Preserving cost and revenue efficiency through inverse data envelopment analysis models

References

• Amin GR, Emrouznejad A, Gattoufi S. 2017. Minor and major consolidations in inverse DEA: definition and determination. Comput Ind Eng. 103(Supplement C):193–200.
   Web of Science ®Google Scholar
• Aparicio J, Borras F, Pastor JT, Vidal F. 2015. Measuring and decomposing firms revenue and cost efficiency: the Russell measures revisited. Int J Prod Econ. 165:19–28.
   Web of Science ®Google Scholar
• Charnes A, Cooper WW, Rhodes E. 1978. Measuring the efficiency of decision making units. Eur J Oper Res. 6(2):429–444.
   Web of Science ®Google Scholar
• Chen L, Wang Y, Lai F, Feng F. 2017. An investment analysis for China’s sustainable development based on inverse data envelopment analysis. J Clean Prod. 142(4):1638–1649.
   Web of Science ®Google Scholar
• Ehrgott M. 2005. Multicriteria optimization. Berlin: Springer.
   Google Scholar
• Eyni M, Tohidi G, Mehrabeian S. 2017. Applying inverse DEA and cone constraint to sensitivity analysis of DMUs with undesirable inputs and outputs. J Oper Res Soc. 68(1):34–40.
   Web of Science ®Google Scholar
• Fare R, Grosskopf S, Lovell CAK. 1985. The measurement of efficiency of production. Boston: Kluwer Academic Publisher.
   Google Scholar
• Ferrier GD, Lovell CA. 1990. Measuring cost efficiency in banking: econometric and linear programming evidence. J Econom. 46(1–2):229–245.
   Web of Science ®Google Scholar
• Ghiyasi M. 2017. Inverse DEA based on cost and revenue efficiency. Comput Ind Eng. 114:258–263.
   Web of Science ®Google Scholar
• Ghobadi S, Jahangiri S. 2015. Inverse DEA: review, extension and application. Int J Inf Technol Decis Mak. 14(4):805–824.
   Web of Science ®Google Scholar
• Hadi-Vencheh A, Foroughi AA, Soleimani-Damaneh M. 2008. A DEA model for resource allocation. Econ Model. 25(5):983–993.
   Web of Science ®Google Scholar
• Hadi-Vencheh A, Hatami-Marbini A, Ghelej Beigi Z, Gholami K. 2015. An inverse optimization model for imprecise data envelopment analysis. Opimization. 64(11):2441–2452.
   Web of Science ®Google Scholar
• Jahanshahloo GR, Lotfi FH, Shoja N, Tohidi G, Razavyan S. 2004a. The outputs estimation of a DMU according to improvement of its efficiency. Appl Math Comput. 147(2):409–413.
   Web of Science ®Google Scholar
• Jahanshahloo GR, Lotfi FH, Shoja N, Tohidi G, Razavyan S. 2004b. Input estimation and identification of extra inputs in inverse DEA models. Appl Math Comput. 156(2):427–437.
   Web of Science ®Google Scholar
• Jahanshahloo GR, Hoseinzadeh Lotfi F, Rostami-Malkhalifeh M, Ghobadi S. 2014. Using enhanced Russell model to solve inverse data envelopment analysis problems. Sci World J. 2014:571896.
   Web of Science ®Google Scholar
• Jahanshahloo GR, Soleimani-Damaneh M, Ghobadi S. 2015. Inverse DEA under inter-temporal dependence using multiple-objective programming. Eur J Oper Res. 240(2):447–456.
   Web of Science ®Google Scholar
• Jimborean R, Brack E. 2010. The cost-efficiency of French banks. MPRA Paper, 23471 University Library of Munich, Germany.
   Google Scholar
• Kuosmanen T, Post T. 2001. Measuring economic efficiency with incomplete price information: With an application to European commercial banks. Eur J Oper Res. 134(1):43.
   Web of Science ®Google Scholar
• Lertworasirikul S, Charnsethikul P, Fang SC. 2011. Inverse data envelopment analysis model to preserve relative efficiency values: the case of variable returns to scale. Comput Ind Eng. 61(4):1017–1023.
   Web of Science ®Google Scholar
• Li XY, Cui JC. 2008. A comprehensive DEA approach for the resource allocation problem based on scale economies classification. J Syst Sci Complex. 21(4):540–557.
   Web of Science ®Google Scholar
• Mostafaee A, Saljooghi FH. 2010. Cost efficiency measures in data envelopment analysis with data uncertainty. Eur J Oper Res. 202(2):595–603.
   Web of Science ®Google Scholar
• Mozaffari MR, Kamyab P, Jablonsky J, Gerami J. 2014. Cost and revenue efficiency in DEA-R models. Comput Ind Eng. 78:188–194.
   Web of Science ®Google Scholar
• Puig-Junoy J. 2000. Partitioning input cost efficiency into its allocative and technical components: an empirical DEA application to hospitals. Socio-Econ Plan Sci. 34(3):199–218.
   Google Scholar
• Ray SC, Kim HJ. 1995. Cost efficiency in the US steel industry: A nonparametric analysis using data envelopment analysis. Eur J Oper Res. 80(3):654–671.
   Web of Science ®Google Scholar
• Sahoo BK, Tone K. 2013. Non-parametric measurement of economies of scale and scope in non-competitive environment with price uncertainty. Omega. 41(1):97–111.
   Web of Science ®Google Scholar
• Sahoo BK, Mehdiloozad M, Tone K. 2014. Cost, revenue and profit efficiency measurement in DEA: a directional distance function approach. Eur J Oper Res. 237(3):921–931.
   Web of Science ®Google Scholar
• Shiraz RK, Hatami-Marbini A, Emrouznejad A, Fukuyama H. 2018. Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs. Oper Res. 18(1):1–36.
   Web of Science ®Google Scholar
• Tone K. 2002. A strange case of the cost and allocative efficiencies in DEA. J Oper Res Soc. 53(11):1225–1231.
   Web of Science ®Google Scholar
• Tone K, Sahoo BK. 2006. Re-examining scale elasticity in DEA. Ann Oper Res. 145(1):69–87.
   Web of Science ®Google Scholar
• Wei QL, Zhang JZ, Zhang XS. 2000. An inverse DEA model for inputs/outputs estimate. Eur J Oper Res. 121(1):151–163.
   Web of Science ®Google Scholar
• Yan H, Wei QL, Hao G. 2002. DEA models for resource reallocation and production input/output estimation. Eur J Oper Res. 136(1):19–31.
   Web of Science ®Google Scholar