Open Access
RAIRO-Oper. Res.
Volume 56, Number 6, November-December 2022
Page(s) 3915 - 3940
Published online 25 November 2022
  • S. Abolghasem, M. Toloo and S. Amézquita, Cross-efficiency evaluation in the presence of flexible measures with an application to healthcare systems. Health Care Manage. Sci. 22 (2019) 512–533. [CrossRef] [PubMed] [Google Scholar]
  • M. Afsharian, H. Ahn and S.G. Harms, A review of DEA approaches applying a common set of weights: the perspective of centralized management. Eur. J. Oper. Res. 294 (2021) 3–15. [CrossRef] [Google Scholar]
  • N. Aghayi, M. Tavana and M.A. Raayatpanah, Robust efficiency measurement with common set of weights under varying degrees of conservatism and data uncertainty. Eur. J. Ind. Eng. 10 (2016) 385–405. [CrossRef] [Google Scholar]
  • G.R. Amin and M. Toloo, A polynomial-time algorithm for finding ε in DEA models. Comput. Oper. Res. 31 (2004) 803–805. [CrossRef] [MathSciNet] [Google Scholar]
  • A. Amirteimoori and A. Emrouznejad, Flexible measures in production process: a DEA-based approach. RAIRO: Oper. Res. 45 (2011) 63–74. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • A. Amirteimoori, A. Emrouznejad and L. Khoshandam, Classifying flexible measures in data envelopment analysis: a slack-based measure. Measurement 46 (2013) 4100–4107. [CrossRef] [Google Scholar]
  • A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of DMUs. Eur. J. Oper. Res. 2 (1978) 429–444. [CrossRef] [Google Scholar]
  • A. Charnes, W.W. Cooper, Z.M. Huang and D.B. Sun, Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks. J. Econ. 46 (1990) 73–91. [CrossRef] [Google Scholar]
  • M. Chen, S. Ang, F. Yang and L. Jiang, Efficiency evaluation of non-homogeneous DMUs with inconsistent input quality. Comput. Ind. Eng. 158 (2021) 107418. [Google Scholar]
  • C.I. Chiang and G.H. Tzeng, A new efficiency measure for DEA: efficiency achievement measure established on fuzzy multiple objectives programming. J. Manage. 17 (2000) 369–388. [Google Scholar]
  • C.I. Chiang, M.J. Hwang and Y.H. Liu, Determining a common set of weights in a DEA problem using a separation vector. Math. Comput. Model. 54 (2011) 2464–2470. [Google Scholar]
  • W.D. Cook and J. Zhu, Classifying inputs and outputs in DEA. Eur. J. Oper. Res. 180 (2007) 692–699. [CrossRef] [Google Scholar]
  • W.D. Cook, Y. Roll and A. Kazakov, A DEA model for measuring the relative efficiency of highway maintenance patrols. Inf. Syst. Oper. Res. 28 (1990) 113–124. [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]
  • C.S. de Blas, S.J. Martin and G.D. Gonzalez, Combined social networks and data envelopment analysis for ranking. Eur. J. Oper. Res. 3 (2017) 990–999. [Google Scholar]
  • D.K. Despotis, Improving the discriminating power of DEA: focus on globally efficient units. J. Oper. Res. Soc. 53 (2002) 314–323. [CrossRef] [Google Scholar]
  • B. Ebrahimi, Efficiency bounds and efficiency classifications in imprecise DEA: an extension. J. Oper. Res. Soc. 71 (2020) 491–504. [CrossRef] [Google Scholar]
  • B. Ebrahimi and E. Hajizadeh, A novel DEA model for solving performance measurement problems with flexible measures: an application to Tehran Stock Exchange. Measurement 179 (2021) 109444. [CrossRef] [Google Scholar]
  • B. Ebrahimi and M. Khalili, A new integrated AR-IDEA model to find the best DMU in the presence of both weight restrictions and imprecise data. Comput. Ind. Eng. 125 (2018) 357–363. [CrossRef] [Google Scholar]
  • B. Ebrahimi, M. Tavana, M. Toloo and V. Charles, A novel mixed binary linear DEA model for ranking decision-making units with preference information. Comput. Ind. Eng. 149 (2020) 106720. [CrossRef] [Google Scholar]
  • D. Ennen and I. Batool, Airport efficiency in Pakistan-A Data Envelopment Analysis with weight restrictions. J. Air. Transp. Manage. 69 (2018) 205–212. [CrossRef] [Google Scholar]
  • T. Ertay, The most cost efficient automotive vendor with price uncertainty: a new DEA approach. Measurement 52 (2014) 135–144. [CrossRef] [Google Scholar]
  • M. Ghadami, H. Sahebi, M. Pishvaee and H. Gilani, A sustainable cross-efficiency DEA model for international MSW-to-biofuel supply chain design. RAIRO: Res. Oper. 55 (2021) 2653. [Google Scholar]
  • A. Hatami Marbini, M. Tavana, P.J. Agrell, F. Hosseinzadeh Lotfi and Z. Ghelej Beigi, A common-weights DEA model for centralized resource reduction and target setting. Comput. Ind. Eng. 79 (2015) 195–203. [CrossRef] [Google Scholar]
  • F. Hosseinzadeh Lotfi, M. Rostamy-Malkhlifeh, G.R. Jahanshahloo, Z. Moghaddas, M. Khodabakhshi and M. Vaez-Ghasemi, A review of ranking models in data envelopment analysis. J. Appl. Math. (2013) 20. DOI: 10.1155/2013/492421. [Google Scholar]
  • C.F. Hu, H.F. Wang and T. Liu, Measuring efficiency of a recycling production system with imprecise data. Numer. Algebra Control Optim. 12 (2022) 79. [CrossRef] [MathSciNet] [Google Scholar]
  • C.L. Hwang and A.S.M. Masud, Methods for multiple objective decision making. In: Multiple Objective Decision Making – Methods and Applications. Springer, Berlin (1979). [CrossRef] [Google Scholar]
  • G.R. Jahanshahloo, F. Hosseinzadeh Lotfi, M. Khanmohammadi, M. Kazemimanesh and V. Rezaie, Ranking of units by positive ideal DMU with common weights. Expert. Syst. Appl. 37 (2010) 7483–7488. [CrossRef] [Google Scholar]
  • C. Kao and H. Hung, Data envelopment analysis with common weights: the comprise solution approach. J. Oper. Res. Soc. 56 (2005) 1196–1203. [CrossRef] [Google Scholar]
  • S. Kazemi, M. Tavana, M. Toloo and N.A. Zenkevich, A common weights model for investigating efficiency-based leadership in the russian banking industry. RAIRO: Oper. Res. 55 (2021) 213–229. [Google Scholar]
  • K. Khalili-Damghani and M. Fadaei, A comprehensive common weights data envelopment analysis model, ideal and anti-ideal virtual decision making units approach. J. Ind. Syst. Eng. 11 (2018) 281–306. [Google Scholar]
  • A. Kresta, Finding the best asset financing alternative: a DEA–WEO approach. Measurement 55 (2014) 288–294. [CrossRef] [Google Scholar]
  • K.F. Lam, Finding a common set of weights for ranking decision-making units in Data Envelopment Analysis. J. Econ. Bus. Manage. 4 (2016) 534–537. [CrossRef] [Google Scholar]
  • F.H.F. Liu and H.H. Peng, Ranking of units on the DEA frontier with common weights. Comput. Oper. Res. 35 (2008) 1624–1637. [CrossRef] [Google Scholar]
  • M. Luptáčik M. Luptá#ik and E. Nežinský,, E. Nežinský, Measuring income inequalities beyond the Gini coefficient. Cent. Eur. J. Oper. Res. 28 (2020) 561–578. [CrossRef] [MathSciNet] [Google Scholar]
  • S. Mehrabian, G.R. Jahanshahloo, M.R. Alirezaei and G.R. Amin, An assurance interval of the non-Archimedean epsilon in DEA models. Eur. J. Oper. Res. 48 (1998) 344–347. [Google Scholar]
  • L.P. Navas, F. Montes, S. Abolghasem, R.J. Salas, M. Toloo and R. Zarama, Colombian higher education institutions evaluation. Soc.-Econ. Plan. Sci. 71 (2020) 100801. [CrossRef] [Google Scholar]
  • A. Oukil, Ranking via composite weighting schemes under a DEA cross-evaluation framework. Comput. Ind. Eng. 117 (2018) 217–224. [CrossRef] [Google Scholar]
  • V.V. Podinovski and T. Bouzdine-Chameeva, Consistent weight restrictions in data envelopment analysis. Eur. J. Oper. Res. 244 (2015) 201–209. [CrossRef] [Google Scholar]
  • S. Ramazani-Tarkhorani, M. Khodabakhshi, S. Mehrabian and F. Nuri-Bahmani, Ranking decision-makingunits using common weights in DEA. Appl. Math. Model. 38 (2013) 3890–3896. [Google Scholar]
  • Y. Roll, W.D. Cook and B. Golany, Controlling factor weighs in data envelopment analysis. IIE Trans. 24 (2013) 1991. [Google Scholar]
  • J.L. Ruiz and I. Sirvent, Common benchmarking and ranking of units with DEA. Omega 65 (2016) 1–9. [CrossRef] [Google Scholar]
  • M. Salahi, N. Torabi and A. Amiri, An optimistic robust optimization approach to common set of weights in DEA. Measurement 93 (2016) 67–73. [CrossRef] [Google Scholar]
  • M. Salahi, M. Toloo and N. Torabi, A new robust optimization approach to common weights formulation in DEA. J. Oper. Res. Soc. 72 (2020) 1390–1402. [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) 185. [Google Scholar]
  • G.H. Shirdel and S. Ramezani-Tarkhorani, A new method for ranking decision making units using common set of weights: a developed criterion. J. Ind. Manage. Optim. 16 (2020) 633. [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]
  • J. Sun, J. Wu and D. Guo, Performance ranking of units considering ideal and anti-ideal DMU with common weights. Appl. Math. Model. 37 (2013) 6301–6310. [CrossRef] [MathSciNet] [Google Scholar]
  • M. Tavana and F.J. Santos-Arteaga, An integrated data envelopment analysis and mixed integer non-linear programming model for linearizing the common set of weights. Cent. Eur. J. Oper. Res. 27 (2019) 887–904. [CrossRef] [MathSciNet] [Google Scholar]
  • R.G. Thompson, F.D. Singleton Jr, R.M. Thrall and B.A. Smith, Comparative site evaluations for locating a high-energy physics lab in Texas. Interfaces 16 (1986) 35–49. [CrossRef] [Google Scholar]
  • G. Tohidi and F. Matroud, A new non-oriented model for classifying flexible measures in DEA. J. Oper. Res. Soc. 68 (2017) 1019–1029. [CrossRef] [Google Scholar]
  • M. Toloo, Alternative solutions for classifying inputs and outputs in data envelopment analysis. Comput. Math. Appl. 63 (2012) 1104–1110. [CrossRef] [MathSciNet] [Google Scholar]
  • M. Toloo, The most efficient unit without explicit inputs: an extended MILP-DEA model. Measurement 46 (2013) 3628–3634. [CrossRef] [Google Scholar]
  • M. Toloo, An epsilon-free approach for finding the most efficient unit in DEA. Appl. Math. Model. 38 (2014) 3182–3192. [Google Scholar]
  • M. Toloo, Selecting and full ranking suppliers with imprecise data: a new DEA method. Int. J. Adv. Manuf. Technol. 74 (2014) 1141–1148. [Google Scholar]
  • M. Toloo, A cost efficiency approach for strategic vendor selection problem under certain input prices assumption. Measurement 85 (2016) 175–183. [CrossRef] [Google Scholar]
  • M. Toloo and M. Mirbolouki, A new project selection method using data envelopment analysis. Comput. Ind. Eng. 138 (2019) 106119. [CrossRef] [Google Scholar]
  • M. Toloo and M. Salahi, A powerful discriminative approach for selecting the most efficient unit in DEA. Comput. Ind. Eng. 115 (2018) 269–277. [Google Scholar]
  • M. Toloo, B. Sohrabi and S. Nalchigar, A new method for ranking discovered rules from data mining by DEA. Expert Syst. Appl. 36 (2009) 8503–8508. [CrossRef] [Google Scholar]
  • M. Toloo, B. Ebrahimi and G.R. Amin, New data envelopment analysis models for classifying flexible measures: the role of non-Archimedean epsilon. Eur. J. Oper. Res. 292 (2021) 1037–1050. [CrossRef] [Google Scholar]
  • M. Toloo, E.K. Mensah and M. Salahi, Robust optimization and its duality in data envelopment analysis. Omega 108 (2022) 102583. [Google Scholar]
  • Y.M. Wang, Y. Luo and Y.X. Lan, Common weights for fully ranking decision making units by regression analysis. Expert Syst. Appl. 38 (2011) 9122–9128. [CrossRef] [Google Scholar]
  • J. Wu, J. Chu, Q. Zhu, Y. Li and L. Liang, Determining common weights in data envelopment analysis based on the satisfaction degree. J. Oper. Res. Soc. 67 (2016) 1446–1458. [Google Scholar]
  • A.P. Yekta, S. Kordrostami, A. Amirteimoori and R.K. Matin, Data envelopment analysis with common weights: the weight restriction approach. Math. Sci. 12 (2018) 197–203. [CrossRef] [MathSciNet] [Google Scholar]

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