Open Access
Issue
RAIRO-Oper. Res.
Volume 55, Number 6, November-December 2021
Page(s) 3677 - 3699
DOI https://doi.org/10.1051/ro/2021170
Published online 14 December 2021
  • M. Amirkhan, H. Didehkhani, K. Khalili-Damghani and A. Hafezalkotob, Measuring performance of a three-stage network structure using data envelopment analysis and Nash bargaining game: a supply chain application. Int. J. Inf. Technol. Decis. Making 17 (2018) 1429–1467. [Google Scholar]
  • J. Aparicio, J.L. Ruiz and I. Sirvent, Closest target and minimum distance to the Pareto-efficient frontier in DEA. J. Prod. 28 (2007) 209–218. [Google Scholar]
  • R.D. Banker, A. Charnes and W.W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage. Sci. 30 (1984) 1078–1092. [Google Scholar]
  • S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, Cambridge (2004). [Google Scholar]
  • I. Castelli, R. Pesenti and W. Ukovich, A classification of DEA models when the internal structure of the decision making unites is considered. Ann. Oper. Res. 173 (2010) 207–235. [Google Scholar]
  • S.L. Chao, Integrating multi-stage data envelopment analysis and a fuzzy analytical hierarchical process to evaluate the efficiency of major global liner shipping companies. Maritime Policy Manage. 44 (2017) 496–511. [Google Scholar]
  • A. Charnes and W.W. Cooper, Programming with linear fractional functions. Naval Res. Logistics Q. 9 (1962) 181–185. [Google Scholar]
  • A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (1978) 429–444. [Google Scholar]
  • K. Chen and J. Zhu, Second order cone programming approach to two-stage network data envelopment analysis. Eur. J. Oper. Res. 262 (2017) 231–238. [Google Scholar]
  • Y. Chen, W.D. Cook, N. Li and J. Zhu, Additive efficiency decomposition in two-stage DEA. Eur. J. Oper. Res. 196 (2009) 1170–1176. [Google Scholar]
  • Y. Chen, J. Du, H.D. Sherman and J. Zhu, DEA model with shared resources and efficiency decomposition. Eur. J. Oper. Res. 207 (2010) 339–349. [Google Scholar]
  • K. Chen, W.D. Cook and J. Zhu, A conic relaxation model for searching for the global optimum of network data envelopment analysis. Eur. J. Oper. Res. 280 (2020) 242–253. [Google Scholar]
  • W.D. Cook, J. Zhu, G.B. Bi and F. Yang, Network DEA: additive efficiency decomposition. Eur. J. Oper. Res. 207 (2010) 1122–1129. [Google Scholar]
  • D.K. Despotis, D. Sotiros and G. Koronakos, A network DEA approach for series multi-stage processes. Omega 61 (2016) 35–48. [Google Scholar]
  • T.S.H. Driessen, Cooperative Games, Solutions and Applications. Kluwer Academic Publishers, Dordrecht (1988). [Google Scholar]
  • J. Du, L. Liang, Y. Chen, W.D. Cook and J. Zhu, A bargaining game model for measuring performance of two-stage network structures. Eur. J. Oper. Res. 210 (2011) 390–397. [Google Scholar]
  • R. Färe and S. Grosskopf, Productivity and intermediate products: a frontier approach. Econ. Lett. 50 (1996) 65–70. [Google Scholar]
  • R. Färe and S. Grosskopf, Network DEA. Planning Sci. 34 (2000) 35–49. [Google Scholar]
  • C. Guo, F. Wei and Y. Chen, A note on second order cone programming approach to two-stage network data envelopment analysis. Eur. J. Oper. Res. 263 (2017) 733–735. [Google Scholar]
  • M. Hu and M. Fukushima, Multi-leader-follower games: models, methods and applications. J. Oper. Res. Soc. Jpn. 58 (2014) 1–23. [Google Scholar]
  • C.R. Johnson, Positive definite matrices. Am. Math. Mon. 77 (1970) 259–264. [Google Scholar]
  • C. Kao, Efficiency decomposition in network data envelopment analysis: a relational model. Eur. J. Oper. Res. 192 (2009) 949–962. [Google Scholar]
  • C. Kao, Efficiency decomposition for general multi-stage systems in data envelopment analysis. Eur. J. Oper. Res. 232 (2014) 117–124. [Google Scholar]
  • C. Kao, A classification of slacks-based efficiency measures in network data envelopment analysis with an analysis of the properties possessed. Eur. J. Oper. Res. 270 (2018) 1109–1121. [Google Scholar]
  • C. Kao and S.N. Hwang, Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. Eur. J. Oper. Res. 185 (2008) 418–429. [Google Scholar]
  • C.-Y. Lee and A. Johnson, Two-dimensional efficiency decomposition to measure the demand effect in productivity analysis. Eur. J. Oper. Res. 216 (2012) 584–593. [Google Scholar]
  • Y. Li, Y. Chen, L. Liang and J. Xie, DEA models for extended two-stage network structures. Omega 40 (2012) 611–618. [Google Scholar]
  • H. Li, C. Chen, W.D. Cook, J. Zhang and J. Zhu, Two-stage network DEA: Who is the leader? Omega 74 (2018) 15–19. [Google Scholar]
  • L. Liang, W.D. Cook and J. Zhu, DEA models for two-stage processes: game approach and efficiency decomposition. Nav. Res. Logistics 55 (2008) 643–653. [Google Scholar]
  • L. Liang, Z.Q. Li, W.D. Cook and J. Zhu, Data envelopment analysis efficiency in two-stage networks with feedback. IIE Trans. 43 (2011) 309–322. [Google Scholar]
  • R. Mahmoudi, A. Emrouznejad and M. Rasti-Barzoki, A bagaining game model for performance assessment in network DEAconsidering sub-networks: areal case study in banking. Neural Comput. App. 31 (2019) 6429–6447. [Google Scholar]
  • M.Z. Mahmoudabadi and A. Emrouznejad, Comprehensive performance evaluation of banking branches: a three-stage slacks-based measure (SBM) data envelopment analysis. Int. Rev. Econ. Finance 64 (2019) 359–376. [Google Scholar]
  • J.F. Nash, The bargaining problem. Econ. J. Econ. Soc. 18 (1950) 155–162. [Google Scholar]
  • J.F. Nash, Two-person cooperative games. Econ. J. Econ. Soc. 21 (1953) 128–140. [Google Scholar]
  • K. Ritzberger, Foundations of Non-Cooperative Game Theory. Oxford University Press (2002). [Google Scholar]
  • M. Tavana and K. Khalili-Damghani, A new two-stage Stackelberg fuzzy data envelopment analysis model. Measurement 53 (2014) 277–296. [Google Scholar]
  • M. Tavana, M.A. Kaviani, D.D. Caprio and B. Rahpeyma, A two-stage data envelopment analysis model for measuring performance in three-level supply chains. Measurement 78 (2016) 322–333. [Google Scholar]
  • K. Tone and M. Tsutsui, Network DEA: a slacks-based measure approach. Eur. J. Oper. Res. 197 (2009) 243–252. [Google Scholar]
  • J. Wu, L. Liang, F. Yang and H. Yan, Bargaining game model in the evaluation of decision making units. Expert Syst. App. 36 (2009) 3357–4362. [Google Scholar]
  • J. Wu, Q. Zhu, X. Ji, J. Chu and L. Liang, Two-stage network processes with shared resources and resources recovered from undesirable outputs. Eur. J. Oper. Res. 251 (2016) 182–197. [Google Scholar]
  • H. Wu, K. Lv, L. Liang and H. Hu, Measuring performance of sustainable manufacturing with recyclablee waste: a case from China’s iron and steel industry. Omega 66 (2017) 38–47. [Google Scholar]
  • L. Zhang and K. Chen, Hierarchical network systems: an application to high-technology industry in China. Omega 82 (2017) 118–131. [Google Scholar]
  • L. Zhang, C. Guo and F. Wei, Multistage network data envelopment analysis: semidefinite programming approach. J. Oper. Res. Soc. 70 (2019) 1284–1295. [Google Scholar]
  • X. Zhou, Z. Xu, J. Chai, L. Yao, S. Wang and B. Lev, Efficiency evaluation for banking systems under uncertainty: a multi-period three-stageDEA model. Omega 85 (2018) 68–82. [Google Scholar]
  • Z. Zhuo, L. Sun, W. Yang, W. Liu and C. Ma, A bargaining game model for efficiency decomposition in the centralized model of two-stage systems. Comput. Ind. Eng. 64 (2013) 103–108. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.