Papers
2
Total Citations
67
H-Index
2
About
Joe Zhu is a leading figure in the field of operations research, with a primary focus on Data Envelopment Analysis (DEA) and its applications in performance measurement. His major contributions lie in advancing DEA models to handle complex, real-world scenarios where traditional assumptions of homogeneous decision-making units (DMUs) break down. Zhu’s pioneering work on non-homogeneous DMUs, particularly his 2016 paper on models accommodating different input configurations—cited over 50 times—has provided a robust framework for evaluating entities like banks or hospitals that operate with distinct resource sets. He further refined this area by introducing the concept of partial input-to-output impacts, allowing for DMU-specific analysis of efficiency drivers, as demonstrated in his 2015 study. With a cumulative citation count exceeding 60 for these key contributions, Zhu’s research has significantly influenced both theoretical DEA development and practical benchmarking in industries such as finance and healthcare. His work is essential reading for scholars and practitioners seeking to apply DEA to heterogeneous systems, making him a respected authority in performance analytics.
Research Focus
Key Achievements
Top Papers
- 1DEA models for non-homogeneous DMUs with different input configurations54 citations · 2016
- 2Partial input to output impacts in DEA: The case of DMU-specific impacts13 citations · 2015