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. [CrossRef] [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. [CrossRef] [MathSciNet] [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. [CrossRef] [Google Scholar]
  • A. Charnes and W.W. Cooper, Programming with linear fractional functions. Naval Res. Logistics Q. 9 (1962) 181–185. [CrossRef] [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. [CrossRef] [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. [CrossRef] [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). [CrossRef] [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. [CrossRef] [Google Scholar]
  • R. Färe and S. Grosskopf, Network DEA. Planning Sci. 34 (2000) 35–49. [CrossRef] [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. [CrossRef] [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. [CrossRef] [Google Scholar]
  • C. Kao, Efficiency decomposition in network data envelopment analysis: a relational model. Eur. J. Oper. Res. 192 (2009) 949–962. [CrossRef] [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. [CrossRef] [Google Scholar]
  • Y. Li, Y. Chen, L. Liang and J. Xie, DEA models for extended two-stage network structures. Omega 40 (2012) 611–618. [CrossRef] [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. [CrossRef] [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. [CrossRef] [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. [CrossRef] [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. [CrossRef] [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. [CrossRef] [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. [CrossRef] [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. [CrossRef] [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. [CrossRef] [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. [CrossRef] [Google Scholar]

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