Open Access
RAIRO-Oper. Res.
Volume 57, Number 6, November-December 2023
Page(s) 3007 - 3031
Published online 30 November 2023
  • A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (1987) 429–444. [Google Scholar]
  • W.W. Cooper, K.S. Park and G. Yu, IDEA and AR-IDEA: models for dealing with imprecise data in DEA. Manage. Sci. 45 (1999) 597–607. [CrossRef] [Google Scholar]
  • S.H. Kim, C.K. Park and K.S. Park, An application of data envelopment analysis in telephone offices evaluation with partial data. Comput. Oper. Res. 26 (1999) 59–72. [CrossRef] [Google Scholar]
  • W.W. Cooper, K.S. Park and G. Yu, IDEA (imprecise data envelopment analysis) with CMDs (column maximum decision making units). J. Oper. Res. Soc. 52 (2001) 176–181. [CrossRef] [Google Scholar]
  • D.K. Despotis and Y.G. Smirlis, Data envelopment analysis with imprecise data. Eur. J. Oper. Res. 140 (2002) 24–36. [Google Scholar]
  • T. Entani, Y. Maeda and H. Tanaka, Dual models of interval DEA and its extension to interval data. Eur. J. Oper. Res. 136 (2002) 32–45. [Google Scholar]
  • Y.K. Lee, K.S. Park and S.H. Kim, Identification of inefficiencies in an additive model based IDEA (imprecise data envelopment analysis). Comput. Oper. Res. 29 (2002) 1661–1676. [CrossRef] [MathSciNet] [Google Scholar]
  • J. Zhu, Imprecise data envelopment analysis (IDEA): a review and improvement with an application. Eur. J. Oper. Res. 144 (2003) 513–529. [Google Scholar]
  • K.S. Park, Simplification of the transformations and redundancy of assurance regions in IDEA (imprecise DEA). J. Oper. Res. Soc. 55 (2004) 1363–1366. [CrossRef] [Google Scholar]
  • J. Zhu, Imprecise DEA via standard linear DEA models with a revisit to a Korean mobile telecommunication company. Oper. Res. 52 (2004) 323–329. [Google Scholar]
  • G.R. Jahanshahloo, R. Kazemi-Matin and A. Hadi-Vencheh, On FDH efficiency analysis with interval data. Appl. Math. Comput. 159 (2004) 47–55. [MathSciNet] [Google Scholar]
  • G.R. Jahanshahloo, R. Kazemi-Matin and A. Hadi-Vencheh, On return to scale of fully efficient DMUs in data envelopment analysis under interval data. Appl. Math. Comput. 154 (2004) 31–40. [MathSciNet] [Google Scholar]
  • G.R. Jahanshahloo, F. Hosseinzadeh-Lofti and M. Moradi, Sensitivity and stability analysis in DEA with interval data. Appl. Math. Comput. 156 (2004) 463–477. [MathSciNet] [Google Scholar]
  • A. Amirteimoori and S. Kordrostami, Multi-component efficiency measurement with imprecise data. Appl. Math. Comput. 162 (2005) 1265–1277. [MathSciNet] [Google Scholar]
  • Y.M. Wang, R. Greatbanks and J.B. Yang, Interval efficiency assessment using data envelopment analysis. Fuzzy Sets Syst. 153 (2005) 347–370. [Google Scholar]
  • M.S. Haghighat and E. Khorram, The maximum and minimum number of efficient units in DEA with interval data. Appl. Math. Comput. 163 (2005) 919–930. [MathSciNet] [Google Scholar]
  • C. Kao, Interval efficiency measures in data envelopment analysis with imprecise data. Eur. J. Oper. Res. 174 (2006) 1087–1099. [CrossRef] [Google Scholar]
  • Y.G. Smirlis, E.K. Maragos and D.K. Despotis, Data envelopment analysis with missing values: an interval DEA approach. Appl. Math. Comput. 177 (2006) 1–10. [MathSciNet] [Google Scholar]
  • R. Farzipoor-Saen, A decision model for selecting technology suppliers in the presence of nondiscretionary factors. Appl. Math. Comput. 181 (2006) 1609–1615. [Google Scholar]
  • R. Farzipoor-Saen, An algorithm for ranking technology suppliers in the presence of nondiscretionary factors. Appl. Math. Comput. 181 (2006) 1616–1623. [Google Scholar]
  • R. Farzipoor-Saen, Suppliers selection in the presence of both cardinal and ordinal data. Eur. J. Oper. Res. 183 (2007) 741–747. [CrossRef] [Google Scholar]
  • R. Kazemi-Matin, G.R. Jahanshahloo and A. Hadi-Vencheh, Inefficiency evaluation with an additive DEA model under imprecise data, an application on IAUK departments. J. Oper. Res. Soc. Jpn. 50 (2007) 163–177. [Google Scholar]
  • K.S. Park, Efficiency bounds and efficiency classifications in DEA with imprecise data. J. Oper. Res. Soc. 58 (2007) 533–540. [CrossRef] [Google Scholar]
  • G.R. Jahanshahloo, F. Hosseinzadeh-Lotfi, M. Rostamy-Malkhalifeh and M. Ahadzadeh-Namin, A generalized model for data envelopment analysis with interval data. Appl. Math. Model. 33 (2009) 3237–3244. [CrossRef] [MathSciNet] [Google Scholar]
  • R. Farzipoor-Saen, Supplier selection by the pair of nondiscretionary factors-imprecise data envelopment analysis models. J. Oper. Res. Soc. 60 (2009) 1575–1582. [CrossRef] [Google Scholar]
  • K.S. Park, Duality, efficiency computations and interpretations in imprecise DEA. Eur. J. Oper. Res. 200 (2010) 289–296. [CrossRef] [Google Scholar]
  • H. Azizi and H. Ganjeh-Ajirlu, Measurement of the worst practice of decisionmaking units in the presence of non-discretionary factors and imprecise data. Appl. Math. Model. 35 (2011) 4149–4156. [CrossRef] [MathSciNet] [Google Scholar]
  • R. Farzipoor-Saen, Media selection in the presence of flexible factors and imprecise data. J. Oper. Res. Soc. 62 (2011) 1695–1703. [CrossRef] [Google Scholar]
  • R. Farzipoor-Saen, International market selection using advanced data envelopment analysis. IMA J. Manage. Math. 22 (2011) 371–386. [Google Scholar]
  • C. Kao and S.T. Liu, Efficiencies of two-stage systems with fuzzy data. Fuzzy Set Syst. 176 (2011) 20–35. [CrossRef] [Google Scholar]
  • A. Emrouznejad, M. Rostamy-Malkhalifeh, A. Hatami-Marbini and M. Tavana, General and multiplicative non-parametric corporate performance models with interval ratio data. Appl. Math. Model. 36 (2012) 5506–5514. [CrossRef] [Google Scholar]
  • W. Zhu and Z. Zhou, Interval efficiency of two-stage network DEA model with imprecise data. INFOR 51 (2013) 142–150. [Google Scholar]
  • Y. Bouzembrak, H. Allaoui, G. Goncalves, H. Bouchriha and M. Baklouti, A possibilistic linear programming model for supply chain network design under uncertainty. IMA J. Manage. Math. 24 (2013) 209–229. [Google Scholar]
  • A. Hadi-Vencheh, A. Hatami-Marbini, Z. Ghelej-Beigi and K. Gholami, An inverse optimization model for imprecise data envelopment analysis. Optimization 64 (2015) 2441–2454. [CrossRef] [MathSciNet] [Google Scholar]
  • H. Azizi, S. Kordrostami and A. Amirteimoori, Slacks-based measures of efficiency in imprecise data envelopment analysis: an approach based on data envelopment analysis with double frontiers. Comput. Ind. Eng. 79 (2015) 42–51. [CrossRef] [Google Scholar]
  • F. He, X. Xu, R. Chen and L. Zhu, Interval efficiency improvement in DEA by using ideal points. Measurement 87 (2016) 138–145. [CrossRef] [Google Scholar]
  • G.H. Shirdel, S. Ramezani-Tarkhorani and Z. Jafari, A method for evaluating the performance of decision making units with imprecise data using common set of weights. Int. J. Appl. Comput. Math. 3 (2017) 411–423. [CrossRef] [MathSciNet] [Google Scholar]
  • G. Wei and K. Wang, A comparative study of robust efficiency analysis and data envelopment analysis with imprecise data. Expert Syst. Appl. 81 (2017) 28–38. [CrossRef] [Google Scholar]
  • M. Toloo, E. Keshavarz and A. Hatami-Marbini, Dual-role factors for imprecise data envelopment analysis. Omega 77 (2018) 15–31. [PubMed] [Google Scholar]
  • R. Mo, H. Huang and L. Yang, An interval efficiency measurement in DEA when considering undesirable outputs. Complexity 2020 (2020) 1–12. [CrossRef] [Google Scholar]
  • S. Ghobadi, Merging decision-making units with interval data. RAIRO: Oper. Res. 55 (2021) S1605–S1631. [CrossRef] [EDP Sciences] [Google Scholar]
  • R. Färe and S. Grosskopf, Network DEA. Soc.-Econ. Plann. Sci. 34 (2000) 35–49. [CrossRef] [Google Scholar]
  • C. Kao, Network data envelopment analysis: a review. Eur. J. Oper. Res. 239 (2014) 1–16. [Google Scholar]
  • C. Kao, Network Data Envelopment Analysis Foundations and Extensions, 2nd edition. Springer Cham (2023) 427. [Google Scholar]
  • R. Färe, R. Grabowski, S. Grosskopf and S. Kraft, Efficiency of a fixed but allocatable input: a non-parametric approach. Econ. Lett. 56 (1997) 187–193. [Google Scholar]
  • W.D. Cook, M. Hababou and H.J.H. Tuenter, Multicomponent efficiency measurement and shared inputs in data envelopment analysis: an application to sales and service performance in bank branches. J. Prod. Anal. 14 (2000) 209–224. [CrossRef] [Google Scholar]
  • W.D. Cook and M. Hababou, Sales performance measurement in bank branches. Omega 29 (2001) 299–307. [CrossRef] [Google Scholar]
  • G.R. Jahanshahloo, A. Amirteimoori and S. Kordrostami, Multicomponent performance, progress and regress measurement and shared inputs and outputs in DEA for panel data: an application in commercial bank branches. Appl. Math. Comput. 151 (2004) 1–16. [MathSciNet] [Google Scholar]
  • M.M. Yu and C.K. Fan, Measuring the cost effectiveness of multimode bus transit in the presence of accident risks. Transp. Plan. Techn. 129 (2006) 383–407. [Google Scholar]
  • C. Kao, Efficiency measurement for parallel production systems. Eur. J. Oper. Res. 196 (2009) 1107–1112. [Google Scholar]
  • C. Kao, Efficiency decomposition for parallel production systems. J. Oper. Res. Soc. 63 (2012) 64–71. [CrossRef] [Google Scholar]
  • M.D. Kremantzis, P. Beullens, L.S. Kyrgiakos and J. Klein, Measurement and evaluation of multi-function parallel network hierarchical DEA systems. Soc.-Econ. Plann. Sci. 84 (2022) 101428. [CrossRef] [Google Scholar]
  • M.D. Troutt, P.J. Ambrose and C.K. Chan, Optimal throughput for multistage input–output processes. Int. J. Oper. Prod. Manage. 21 (2001) 148–158. [CrossRef] [Google Scholar]
  • A. Amirteimoori and S. Kordrostami, DEA-like models for multi-component performance measurement. Appl. Math. Comput. 163 (2005) 735–743. [MathSciNet] [Google Scholar]
  • K.S. Park and K. Park, Measurement of multi period aggregative efficiency. Eur. J. Oper. Res. 193 (2009) 567–580. [CrossRef] [Google Scholar]
  • M. Tsutsui and M. Goto, A multi-division efficiency evaluation of U.S. electric power companies using a weighted slacks-based measure. Soc.-Econ. Plann. Sci. 43 (2009) 201–208. [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]
  • Q.L. Wei and T.S. Chang, Optimal system design series-network DEA models. J. Oper. Res. Soc. 52 (2011) 1109–1119. [CrossRef] [Google Scholar]
  • L. Fang, Optimal budget for system design series network DEA model. J. Oper. Res. Soc. 65 (2014) 1781–1787. [CrossRef] [Google Scholar]
  • R. Lin and Q. Liu, Directional distance based efficiency decomposition for series system in network data envelopment analysis. J. Oper. Res. Soc. 73 (2021) 1873–1888. [Google Scholar]
  • T.R. Sexton and H.F. Lewis, Two-stage DEA: an application to major league baseball. J. Prod. Anal. 19 (2003) 227–249. [CrossRef] [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. 85 (2008) 418–429. [CrossRef] [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]
  • L. Liang, Z.Q. Li and W.D. Cook, Data envelopment analysis efficiency in two-stage networks with feedback. IIE. Trans. 43 (2011) 309–322. [CrossRef] [Google Scholar]
  • J.S. Liu and W.M. Lu, Network-based method for ranking of efficient units in two-stage DEA models. J. Oper. Res. Soc. 63 (2012) 1153–1164. [CrossRef] [Google Scholar]
  • G. Halkos, N. Tzeremes and S. Kourtzidis, Weight assurance region in two-stage additive efficiency decomposition DEA model: an application to school data. J. Oper. Res. Soc. 66 (2015) 696–704. [CrossRef] [Google Scholar]
  • S. Aviles-Sacoto, W.D. Cook, R. Imanirad and J. Zhu, Two-stage network DEA: when intermediate measures can be treated as outputs from the second stage. J. Oper. Res. Soc. 66 (2015) 1868–1877. [Google Scholar]
  • M. Tavana, M.A. Kaviani, D. Di-Caprio and B. Rahpeyma, A two-stage data envelopment analysis model for measuring performance in three-level supply chains. Measurement 78 (2016) 322–333. [CrossRef] [Google Scholar]
  • R. Azizi and R. Kazemi-Matin, Ranking two-stage production units in data envelopment analysis. Asia Pac. J. Oper. Res. 33 (2016) 1–19. [Google Scholar]
  • G. Halkos, N. Tzeremes and S. Kourtzidis, A unified classification of two-stage DEA models. Surv. Oper. Res. Man. Sci. 19 (2014) 1–16. [Google Scholar]
  • M.S. Shahbazifar, R. Kazemi Matin, M. Khounsiavash and F. Koushki, Group ranking of two-stage production units in network data envelopment analysis. RAIRO: Oper. Res. 55 (2021) 1825–1840. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • H.F. Lewis and T.R. Sexton, Network DEA: efficiency analysis of organizations with complex internal structure. Comput. Oper. Res. 31 (2004) 1365–1410. [Google Scholar]
  • C. Kao and S.N. Hwang, Efficiency measurement for network systems: IT impact on firm performance. Decis. Support Syst. 48 (2010) 437–446. [CrossRef] [Google Scholar]
  • S. Lozano, Scale and cost efficiency analysis of networks of processes. Expert Syst. Appl. 38 (2011) 6612–6617. [CrossRef] [Google Scholar]
  • R. Kazemi-Matin and R. Azizi, A unified network-DEA model for performance measurement of production systems. Measurement 60 (2015) 186–193. [CrossRef] [Google Scholar]
  • F. Boloori, M. Afsharian and J. Pourmahmoud, Equivalent multiplier and envelopment DEA models for measuring efficiency under general network structures. Measurement 80 (2016) 259–269. [CrossRef] [Google Scholar]
  • A. Kalhor and R. Kazemi-Matin, Performance evaluation of general network production processes with undesirable outputs: a DEA approach. RAIRO: Oper Res. 52 (2018) 17–34. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • F. Yang, Y. Sun, D. Wang and S. Ang, Network data envelopment analysis with two-level maximin strategy. RAIRO: Oper. Res. 56 (2022) 2543–2556. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • L.S. Kyrgiakos, G. Kleftodimos, G. Vlontzos and P.M. Pardalos, A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability. Oper. Res. Int. J. 23 (2023) 7. [CrossRef] [Google Scholar]
  • V.H.L. Saputri, W. Sutopo, M. Hisjam and A. Ma’aram, Sustainable agri-food supply chain performance measurement model for GMO and Non-GMO using data envelopment analysis method. Appl. Sci. 9 (2019) 1199. [CrossRef] [Google Scholar]
  • A. Kord, A. Payan and S. Saati, Network DEA models with stochastic data to assess the sustainability performance of agricultural practices: an application for Sistan and Baluchestan Province in Iran. J. Math. 2022 (2022) 1–19. [CrossRef] [Google Scholar]
  • L.C. Lu, S.Y. Chiu, Y. Ho and T.H. Chang, Three-stage circular efficiency evaluation of agricultural food production, food consumption, and food waste recycling in EU countries. J. Clean. Prod. 343 (2022) 130870. [CrossRef] [Google Scholar]
  • A. Kord, A. Payan and S. Saati, Sustainability and optimal allocation of human resource of agricultural practices in Sistan and Baluchestan Province based on network DEA. J. Math. Extension 15 (2021) 1–44. [Google Scholar]
  • A. Abbas, C. Zhao, M. Waseem and A.K.R. Ahmad Khan, Analysis of energy input-output of farms and assessment of greenhouse gas emissions: a case study of cotton growers. Front. Environ. Sci. 9 (2022) 826838. [CrossRef] [Google Scholar]
  • Y. Yang, Q. Zhuang, G. Tian and S. Wei, A management and environmental performance evaluation of China’s family farms using an ultimate comprehensive cross-efficiency model (UCCE). Sustainability 11 (2019) 6. [Google Scholar]
  • A. Nandy, P.K. Singh and A.K. Singh, Systematic review and meta-regression analysis of technical efficiency of agricultural production systems. Global Bus. Rev. 22 (2021) 396–421. [CrossRef] [Google Scholar]
  • X. Li, X. Li and J. Jiang, Deep intelligence-driven efficient forecasting for the agriculture economy of computational social systems. Comput. Intell. Neurosci. 2022 (2022). DOI: 10.1155/2022/2854675. [Google Scholar]
  • K. Kočišová, Application of the DEA on the measurement of efficiency in the EU countries. Agric. Econ. 61 (2015) 51–62. [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.