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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. 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