Chapter 1 Merging Data Envelopment Analysis and Atructural Risk Minimization: Some Examples of Use of Multi-Output Machine Learning Techniques on Real-World Data -- Chapter 2 A New Network Data Envelopment Analysis Model for Efficacy Evaluation of Decision-Making Units -- Chapter 3 Possibilistic Network DEA Approach for Performance Evaluation of Two-Stage Decision-Making Units under Uncertainty.

This book explores the intersection of data envelopment analysis (DEA) and various analytical decision-making methodologies. Featuring contributions from experts in the field from across the world, each chapter delves into different aspects of DEA and its applications in real-world scenarios. The book covers a wide range of topics, including integrating DEA with machine learning techniques, performance evaluation in diverse sectors like banking and civil engineering, and using DEA in managerial decision-making. It also examines data mining during the Covid-19 pandemic and the application of blockchain and IoT in supply chain management. The book offers a deep dive into the evolution of nonparametric frontier methods and the development of new optimization algorithms, addressing the complexities of modern analytical decision-making tools. A few chapters delve into futuristic topics like fuzzy sets and their extensions in decision-making and exploring e-learning platforms for education. This book is an invaluable resource for researchers, practitioners and students interested in the latest DEA advancements and practical applications in various fields. Its multidisciplinary approach makes it a useful addition to the libraries of those seeking to understand the complexities and potentials of modern analytical decision-making tools.