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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
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A note on two-stage network DEA model : frontier projection and duality
Lim, Sungmook; Zhu, Joe - In: European journal of operational research : EJOR 248 (2016) 1, pp. 342-346
Persistent link: https://www.econbiz.de/10011434843
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Primal-dual correspondence and frontier projections in two-stage network DEA models
Lim, Sungmook; Zhu, Joe - In: Omega : the international journal of management science 83 (2018), pp. 236-248
Persistent link: https://www.econbiz.de/10011989179
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Computational tractability of chance constrained data envelopment analysis
Chen, Kun; Zhu, Joe - In: European journal of operational research : EJOR 274 (2019) 3, pp. 1037-1046
Persistent link: https://www.econbiz.de/10011990293
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Second order cone programming approach to two-stage network data envelopment analysis
Chen, Kun; Zhu, Joe - In: European journal of operational research : EJOR 262 (2017) 1, pp. 231-238
Persistent link: https://www.econbiz.de/10011785736
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Non-cooperative two-stage network DEA model : linear vs. parametric linear
Guo, Chuanyin; Zhu, Joe - In: European journal of operational research : EJOR 258 (2017) 1, pp. 398-400
Persistent link: https://www.econbiz.de/10011642238
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A conic relaxation model for searching for the global optimum of network data envelopment analysis
Chen, Kun; Cook, Wade D.; Zhu, Joe - In: European journal of operational research : EJOR 280 (2020) 1, pp. 242-253
Persistent link: https://www.econbiz.de/10012132386
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Additive slacks-based measure : computational strategy and extension to network DEA
Chen, Kun; Zhu, Joe - In: Omega : the international journal of management science 91 (2020), pp. 1-14
Persistent link: https://www.econbiz.de/10012151714
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A general computational framework and a hybrid algorithm for large-scale data envelopment analysis
Chu, Junfei; Rui, Yuting; Khezrimotlagh, Dariush; Zhu, Joe - In: European journal of operational research : EJOR 316 (2024) 2, pp. 639-650
Persistent link: https://www.econbiz.de/10014575559