This handbook represents a milestone in the progression of Data Envelopment Analysis (DEA). Written by experts who are often major contributors to DEA theory, it includes a collection of chapters that represent the current state-of-the-art in DEA research. Topics include distance functions and...

Chapter 1. Data Envelopment Analysis and Big Data: A Systematic Literature Review with Bibliometric Analysis -- Chapter 2. Acceleration of large-scale DEA computations using random forest classification -- Chapter 3. The estimation of productive efficiency through machine learning techniques:...