As Multi-Criteria Decision-Making (MCDM) continues to grow and evolve, Machine Learning (ML) techniques have become increasingly important in finding efficient and effective solutions to complex problems. This book is intended to guide researchers, practitioners, and students interested in the intersection of ML and MCDM for optimal design.
Multi-Criteria Decision-Making and Optimum Design with Machine Learning: A Practical Guide is a comprehensive resource that bridges the gap between ML and MCDM. It offers a practical approach by demonstrating the application of ML and MCDM algorithms to real-world problems. Through case studies and examples, the book showcases the effectiveness of these techniques in optimal design. By providing a comparative analysis of conventional MCDM algorithms and machine learning techniques, the readers are able to make informed decisions about their use in different scenarios. The book also explores emerging trends, providing insights into future directions and potential opportunities. A wide range of topics are covered including the definition of optimal design, MCDM algorithms, supervised and unsupervised ML techniques, deep learning techniques, and more, making it a valuable resource for professionals and researchers in various fields.
Designed for professionals, researchers, and practitioners in engineering, computer science, sustainability, and related fields, the book is also a valuable resource for students and academics who wish to expand their knowledge of machine learning applications in multi-criteria decision-making. By offering a blend of theoretical insights and practical examples, this guide aims to inspire further research and application of machine learning in multidimensional decision-making environments.