Uncertainty in Data Envelopment Analysis 1st Edition
Grab the cutting edge of performance measurement with Uncertainty in Data Envelopment Analysis, 1st Edition, by Farhad Hosseinzadeh Lotfi, Masoud Sanei, Ali Asghar Hosseinzadeh, Sadegh Niroomand, and Ali Mahmoodirad. This authoritative work illuminates how uncertainty—stochastic variability, fuzzy data, and incomplete information—reshapes traditional Data Envelopment Analysis (DEA) and its use in real-world decision-making.
Discover practical, research-driven approaches that bridge theory and application. Clear explanations of advanced models, sensitivity analysis, and robust-efficiency techniques make complex concepts accessible to researchers, analysts, and managers. Whether you’re evaluating hospital performance, banking efficiency, manufacturing productivity, or public-sector programs, this book equips you with methods to produce reliable, defensible benchmarking under imperfect data.
Packed with examples and methodological guidance relevant for practitioners across the Middle East, Asia, Europe, and the Americas, the book is both globally relevant and regionally mindful—ideal for universities, consulting firms, and policy institutes seeking modern tools for resource allocation and performance improvement. Emphasizing transparency and reproducibility, the authors provide step-by-step reasoning that supports adoption in empirical studies and practical projects.
If you need to move beyond deterministic DEA and produce robust, credible efficiency assessments despite noisy or incomplete inputs, this volume is an essential reference. Its balanced mix of intuition, mathematics, and application will deepen your expertise and enrich your analyses.
Enhance your toolkit with Uncertainty in Data Envelopment Analysis, 1st Edition—a must-have for anyone serious about rigorous performance evaluation in an uncertain world. Order your copy today to start applying advanced DEA techniques with confidence.
Note: eBooks do not include supplementary materials such as CDs, access codes, etc.


