In today's world decision-making processes have evolved into a more dynamic and real-time structure with big data and complex systems. Classic rule-based and inflexible traditional decision-making approaches are inadequate in the face of rapidly changing environmental conditions and increasing data processing speed. For this reason data analytics and rapidly advancing artificial intelligence-supported numerical decision-making methods aim to produce more accurate results. This book aims to further clarify the issue by establishing a bridge between current theory modeling and applications by focusing on current numerical decision-making methods. While revealing how decision-making processes can be improved with a multi-dimensional data-supported and analytical-based approach it also offers the reader both an academic and practical perspective by focusing on real-world problems.
The chapters covered in the book are shaped on critical issues such as decision-making with multiple criteria optimization uncertainty management sensitivity analysis risk assessment and performance measurement. Innovative methods that have caught up with the current situation such as COCOFISO ANP CILOS WEDBA SV & CODAS Monte Carlo Simulation are discussed in detail and how they can be applied in different disciplines is shown. Applications in various fields such as entrepreneurship traffic engineering quality of life measurement and college selection reveal the wide range of usage potential of numerical decision-making methods. This book study is actually a comprehensive reference source in terms of theory and application for academics data analysts business professionals and decision makers. It provides valuable insights for everyone who wants to better understand numerical decision-making techniques make optimized strategic decisions and effectively integrate data analytics and artificial intelligence into decision support processes. I hope this book will provide readers with more effective and data-based insights in decision-making processes.