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Operational Research, 2(6), 429–444. Google Scholar Chen, C., & Lam, J. S. L. (2018). Sustainability and interactivity between cities and ports: A two-stage data envelopment analysis (DEA) approach. Maritime Policy & Management, 45, 1–18. Google Scholar Chen, C.-M. (2009). A network-DEA model with new efficiency measures to incorporate the dynamic effect in production networks. European Journal of Operational Research, 194, 687–699. Google Scholar Chen, K., Cook, W. D., & Zhu, J. (2020). A conic relaxation model for searching global optimum of network data envelopment analysis. European Journal of Operational Research, 280(1), 242–253. Google Scholar Chen, K., & Zhu, J. (2017). Second order cone programming approach to two-stage network data envelopment analysis. European Journal of Operational Research, 262, 231–238. Google Scholar Chen, K., & Zhu, J. (2020). Additive slacks-based measure: Computational strategy and extension to network DEA. OMEGA, 91, 102022. 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DEA Analysis Professional (formerly KonSi Data

FUTURE events / Conferences / Call for Papers Online DEA & SFA course – 3 days – October 2025 Call for papers – book chapters [Advanced Data Analytics, Machine Learning and AI in Business] Call for papers – North American Productivity Workshop (NAPW XII), June 9 – 12, 2025 Virginia Tech Research Center/Arlington, Virginia Call for papers – DEA at EURO2025, University of Leeds from June 22 to 25 Call for papers – DEA at 5th IMA and OR Society Conference on the Mathematics of Operational Research, April 30 to May 2, 2025 Call for papers – book chapters [Advancing DEA: Bridging Theory and Practice] Call for Papers: AI for Sustainable Performance Analytics, November 23-26, 2024, Doha, Qatar Performance Analytics, AI, And Sustainability Workshop, May 30-31, 2024, University Of Surrey, Guildford, UK Call for papers: DEA in EURO204 conference, Copenhagen, June 30th – July 3rd, 2024 Online DEA & SFA course – 3 days – June 2024 PAST events / Conferences / Call for Papers ICBAP2025: International Conference on Business Analytics in Practice, August 24-27, 2025, University of Piraeus, Greece Annals of Operations Research Special Issue: In Memoriam of Professor Rajiv Banker on the New Developments in Data Envelopment Analysis and Its Applications Call for Papers: Sustainability Analytics and NetZero, October 17-19, 2023, Qatar Lecturer/Senior Lecturer in Business Analytics ICBAP: International Conference on Business Analytics in Practice, Jan 8-11, 2024, Sharjah, UAE Call for Papers: Intelligent Search Engines (Machine Learning with Applications) International Conference on Data Envelopment Analysis, Surrey Business School, University of Surrey, UK, September 4-6, 2023 4th IMA and OR Society Conference on Mathematics of Operational Research, BIRMINGHAM 27-28 APRIL 2023 Call for papers: Environmental Science and Policy; Special issue on “DEA-based index systems for addressing the United Nations’ SDGs”">Call for papers: Environmental Science and Policy; Special issue. Download DEA Analysis Professional (formerly KonSi Data Envelopment Analysis DEA) 5.1 - A software utility you can use to perform

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Using a SBM-NDEA model in the presence of shared input. Journal of Air Transport Management, 34, 146–153. Google Scholar Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243–252. Google Scholar Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. OMEGA, 42, 124–131. Google Scholar Wu, Y. M., Chen, Z. X., & Xia, P. P. (2018). An extended DEA-based measurement for eco-efficiency from the viewpoint of limited preparation. Journal of Cleaner Production, 195, 721–733. Google Scholar Yang, C. L., Yuan, B. J. C., Huang, C. Y., & Chang, C. N. (2015). Evaluating the performance of disaster recovery systemic innovations by using the data envelopment analysis. Asia Pacific Journal of Innovation and Entrepreneurship, 9(2), 51–75. Google Scholar Yu, M. M., & Chen, L. H. (2016). Centralized resource allocation with emission resistance in a two-stage production system: Evidence from a Taiwan’s container shipping company. Transportation Research Part A: Policy and Practice, 94, 650–671. Google Scholar Yu, M. M., Chen, L. H., & Chiang, H. (2017). The effects of alliances and size on airlines’ dynamic operational performance. Transportation Research Part A: Policy and Practice, 106, 197–214. Google Scholar Zhang, W., Pan, X. F., Yan, Y. B., & Pan, X. Y. (2017). Convergence analysis of regional energy efficiency in China based on large-dimensional panel data model. Journal of Cleaner Production, 142, 801–808. Google Scholar Zhu, J. (2011). Airlines performance via two-stage network DEA approach. Journal of CENTRUM Cathedra, 4(2), 260–269. Google Scholar Zhu, J. (2013). Efficiency evaluation with strong ordinal input and output measures. European Journal of Operational Research, 146(3), 477–485. Google Scholar Zhu, Q., Wu, J., & Song, M. (2018). Efficiency evaluation based on data envelopment analysis in the big data context. Computers & Operations Research, 98, 291–300. Google Scholar Zhu, Q. Y., Wu, J., Li, X. C., & Xiong, B. B. (2017). China’s regional natural resource allocation and utilization: A DEA-based approach in a big data environment. Journal of Cleaner Production, 142, 809–818. Google Scholar Download references ReferencesAfsharian, M. (2019). A frontier-based facility location problem with a centralized view of measuring the performance of the network. Journal of the Operational Research Society. Google Scholar Amirteimoori, A., Kordrostami, S., & Azizi, H. (2016). Additive models for network data envelopment analysis in the presence of shared resources. Transportation Research Part D: Transport and Environment, 48, 411–424. Google Scholar An, Q. X., We, Y., Xiong, B. B., Yang, M., & Chen, X. H. (2017). 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And future perspective. OMEGA, 38, 423–430. Google Scholar Cook, W. D., Ramón, N., Ruiz, J. L., Sirvent, I., & Zhu, J. (2019). DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans. OMEGA, 84, 45–54. Google Scholar Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)—30 years on. European Journal of Operational Research, 192(1), 1–17. Google Scholar Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. OMEGA, 44, 1–4. Google Scholar Cook, W. D., Zhu, J., Bi, G.-B., & Yang, F. (2010b). Network DEA: Additive efficiency decomposition. European Journal of Operational Research, 207(2), 1122–1129. Google Scholar Cooper, W. W., Seiford, L. M., & Zhu, J. (2004). Handbook on data envelopment analysis. Boston: Kluwer Academic Publishers. Google Scholar Cui, Q., & Li, Y. (2016). Airline energy efficiency measures considering carbon abatement: A new strategic framework. Transportation Research Part D: Transport and Environment, 49, 246–258. Google Scholar Cui, Q., & Li, Y. (2017). Airline efficiency measures using a dynamic epsilon-based measure model. Transportation Research Part A: Policy and Practice, 100, 121–134. Google Scholar Cui, Q., & Li, Y. (2018). CNG2020 strategy and airline efficiency: A network epsilon-based measure with managerial disposability. International Journal of Sustainable Transportation, 12(5), 313–323. Google Scholar Cui, Q., Li, Y., & Lin, J. L. (2018). Pollution abatement costs change decomposition for airlines: An analysis from a dynamic perspective. Transportation Research Part A: Policy and Practice, 111, 96–107. Google Scholar Cui, Q., Li, Y., & Wei, Y. M. (2017). Exploring the impacts of EU ETS on the pollution abatement costs of European airlines: An application of network environmental production function. Transport Policy, 60, 131–142. Google Scholar Cui, Q., Wei, Y. M., Yu, C. L., & Li, Y. (2016). Measuring the energy efficiency for airlines under the pressure of being included into the EU ETS. Journal of Advanced Transportation, 50(8), 1630–1649. Google Scholar Díaz-Hernández, J. J., Martínez-Budría, E., & Salazar-González, J. J. (2014). Measuring cost efficiency in the presence of quasi-fixed inputs using dynamic data envelopment analysis: The case of port infrastructure. Maritime Economics & Logistics, 16(2), 111–126. Google Scholar Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34, 35–49. Google Scholar Gan, G., Lee, H.-S., Lee, L., Wang, X., & Wang, Q. (2019). Network hierarchical DEA with an application to international shipping industry in Taiwan. Journal of the Operational Research Society. Google Scholar Gong, B. G., Guo, D. D., Zhang, X. Q., & Cheng, J. S. (2017). An approach for evaluating cleaner production performance in iron and steel enterprises involving competitive relationships. Journal of Cleaner Production, 142, 739–748. Google Scholar Halická, M., & Trnovská, M. (2018). The Russell measure model: Computational aspects, duality, and profit efficiency.

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Status. OMEGA, 58(2016), 46–54. Google Scholar Olesen, O. B., Petersen, N. C., & Podinovski, V. V. (2015). Efficiency analysis with ratio measures. European Journal of Operational Research, 245(2), 446–462. Google Scholar Olfat, L., Amiri, M., Soufi, J. B., & Pishdar, M. (2016). A dynamic network efficiency measurement of airports performance considering sustainable development concept: A fuzzy dynamic network-DEA approach. Journal of Air Transport Management, 57, 272–290. Google Scholar Omrani, H., & Keshavarz, M. (2016). A performance evaluation model for supply chain of shipping company in Iran: An application of the relational network DEA. Maritime Policy & Management, 43(1), 121–135. Google Scholar Omrani, H., & Soltanzadeh, E. (2016). Dynamic DEA models with network structure: An application for Iranian airlines. Journal of Air Transport Management, 57, 52–61. Google Scholar Shao, Y., & Sun, C. (2016). Performance evaluation of China’s air routes based on network data envelopment analysis approach. Journal of Air Transport Management, 55, 67–75. Google Scholar Shen, Y., Hermans, E., Brijs, T., Wets, G., & Vanhoof, K. (2012). Road safety risk evaluation and target setting using data envelopment analysis and its extensions. Accident Analysis and Prevention, 48, 430–441. Google Scholar Shwartz, M., Burgess, J. F., Jr., & Zhu, J. (2016). A DEA based composite measure of quality and its associated data uncertainty interval for health care provider profiling and pay-for-performance. European Journal of Operational Research, 253(2), 489–502. Google Scholar Song, M. L., Cen, L., Zheng, Z. X., Fisher, R., Liang, X., Wang, Y. T., et al. (2017). How would big data support societal development and environmental sustainability? Insights and practices. Journal of Cleaner Production, 142, 489–500. Google Scholar Song, M. L., Fisher, R., Wang, J. L., & Cui, L. B. (2018). Environmental performance evaluation with big data: Theories and methods. Annals of Operations Research, 270(1–2), 459–472. Google Scholar Song, M. L., & Wang, S. H. (2017). Participation in global value chain and green technology progress: Evidence from big data of Chinese enterprises. Environmental Science and Pollution Research, 24(2), 1648–1661. Google Scholar Storto, C. L. (2018). The analysis of the cost-revenue production cycle efficiency of the Italian airports: A NSBM DEA approach. Journal of Air Transport Management, 72, 77–85. Google Scholar Summerfield, N., Deoka, A., Xu, M., & Zhu, W. W. (2019). Should driver cooperate? Performance evaluation of cooperative navigation on simulated road networks using network DEA. Journal of the Operational Research Society. Google Scholar Sun, Y. X., Yu, X. B., Tan, Z. F., Xu, X. F., & Yan, Q. Y. (2017). Efficiency evaluation of operation analysis systems based on dynamic data envelope analysis models from a big data perspective. Applied Sciences-Basel, 7(6), 14. Google Scholar Tavassoli, M., Faramarzi, G. R., & Saen, R. F. (2014). Efficiency and effectiveness in airline performance. Download DEA Analysis Professional (formerly KonSi Data Envelopment Analysis DEA) 5.1 - A software utility you can use to perform DEA Analysis Professional (formerly known as KonSi Data Envelopment Analysis) is a standalone software for performance measurement using DEA. It is widely adopted in

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America’s top-rated MBA programs. Eur J Oper Res 189(2008):245–268 Google Scholar Ray SC, Mukherjee K (2016) Data envelopment analysis with aggregated inputs and a test of allocative efficiency when input prices vary across firms. Data Envel Anal J 2(2):141–161 Google Scholar Ray SC, Chen L, Mukherjee K (2008) Input price variation across locations and a generalized measure of cost efficiency. Int J Prod Econ 116:208–218 Google Scholar Ray SC, Mukherjee K, Venkatesh A (2018) Nonparametric measures of efficiency in the presence of undesirable outputs: a by-production approach with weak disposability. Empir Econ 54(1):31–65 Google Scholar Ray SC, Walden J, Chen L (2018) Economic Measures of Capacity Utilization: A Nonparametric Cost Function Analysis. Working Paper 2018--02, University of Connecticut, Department of Economics Google Scholar Rodseth KL (2015) Axioms of a polluting technology: a materials balance approach. Environ Res Econ 67(1):1–22. Online October 2015 Google Scholar Rodseth KL (2016) Environmental efficiency measurement and the materials balance condition reconsidered. Eur J Oper Res 250:342–346 Google Scholar Ruggiero J (1998) Non-discretionary inputs in data envelopment analysis. Eur J Oper Res 111:461–469 Google Scholar Seiford L, Zhu J (1999) An investigation of returns to scale in data envelopment analysis. Omega Int J Manag Sci 27:1–11 Google Scholar Shephard RW (1953) Cost and production functions. Princeton University Press, Princeton Google Scholar Shephard RW (1970) Theory of cost and production functions. Princeton University Press, Princeton Google Scholar Tone K (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130:498–509 Google Scholar Tone K (2002) A strange case of the cost and allocative efficiencies in DEA. J Oper Res Soc 53:1225–1231 Google Scholar Varian HR (1984) The nonparametric approach to production analysis. Econometrica 52(3):579–597 Google Scholar Zhu J (2003) Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets and DEA excel solver. Kluwer Academic, Boston Google Scholar Download referencesAuthor informationAuthors and AffiliationsDepartment of Economics, University of Connecticut, Storrs, CT, USASubhash C. RayAuthorsSubhash C. RayYou can also search for this author in PubMed Google ScholarCorresponding authorCorrespondence to Subhash C. Ray .Editor informationEditors and AffiliationsDepartment of Economics, University of Connecticut, Storrs, CT, USASubhash C. Ray Department of Agricultural and Resource Economics, University of Maryland, College Park, MD, USARobert Chambers Department of Economics, Binghamton University, Binghamton, NY, USASubal Kumbhakar Rights and permissionsCopyright information© 2020 Springer Nature Singapore Pte Ltd.About this entryCite this entryRay, S.C. (2020). Data Envelopment Analysis: A Nonparametric Method of Production Analysis. In: Ray, S., Chambers, R., Kumbhakar, S. (eds) Handbook of Production Economics. Springer, Singapore. citation.RIS.ENW.BIBDOI: 20 December 2019Accepted: 01 March 2020Published: 05 August 2020 Publisher Name: Springer, Singapore Print ISBN: 978-981-10-3450-3 Online ISBN: 978-981-10-3450-3eBook Packages: Living Reference Economics and FinanceReference Module Humanities and Social SciencesReference Module Business, Economics and Social SciencesPublish with us