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Quantitative models for performance evaluation and benchmarking : data envelopment analysis with spreadsheets

Zhu, Joe - 2015 - 3. ed.

Persistent link: https://www.econbiz.de/10010478432

Sensitivity and stability of the classifications of returns to scale in Data Envelopment Analysis

Seiford, Lawrence M.; Zhu, Joe - In: Journal of productivity analysis 12 (1999) 1, pp. 55-75

Persistent link: https://www.econbiz.de/10001410503

Quantitative models for performance evaluation and benchmarking : data envelopment analysis with spreadsheets and DEA Excel Solver

Zhu, Joe - 2003

Persistent link: https://www.econbiz.de/10001718866

On the decomposition of DEA inefficiency

Chen, Yao; Morita, Hiroshi; Zhu, Joe - In: Multi-objective programming and goal programming : …, (pp. 99-104). 2003

Persistent link: https://www.econbiz.de/10002005968

An approach for determining DEA efficiency bounds

Chen, Yao; Morita, Hiroshi; Zhu, Joe - In: Multi-objective programming and goal programming : …, (pp. 105-110). 2003

Persistent link: https://www.econbiz.de/10002005978

Modeling performance measurement : applications and implementation issues in DEA

Cook, Wade D.; Zhu, Joe - 2005

Persistent link: https://www.econbiz.de/10002454925

Notes on sensitivity and stability of the classifications of returns to scale in data envelopment analysis: a comment

Seiford, Lawrence M.; Zhu, Joe - In: Journal of productivity analysis 23 (2005) 3, pp. 315-316

Persistent link: https://www.econbiz.de/10002975748

Airlines Performance via Two-Stage Network DEA Approach

Zhu, Joe - 2012

The performance of the airline industry has been widely studied using data envelopment analysis (DEA). Many existing DEA-based airline performance studies have used the standard DEA model, with some minor modifications. These studies have ignored the internal structure relative to the measures...

Persistent link: https://www.econbiz.de/10014042566

Data envelopment analysis and big data : a systematic literature review with bibliometric analysis

Charles, Vincent; Gherman, Tatiana; Zhu, Joe - In: Data-enabled analytics : DEA for big data, (pp. 1-29). 2021

Persistent link: https://www.econbiz.de/10012880161

Acceleration of large-scale DEA computations using random forest classification

Yu, Anyu; Shi, Yu; Zhu, Joe - In: Data-enabled analytics : DEA for big data, (pp. 31-49). 2021

Persistent link: https://www.econbiz.de/10012880167