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How to cite this paper
APA: Dastani, F., Tohidi, G., Sanei, M., Lotfi, F & Razavyan, S. (2025). Multi-period efficiency evaluation of heterogeneous decision-making units: Assessing efficiency before, during, and after COVID-19. Decision Science Letters, 14(4), 861-876.
Chicago/Turabian: Dastani, F., Tohidi, G., Sanei, M., Lotfi, F & Razavyan, S. 2025. "Multi-period efficiency evaluation of heterogeneous decision-making units: Assessing efficiency before, during, and after COVID-19." Decision Science Letters 14, no. 4 (2025): 861-876.
AMA: Dastani, F., Tohidi, G., Sanei, M., Lotfi, F & Razavyan, S. Multi-period efficiency evaluation of heterogeneous decision-making units: Assessing efficiency before, during, and after COVID-19. Decision Science Letters. 2025;14(4):861-876.
References
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Atici, K. B., & Podinovski, V. V. (2015). Using data envelopment analysis for the assessment of technical efficiency of units with different specialisations: An application to agriculture. Omega, 54, 72–83. https://doi.org/10.1016/j.omega.2015.01.015
Barat, M., Tohidi, G., & Sanei, M. (2019). DEA for heterogeneous mixed networks. Asia Pacific Management Review, 24(2), 161–166. https://doi.org/10.1016/j.apmrv.2018.02.003
Barat, M., Tohidi, G., Sanei, M., & Razavyan, S. (2019). Data envelopment analysis for decision making unit with heterogeneous internal structures: An application to the banking industry. Journal of the Operational Research Society, 70(5), 760–769. https://doi.org/10.1080/01605682.2018.1457483
Castelli, L., Pesenti, R., & Ukovich, W. (2004). DEA-like models for the efficiency evaluation of hierarchically structured units. European Journal of Operational Research, 154(2), 465–476. https://doi.org/10.1016/S0377-2217(03)00182-6
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
Chen, M., Ang, S., Yang, F., & Jiang, L. (2021). Efficiency evaluation of non-homogeneous DMUs with inconsistent input quality. Computers & Industrial Engineering, 158, 107418. https://doi.org/10.1016/j.cie.2021.107418
Cook, W. D., Harrison, J., Imanirad, R., Rouse, P., & Zhu, J. (2013). Data envelopment analysis with heterogeneous dmus. Operations Research, 61(3), 666–676. https://doi.org/10.1287/opre.2013.1173
Cook, W. D., Harrison, J., Rouse, P., & Zhu, J. (2012). Relative efficiency measurement: The problem of a missing output in a subset of decision making units. European Journal of Operational Research, 220(1), 79–84. https://doi.org/10.1016/j.ejor.2012.01.022
Du, J., Chen, Y., & Huo, J. (2015). DEA for non-homogenous parallel networks. Omega, 56, 122–132. https://doi.org/10.1016/j.omega.2014.10.001
Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research, 132(2), 245–259. https://doi.org/10.1016/S0377-2217(00)00149-1
Günaydın, Y., Correia, A., & Kozak, M. (2022). Comparing efficiency in all-inclusive and bed and breakfast hotel businesses: A multi-period data envelopment analysis in Turkey. European Journal of Management and Business Economics, 31(4), 439–452. https://doi.org/10.1108/EJMBE-11-2021-0308
Haas, D. A., & Murphy, F. H. (2003). Compensating for non-homogeneity in decision-making units in data envelopment analysis. European Journal of Operational Research, 144(3), 530–544. https://doi.org/10.1016/S0377-2217(02)00139-X
Huang, C., Tu, C., Luo, Z., Chiu, Y., & Wang, Z. (2015). Using the heterogeneous frontier two‐stage DEA model to assess the efficiencies of expense utilization and operation of the Taiwanese hotel industry. International Transactions in Operational Research, 23(6), 1067–1087. https://doi.org/10.1111/itor.12180
Kao, C., & Liu, S.-T. (2014). Multi-period efficiency measurement in data envelopment analysis: The case of Taiwanese commercial banks. Omega, 47, 90–98. https://doi.org/10.1016/j.omega.2013.09.001
Kyrgiakos, L. S., Kleftodimos, G., Vlontzos, G., & Pardalos, P. M. (2023). A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability. Operational Research, 23(1), 7. https://doi.org/10.1007/s12351-023-00741-5
Li, Y., Lei, X., & Morton, A. (2019). Performance evaluation of heterogeneous hospitals: The case of Hong Kong hospitals. Health Care Management Science, 22(2), 215–228. https://doi.org/10.1007/s10729-018-9433-y
Li, W., Liang, L., Cook, W. D., & Zhu, J. (2016). DEA models for non-homogeneous DMUs with different input configurations. European Journal of Operational Research, 254(3), 946–956. https://doi.org/10.1016/j.ejor.2016.04.063
Lin, S., Rouse, P., Wang, Y.-M., Lin, L., & Zheng, Z.-Q. (2023). Performance measurement of heterogeneous Hong Kong hospitals using directional distance functions. Health Care Management Science, 26(2), 330–343. https://doi.org/10.1007/s10729-022-09625-0
Moghaddas, Z., Oukil, A., & Vaez-Ghasemi, M. (2022). Global multi-period performance evaluation – New model and productivity index. RAIRO - Operations Research, 56(3), 1503–1521. https://doi.org/10.1051/ro/2022065
Panwar, A., Olfati, M., Pant, M., & Snasel, V. (2022). A review on the 40 years of existence of data envelopment analysis models: Historic development and current trends. Archives of Computational Methods in Engineering, 29(7), 5397–5426. https://doi.org/10.1007/s11831-022-09770-3
Razavi Hajiagha, S. H., Hashemi, S. S., Amoozad Mahdiraji, H., & Azaddel, J. (2015). Multi-period data envelopment analysis based on Chebyshev inequality bounds. Expert Systems with Applications, 42(21), 7759–7767. https://doi.org/10.1016/j.eswa.2015.06.008
Saen, R. F., Memariani, A., & Lotfi, F. H. (2005). Determining relative efficiency of slightly non-homogeneous decision making units by data envelopment analysis: A case study in IROST. Applied Mathematics and Computation, 165(2), 313–328. https://doi.org/10.1016/j.amc.2004.04.050
Singh, S., & Ranjan, P. (2018). Efficiency analysis of Heterogeneous parallel sub-unit systems for the performance measurement of higher education. Annals of Operations Research, 269, 641-666.
Xu, Y., Zhang, H., Cheng, K., Zhang, Z., & Chen, Y. (2021). Efficiency measurement in multi-period network DEA model with feedback. Expert Systems with Applications, 175, 114815. https://doi.org/10.1016/j.eswa.2021.114815
Yan, Q., Zhao, F., Wang, X., Yang, G., Baležentis, T., & Streimikiene, D. (2019). The network data envelopment analysis models for non-homogenous decision making units based on the sun network structure. Central European Journal of Operations Research, 27(4), 1221–1244. https://doi.org/10.1007/s10100-018-0560-9
Zha, Y., Song, A., Xu, C., & Yang, H. (2013). Dealing with missing data based on data envelopment analysis and halo effect. Applied Mathematical Modelling, 37(9), 6135–6145. https://doi.org/10.1016/j.apm.2012.11.015
Zhu, W., Yu, Y., & Sun, P. (2018). Data envelopment analysis cross-like efficiency model for non-homogeneous decision-making units: The case of United States companies’ low-carbon investment to attain corporate sustainability. European Journal of Operational Research, 269(1), 99–110. https://doi.org/10.1016/j.ejor.2017.08.007
Asadi, F., Kordrostami, S., Amirteimoori, A., & Jahani Sayyad Noveiri, M. (2023). An inverse multi-period FDH model with undesirable outputs. Croatian Operational Research Review, 14(2), 111–124. https://doi.org/10.17535/crorr.2023.0010
Atici, K. B., & Podinovski, V. V. (2015). Using data envelopment analysis for the assessment of technical efficiency of units with different specialisations: An application to agriculture. Omega, 54, 72–83. https://doi.org/10.1016/j.omega.2015.01.015
Barat, M., Tohidi, G., & Sanei, M. (2019). DEA for heterogeneous mixed networks. Asia Pacific Management Review, 24(2), 161–166. https://doi.org/10.1016/j.apmrv.2018.02.003
Barat, M., Tohidi, G., Sanei, M., & Razavyan, S. (2019). Data envelopment analysis for decision making unit with heterogeneous internal structures: An application to the banking industry. Journal of the Operational Research Society, 70(5), 760–769. https://doi.org/10.1080/01605682.2018.1457483
Castelli, L., Pesenti, R., & Ukovich, W. (2004). DEA-like models for the efficiency evaluation of hierarchically structured units. European Journal of Operational Research, 154(2), 465–476. https://doi.org/10.1016/S0377-2217(03)00182-6
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
Chen, M., Ang, S., Yang, F., & Jiang, L. (2021). Efficiency evaluation of non-homogeneous DMUs with inconsistent input quality. Computers & Industrial Engineering, 158, 107418. https://doi.org/10.1016/j.cie.2021.107418
Cook, W. D., Harrison, J., Imanirad, R., Rouse, P., & Zhu, J. (2013). Data envelopment analysis with heterogeneous dmus. Operations Research, 61(3), 666–676. https://doi.org/10.1287/opre.2013.1173
Cook, W. D., Harrison, J., Rouse, P., & Zhu, J. (2012). Relative efficiency measurement: The problem of a missing output in a subset of decision making units. European Journal of Operational Research, 220(1), 79–84. https://doi.org/10.1016/j.ejor.2012.01.022
Du, J., Chen, Y., & Huo, J. (2015). DEA for non-homogenous parallel networks. Omega, 56, 122–132. https://doi.org/10.1016/j.omega.2014.10.001
Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research, 132(2), 245–259. https://doi.org/10.1016/S0377-2217(00)00149-1
Günaydın, Y., Correia, A., & Kozak, M. (2022). Comparing efficiency in all-inclusive and bed and breakfast hotel businesses: A multi-period data envelopment analysis in Turkey. European Journal of Management and Business Economics, 31(4), 439–452. https://doi.org/10.1108/EJMBE-11-2021-0308
Haas, D. A., & Murphy, F. H. (2003). Compensating for non-homogeneity in decision-making units in data envelopment analysis. European Journal of Operational Research, 144(3), 530–544. https://doi.org/10.1016/S0377-2217(02)00139-X
Huang, C., Tu, C., Luo, Z., Chiu, Y., & Wang, Z. (2015). Using the heterogeneous frontier two‐stage DEA model to assess the efficiencies of expense utilization and operation of the Taiwanese hotel industry. International Transactions in Operational Research, 23(6), 1067–1087. https://doi.org/10.1111/itor.12180
Kao, C., & Liu, S.-T. (2014). Multi-period efficiency measurement in data envelopment analysis: The case of Taiwanese commercial banks. Omega, 47, 90–98. https://doi.org/10.1016/j.omega.2013.09.001
Kyrgiakos, L. S., Kleftodimos, G., Vlontzos, G., & Pardalos, P. M. (2023). A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability. Operational Research, 23(1), 7. https://doi.org/10.1007/s12351-023-00741-5
Li, Y., Lei, X., & Morton, A. (2019). Performance evaluation of heterogeneous hospitals: The case of Hong Kong hospitals. Health Care Management Science, 22(2), 215–228. https://doi.org/10.1007/s10729-018-9433-y
Li, W., Liang, L., Cook, W. D., & Zhu, J. (2016). DEA models for non-homogeneous DMUs with different input configurations. European Journal of Operational Research, 254(3), 946–956. https://doi.org/10.1016/j.ejor.2016.04.063
Lin, S., Rouse, P., Wang, Y.-M., Lin, L., & Zheng, Z.-Q. (2023). Performance measurement of heterogeneous Hong Kong hospitals using directional distance functions. Health Care Management Science, 26(2), 330–343. https://doi.org/10.1007/s10729-022-09625-0
Moghaddas, Z., Oukil, A., & Vaez-Ghasemi, M. (2022). Global multi-period performance evaluation – New model and productivity index. RAIRO - Operations Research, 56(3), 1503–1521. https://doi.org/10.1051/ro/2022065
Panwar, A., Olfati, M., Pant, M., & Snasel, V. (2022). A review on the 40 years of existence of data envelopment analysis models: Historic development and current trends. Archives of Computational Methods in Engineering, 29(7), 5397–5426. https://doi.org/10.1007/s11831-022-09770-3
Razavi Hajiagha, S. H., Hashemi, S. S., Amoozad Mahdiraji, H., & Azaddel, J. (2015). Multi-period data envelopment analysis based on Chebyshev inequality bounds. Expert Systems with Applications, 42(21), 7759–7767. https://doi.org/10.1016/j.eswa.2015.06.008
Saen, R. F., Memariani, A., & Lotfi, F. H. (2005). Determining relative efficiency of slightly non-homogeneous decision making units by data envelopment analysis: A case study in IROST. Applied Mathematics and Computation, 165(2), 313–328. https://doi.org/10.1016/j.amc.2004.04.050
Singh, S., & Ranjan, P. (2018). Efficiency analysis of Heterogeneous parallel sub-unit systems for the performance measurement of higher education. Annals of Operations Research, 269, 641-666.
Xu, Y., Zhang, H., Cheng, K., Zhang, Z., & Chen, Y. (2021). Efficiency measurement in multi-period network DEA model with feedback. Expert Systems with Applications, 175, 114815. https://doi.org/10.1016/j.eswa.2021.114815
Yan, Q., Zhao, F., Wang, X., Yang, G., Baležentis, T., & Streimikiene, D. (2019). The network data envelopment analysis models for non-homogenous decision making units based on the sun network structure. Central European Journal of Operations Research, 27(4), 1221–1244. https://doi.org/10.1007/s10100-018-0560-9
Zha, Y., Song, A., Xu, C., & Yang, H. (2013). Dealing with missing data based on data envelopment analysis and halo effect. Applied Mathematical Modelling, 37(9), 6135–6145. https://doi.org/10.1016/j.apm.2012.11.015
Zhu, W., Yu, Y., & Sun, P. (2018). Data envelopment analysis cross-like efficiency model for non-homogeneous decision-making units: The case of United States companies’ low-carbon investment to attain corporate sustainability. European Journal of Operational Research, 269(1), 99–110. https://doi.org/10.1016/j.ejor.2017.08.007