Open Access
| Issue |
RAIRO-Oper. Res.
Volume 59, Number 5, September-October 2025
|
|
|---|---|---|
| Page(s) | 3207 - 3225 | |
| DOI | https://doi.org/10.1051/ro/2025123 | |
| Published online | 24 October 2025 | |
- R. Ahmed, Financial efficiency measurement of non-Life insurance companies in bangladesh. J. Commer. Manag. Thought 7, 3 (2016) 552–575. [Google Scholar]
- C. G. M. d. F. Alves and L.A. Meza, A review of network DEA models based on slacks-based measure: Evolution of literature, applications, and further research direction. Int. Trans. Oper. Res. 30 (2023) 2779–2760. [Google Scholar]
- A. Amirteimoori, S. Kordrostami and H. Azizi, Additive models for network data envelopment analysis in the presence of shared resources. Transp. Res. D. 48 (2016) 411–424. [Google Scholar]
- Q. An, H. Chen, B. Xiong, J. Wu and L. Liang, Target intermediate products setting in a two-stage system with fairness concern. Omega 73 (2017) 49–59. [Google Scholar]
- M. Asmild, J.C. Paradi, V. Aggarwall and C. Schaffnit, Combining DEA window analysis with the Malmquist index approach in a study of the Canadian banking industry. J. Prod. Anal. 21 (2004) 67–89. [Google Scholar]
- G. Bi, J. Ding, Y. Luo and L. Liang, Resource allocation and target setting for parallel production system based on DEA. Appl. Math. Model. 35, 9 (2011) 4270–4280. [Google Scholar]
- G. Bi, C. Feng, J. Ding and M.R. Khan, Estimating relative efficiency of DMU: Pareto principle and Monte Carlo oriented DEA approach. INFOR Inf. Syst Oper. Res. 50, 1 (2012) 44–57. [Google Scholar]
- W.F. Bowlin, Measuring performance: An introduction to data envelopment analysis (DEA). J. Cost Anal. 15, 2 (1998) 3–27. [CrossRef] [Google Scholar]
- L. Castelli, R. Pesenti and W. Ukovich, A classification of DEA models when the internal structure of the decision making units is considered. Ann. Oper. Res. 173 (2010) 207–235. [CrossRef] [MathSciNet] [Google Scholar]
- S.-L. Chao, M.-M. Yu and W.-F. Hsieh, Evaluating the efficiency of major container shipping companies: A framework of dynamic network DEA with shared inputs. Transp. Res. Part A Policy Pract. 117 (2018) 44–57. [Google Scholar]
- Y. Chen, W.D. Cook, N. Li and J. Zhu, Additive efficiency decomposition in two-stage DEA. Eur. J. Oper. Res. 196,3 (2009) 1170–1176. [Google Scholar]
- W.D. Cook and L.M. Seiford, Data envelopment analysis (DEA) – Thirty years on. Eur. J. Oper. Res. 192 1 (2009) 1–17. [Google Scholar]
- W.D. Cook, K. Tone and J. Zhu, Data envelopment analysis: Prior to choosing a model. Omega 44 (2014) 1–4. [Google Scholar]
- W.D. Cook, J. Zhu, G. Bi and F. Yang, Network DEA: Additive efficiency decomposition. Eur. J. Oper. Res. 207, 2 (2010) 1122–1129. [CrossRef] [Google Scholar]
- J.D. Cummins, M.A. Weiss, X. Xie and H. Zi, Economies of scope in financial services: A DEA efficiency analysis of the US insurance industry. J. Bank. Financ. 34, 7 (2010) 1525–1539. [Google Scholar]
- D.K. Despotis, D. Sotiros and G. Koronakos, Data envelopment analysis of two-stage processes: an alternative (non-conventional) approach. Int. Trans. Oper. Res. 32 (2022) 384–405. [Google Scholar]
- J. Ding, C. Feng, G. Bi, L. Liang and M.R. Khan, Cone ratio models with shared resources and nontransparent allocation parameters in network DEA. J. Prod. Anal. 44 (2015) 137–155. [Google Scholar]
- J. Du, Y. Chen and J. Huo, DEA for non-homogenous parallel networks. Omega 56 (2015) 122–132. [Google Scholar]
- A.I. González-Fernández, M. Rubio-Misas, and F. Ruiz, Multi-objective reference point techniques to optimize profitability, growth, and risk in the non-life insurance industry: international analysis. Int. Trans. Oper. Res. 30, 5 (2023) 2546–2570. [Google Scholar]
- S.K. Goyal, S.R. Singh and D. Sing, Economic order quantity model for imperfect lot with partial backordering under the effect of learning and advertisement dependent imprecise demand. Int. J. Oper. Res. 29, 2 (2017) 197–218. [CrossRef] [MathSciNet] [Google Scholar]
- A. Hatami-Marbini and S. Saati, Measuring performance with common weights: network DEA. Neural Comput. Appl. 32 (2020) 3599–3617. [Google Scholar]
- R. Imanirad, W.D. Cook, and J. Zhu, Partial input to output impacts in DEA: Production considerations and resource sharing among business subunits. Nav. Res. Logist. 60, 3 (2013) 190–207. [Google Scholar]
- M.M. Jaloudi, The efficiency of Jordan insurance companies and its determinants using DEA, slacks, and logit models J. Asian Bus. Econ. Stud. 26, 1 (2019) 153–166. [Google Scholar]
- C. Kao, Efficiency measurement for parallel production systems. Eur. J. Oper. Res. 196, 3 (2009) 1107–1112. [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, 1 (2008) 418–429. [CrossRef] [Google Scholar]
- C. Kao and S.-N. Hwang, Efficiency measurement for network systems: IT impact on firm performance. Decis. Support Syst. 48, 3 (2010) 437–446. [CrossRef] [Google Scholar]
- S. Kumar, S.R. Singh, S. Agarwal and D. Yadav, Joint effect of selling price and promotional efforts on retailer’s inventory control policy with trade credit, time-dependent holding cost, and partial backlogging under inflation. RAIRO Oper. Res. 57, 3 (2023) 1491–1522. [Google Scholar]
- M. Lament and S. Bukowski, Business model impact on the financial efficiency of insurance companies. Eur. Res. Stud. J. 24, 4 (2021) 237–247. [Google Scholar]
- H.F. Lewis and T.R. Sexton, Network DEA: efficiency analysis of organizations with complex internal structure. Comput. Oper. Res. 31, 9 (2004) 1365–1410. [Google Scholar]
- L. Liang, W.D. Cook and J. Zhu, DEA models for two-stage processes: Game approach and efficiency decomposition. Nav. Res. Logist. 55, 7 (2008) 643–653. [Google Scholar]
- N. Liang, Y. Chen, Y. Zha and H. Hu, Performance evaluation of individuals in workgroups with shared outcomes using DEA. INFOR Inf. Syst Oper. Res. 53, 2 (2015) 78–89. [Google Scholar]
- J. Ma and L. Chen, Evaluating operation and coordination efficiencies of parallel-series two-stage system: A data envelopment analysis approach. Expert Syst. Appl. 91 (2018) 1–11. [Google Scholar]
- H. Omrani, A. Emrouznejad, M. Shamsi and P. Fahimi, Evaluation of insurance companies considering uncertainty: A multi-objective network data envelopment analysis model with negative data and undesirable outputs. Socio-Econ. Plan. Sci. 82 (2022) 101306. [Google Scholar]
- H. Omrani, Z. Oveysi, A. Emrouznejad and T. Teplova, A mixed-integer network DEA with shared inputs and undesirable outputs for performance evaluation: Efficiency measurement of bank branches. J. Oper. Res. Soc. 74, 4 (2023) 1150–1165. [Google Scholar]
- G. Papaioannou and V.V. Podinovski, Multicomponent production technologies with restricted allocations of shared inputs and outputs. Eur. J. Oper. Res. 308, 1 (2023) 274–289. [Google Scholar]
- M.-T. Phung, C.-P. Cheng, C. Guo and C.-Y Kao, Mixed network DEA with shared resources: a case of measuring performance for banking industry. Operations Research Perspectives 7 (2020) 100173. [Google Scholar]
- V.V. Podinovski, Variable and constant returns-to-scale production technologies with component processes. Oper. Res. 70, 2 (2022) 1238–1258. [Google Scholar]
- I.M. Premachandra, Y. Chen and J. Watson, DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment. Omega 39, 6 (2011) 620–626. [Google Scholar]
- B.K. Sahoo, J. Zhu, K. Tone and B.M. Klemen, Decomposing technical efficiency and scale elasticity in two-stage network DEA. Eur. J. Oper. Res. 233, 3 (2014) 584–594. [CrossRef] [Google Scholar]
- L.M. Seiford and J. Zhu, Profitability and marketability of the top 55 US commercial banks. Management Science 45, 9 (1999) 1270–1288. [Google Scholar]
- T.R. Sexton and H.F. Lewis, Two-stage DEA: An application to major league baseball. Journal of Productivity Analysis 19 (2003) 227–249. [Google Scholar]
- K. Tone, Q.L. Kweh, W.-M. Lu and I.W.K. Ting, Modeling investments in the dynamic network performance of insurance companies. Omega 88 (2019) 237–247. [Google Scholar]
- Q. Wang, Z. Wu and X. Chen, Decomposition weights and overall efficiency in a two-stage DEA model with shared resources. Comput. Ind. Eng. 136 (2019) 135–148. [Google Scholar]
- J. Wu, B. Xiong, Q. An, J. Sun and H. Wu, Total-factor energy efficiency evaluation of Chinese industry by using two-stage DEA model with shared inputs. Ann. Oper. Res. 255 (2017) 257–276. [Google Scholar]
- B. Xiong, J. Wu, Q. An, J. Chu and L. Liang, Resource allocation of a parallel system with interaction consideration using a DEA approach: an application to Chinese input–output table. INFOR Inf. Syst Oper. Res. 56, 3 (2018) 298–316. [Google Scholar]
- D. Yadav, R. Kumari, N. Kumar and B. Sarkar, Reduction of waste and carbon emission through the selection of items with cross-price elasticity of demand to form a sustainable supply chain with preservation technology. J. Clean. Prod. 297 (2021) 126298. [CrossRef] [Google Scholar]
- F.C. Yang, Centralized resource allocation and target setting of a two-stage production process using data envelopment analysis. Inter. Trans. Oper. Res. 31, 2 (2024) 889–917. [Google Scholar]
- M.-M. Yu, L.-H. Chen and B. Hsiao, A fixed cost allocation based on the two-stage network data envelopment approach. J. Bus. Res. 69, 5 (2016) 1817–1822. [CrossRef] [MathSciNet] [Google Scholar]
- X. Zhang, Q. Xia and F. Wei, Efficiency evaluation of two-stage parallel-series structures with fixed-sum outputs: An approach based on SMAA and DEA. Expert Syst. Appl. 227 (2023) 120264. [Google Scholar]
- L. Zhao, Q. Zhu and L. Zhang, Regulation adaptive strategy and bank efficiency: A network slacks-based measure with shared resources. Eur. J. Oper. Res. 295, 1 (2021) 348–362. [CrossRef] [Google Scholar]
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