Dea analysis professional formerly konsi data envelopment analysis dea

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. Google Scholar Chen, L., & Jia, G. Z. (2017). Environmental efficiency analysis of China’s regional industry: A data envelopment analysis (DEA) based approach. Journal of Cleaner Production, 142, 846–853. Google Scholar Chen, P.-C., Yu, M.-M., Shih, J.-C., Chang, C.-C., & Hsu, S.-H. (2019a). A reassessment of the global food security index by using a hierarchical data envelopment analysis approach. European Journal of Operational Research, 272, 687–698. Google Scholar Chen, Y., Cook, W. D., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Research, 196, 1170–1176. Google Scholar Chen, Y., Cook, W. D., Kao, C., & Zhu, J. (2013). Network DEA pitfalls: Divisional efficiency and frontier projection under general network structures. European Journal of Operational Research, 226(3), 507–515. Google Scholar Chen, Y., Cook, W. D., & Lim, S. (2019b). Preface: DEA and its applications in operations and data analytics. Annals of Operations Research, 278(1–2), 1–4. Google Scholar Chou, H. W., Lee, C. Y., Chen, H. K., & Tsai, M. Y. (2016). Evaluating airlines with slack-based measures and meta-frontiers. Journal of Advanced Transportation, 50(6), 1061–1089. Google Scholar Chu, J. F., Wu, J., & Song, M. L. (2018). An SBM-DEA model with parallel computing design for environmental efficiency evaluation in the big data context: A transportation system application. Annals of Operations Research, 270(1–2), 105–124. Google Scholar Cook, W. D., Chai, D., Doyle, J., & Green, R. (1998). Hierarchies and groups in DEA. Journal of Productivity Analysis, 10(2), 177–198. Google Scholar Cook, W. D., & Green, R. H. (2005). Evaluating power plant efficiency: A hierarchical model. Computers & Operations Research, 32(4), 813–823. Google Scholar Cook, W. D., Harrison, J., Imanirad, R., Rouse, P., & Zhu, J. (2013). Data envelopment analysis with non-homogeneous DMUs. Operations Research, 61(3), 666–676. Google Scholar Cook, W. D., Liang, L., & Zhu, J. (2010a). Measuring performance of two-stage network structures by DEA: A review

142, 513–523. Google Scholar Li, W. H., Liang, L., Cook, W. D., & Zhu, J. (2016). DEA models for non-homogeneous DMUs with different input configurations. European Journal of Operational Research, 254, 946–956. Google Scholar Li, Y., Wang, Y. Z., & Cui, Q. (2015). Evaluating airline efficiency: An application of virtual frontier network SBM. Transportation Research Part E: Logistics and Transportation Review, 81, 1–17. Google Scholar Liang, L., Cook, W. D., & Zhu, J. (2008). DEA models for two-stage processes: Game approach and efficiency decomposition. Naval Research Logistics, 55(7), 643–653. Google Scholar Liang, L., Yang, F., Cook, W. D., & Zhu, J. (2006). DEA models for supply chain efficiency evaluation. Annals of Operations Research, 145(1), 35–49. Google Scholar Lim, S., & Zhu, J. (2017). DEA and its applications in operations—Part I. INFOR, 55(3), 159–273. Google Scholar Lim, S., & Zhu, J. (2018). DEA and its applications in operations—Part II. INFOR, 56(3), 265–359. Google Scholar Lim, S., & Zhu, J. (2019). Primal–dual correspondence and frontier projections in two-stage network DEA models. OMEGA, 83, 236–248. Google Scholar Liu, D. (2017). Evaluating the multi-period efficiency of East Asia airport companies. Journal of Air Transport Management, 59, 71–82. Google Scholar Liu, J. S., Lu, L. Y., & Lu, W. (2016). Research fronts and prevailing applications in data envelopment analysis. In J. Zhu (Ed.), Data envelopment analysis (pp. 543–574). Berlin: Springer. Google Scholar Liu, J. S., Lu, L. Y., Lu, W., & Lin, B. J. (2013a). A survey of DEA applications. Omega, 41(5), 893–902. Google Scholar Liu, J. S., Lu, L. Y., Lu, W., & Lin, B. J. (2013b). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega, 41(1), 3–15. Google Scholar Liu, X. H., Chu, J. F., Yin, P. Z., & Sun, J. S. (2017). DEA cross-efficiency evaluation considering undesirable output and ranking priority: A case study of eco-efficiency analysis of coal-fired power plants. Journal of Cleaner Production, 142, 877–885. Google Scholar Lozano, S., & Gutiérrez, E. (2014). A slacks-based network DEA efficiency analysis of European airlines. Transportation Planning and Technology, 37(7), 623–637. Google Scholar Mahajan, J. (1991). A data envelopment analytic model for assessing the relative efficiency of the selling function. European Journal of Operational Research, 53(2), 189–205. Google Scholar Mahdiloo, M., Jafarzadeh, A. H., Saen, R. F., Tatham, P., & Fisher, R. (2016). A multiple criteria approach to two-stage data envelopment analysis. Transportation Research Part D: Transport and Environment, 46, 317–327. Google Scholar Mallikarjun, S. (2015). Efficiency of US airlines: A strategic operating model. Journal of Air Transport Management, 43, 46–56. Google Scholar Misiunas, N., Oztekin, A., Chen, Y., & Chandra, K. (2016). DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional

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

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). Allocation of carbon dioxide emission permits with the minimum cost for Chinese provinces in big data environment. Journal of Cleaner Production, 142, 886–893. Google Scholar Aparicio, J., Pastor, J. T., Vidal, F., & Zofío, J. L. (2017). Evaluating productive performance: A new approach based on the product-mix problem consistent with Data envelopment analysis. OMEGA, 67, 134–144. Google Scholar Azadi, M., Shabani, A., Khodakarami, M., & Saen, R. F. (2015). Reprint of “Planning in feasible region by two-stage target-setting DEA methods: An application in green supply chain management of public transportation service providers”. Transportation Research Part E: Logistics and Transportation Review, 74, 22–36. Google Scholar Badiezadeh, T., Saen, R. F., & Samavati, T. (2018). Assessing sustainability of supply chains by double frontier network DEA: A big data approach. Computers & Operations Research, 98, 284–290. Google Scholar Chang, Y. C., & Yu, M. M. (2014). Measuring production and consumption efficiencies using the slack-based measure network data envelopment analysis approach: The case of low-cost carriers. Journal of Advanced Transportation, 48(1), 15–31. Google Scholar Chang, Y. T., Park, H. K., Zou, B., & Kafle, N. (2016). Passenger facility charge versus airport improvement program funds: A dynamic network DEA analysis for US airport financing. Transportation Research Part E: Logistics and Transportation Review, 88, 76–93. Google Scholar Chao, S. L. (2017). Integrating multi-stage data envelopment analysis and a fuzzy analytical hierarchical process to evaluate the efficiency of major global liner shipping companies. Maritime Policy & Management, 44(4), 496–511. Google Scholar Chao, S. L., Yu, M. M., & Hsieh, W. F. (2018). Evaluating the efficiency of major container shipping companies: A framework of dynamic network DEA with shared inputs. Transportation Research Part A: Policy and Practice, 117, 44–57. Google Scholar Charles, V., Aparicio, J., & Zhu, J. (2019). The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis. European Journal of Operational Research, 279(3), 929–940. Google Scholar Charles, V., Aparicio, J., & Zhu, J. (2020a). Data science for better productivity. Journal of the Operational Research Society. (in press).Charles, V., Aparicio, J., & Zhu, J. (Eds.). (2020b). Preface. In Data science and productivity analytics. New York: Springer.Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the inefficiency of decision making units. European Journal of

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.