses the challenges in the implementation of decision-making models in the context of green and sustainable engineering, criteria identification, quantification, comparison, selection, and analysis in the context of manufacturing, supply chain, transportation, and energy sectors. All academic communities in the areas of management, economics, business sciences, mechanical, and manufacturing technologies are able to use, apply, and implement the models presented in this book. It is intended for researchers, manufacturers, engineers, managers, industry professionals, academicians, and students.

This book presents a collection of chapters on various aspects of futuristic composite materials, from manufacturing challenges to materials characterization. The book covers the scientific basis of processing and synthesizing futuristic composites, including the prerequisite theoretical background and latest fabrication techniques. The book also discusses industrial applications of composites, such as in aerospace, automotive, and sports equipment. This book will serve as a valuable guide for researchers and professionals working in the area of futuristic lightweight materials.

Machine Learning (ML) is a sub field of artificial intelligence that uses soft computing and algorithms to enable computers to learn on their own and identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical, transport, supply chain and logistics, to manufacturing and industrial. Through numerous case studies, it will assist researchers and practitioners in selecting the correct options and strategies for managing organizational tasks.

Given the increasing need to optimize resources sustainably, decision-makers face challenges in analyzing and considering the numerous factors involved. This book makes an effort to present and concentrate on the challenges in decision-making processes for green and sustainable engineering. Through a collection of case studies such as evaluation of waste assessment and drainage system, sustainable building assessment, renewable energy selection, materials and manufacturing process optimization, and crop pattern influence in environmental and economic conditions, readers can learn how to apply cutting-edge Multiple-Criteria Decision Making (MCDM) methods in addressing complexities involved in the decision-making process.

Sustainability issues have gained more importance in contemporary globalization, pushing decision makers to find a systematic mathematical approach to conduct analyses of this real-world problem. The growing complexity in modern social-economics or engineering environments or systems has forced researchers to solve complicated problems by using multi-criteria decision-making (MCDM) approaches. However, traditional MCDM research mainly focuses on reaching the highest economic value or efficiency, and issues related to sustainability are still not closely explored. Advanced Multi-Criteria Decision Making for Addressing Complex Sustainability Issues discusses and addresses the challenges in the implementation of decision-making models in the context of green and sustainable engineering, criteria identification, quantification, comparison, selection, and analysis in the context of manufacturing, supply chain, transportation, and energy sectors. All academic communities in the areas of management, economics, business sciences, mechanical, and manufacturing technologies are able to use, apply, and implement the models presented in this book. It is intended for researchers, manufacturers, engineers, managers, industry professionals, academicians, and students.