Measuring the performances of Medical Diagnostic Laboratories based on interval efficiencies

Subject Areas : Architecture

Ehsan  Vaezi ¹ (Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.)

Received: 2019-03-09 Accepted : 2019-05-26 Published : 2021-12-01

Keywords:

References:

Aghayi, N., Agrell, P., & Hatami-Marbini, A. (2013). Imprecise data envelopment analysis for the two-stage precess. International Association    for Research and Teaching, 1-20.

Amirteimoori, A., & Kordrostami, S. (2014). Data envelopment analysis with discrete‐valued inputs and outputs. Expert Systems, 31(4), 335-      342.

An, Q., Yang, M., Chu, J., Wu, J., & Zhu, Q. (2017). Efficiency evaluation of an interactive system by data envelopment analysis approach.        Computers & Industrial Engineering, 103, 17-25.

Asandului, L., Roman, M., & Fatulescu, P. (2014). The efficiency of healthcare systems in Europe: A data envelopment analysis approach.          Procedia Economics and Finance, 10, 261-268.

Azizi, H. (2013). A note on data envelopment analysis with missing values: an interval DEA approach. The International Journal of Advanced     Manufacturing Technology 66:1817-1823

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.

Banker, R.D., Charnes, A., & Cooper W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment               analysis. Management Science, 30(9), 1078-1092.

Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical          programming, 88, 411-421.

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, C., & Yan, H. (2011). Network DEA model for supply chain performance evaluation. European Journal of Operational Research, 213      (1): 147–155.

Cheng, C. H., & Lin, Y. (2002). Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation.European      journal of operational research, 142(1), 174-186.

Cooper, W. W., Park, K. S., & Yu, G. (1999). IDEA and AR-IDEA: Models for dealing with imprecise data in DEA. Management science,       45(4), 597-607.

Cooper, W. W., Park, K. S., & Yu, G. I. D. E. A. (2001). IDEA (imprecise data envelopment analysis) with CMDs (column maximum decision   making units). Journal of the Operational Research Society, 52(2), 176-181.

Despotis, D. K., & Smirlis, Y. G. (2002). Data envelopment analysis with imprecise data. European Journal of Operational Research, 140(1),     24-36.

Despotis, D.K., Maragos, E.K. & Smirlis, Y.G. (2006). Data Envelopment Analysis with Missing Values: An    Interval DEA Approach”,           European Journal of  Operational Research. 140 ,24–36.

Du, J., Zhu, J., Cook, W. D., & Huo, J. (2015). DEA models for parallel systems: Game-theoretic approaches.   Asia-Pacific Journal of              Operational Research, 32(02), 1550008.

Entani, T., Maeda, Y., & Tanaka, H. (2002). Dual models of interval DEA and its extension to interval data. European Journal of       Operational Research, 136(1), 32-45.

Fare, R., & Grosskopf, S. (2000). "Network DEA", Socio Economics Planning Science, Vol. 4, No. 1, pp. 35–49.

Fare, R., Grosskopf, S., Lovell, K., & Pasurka, C. (1989). "Multilateral productivity comparisons when some outputs are undesirable: A              nonparametric approach", Review of Economics and Statistics, Vol. 71, No. 1, pp. 90–98.

Farrell, M.J. (1957). The Measurement of Productive Efficiency Journal of the Royal Statistical Society Series A (General) 120:253-290

Farzipoor Saen, R. (2009). A mathematical model for selecting third-party reverse logistics providers. International Journal of Procurement        Management, 2(2), 180-190.

Galán-Martín, Á., Guillén-Gosálbez, G., Stamford, L., & Azapagic, A. (2016). Enhanced data envelopment analysis for sustainability assessment: A novel methodology and application to electricity technologies.                Computers & Chemical Engineering, 90, 188-200.

Gerdessen, J. C., & Pascucci, S. (2013). Data Envelopment Analysis of sustainability indicators of European     agricultural systems at                regional level. Agricultural Systems, 118, 78-90.

Hamid Abu Bakar, A., Lukman Hakim, I., Choy Chong, S., & Lin, B. (2009). Measuring supply chain performance among public hospital           laboratories. International journal of productivity and performance management, 59(1), 75-97.

Hwang, S. N., Chen, C., Chen, Y., Lee, H. S., & Shen, P. D. (2013). Sustainable design performance evaluation with applications in the              automobile industry: Focusing on inefficiency by undesirable factors. Omega, 41(3), 553-558.

Kao, C., (2006). Interval efficiency measures in data envelopment analysis with imprecise data. European Journal of Operational       Research,174, 1087–1099.

Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational              Research, 192 (3): 949-962.

Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance             companies in Taiwan. European journal of operational research, 185(1), 418-429.

Khalili-Damghani, K., Tavana, M., & Haji-Saami, E. (2015). A data envelopment analysis model with interval data and undesirable output        for combined cycle power plant performance assessment. Expert Systems with Applications, 42(2), 760-773.

Korhonen, P. J., & Luptacik, M. (2004). Eco-efficiency analysis of power plants: An extension of data envelopment analysis. European journal   of operational research, 154(2), 437-446.

Leleu, H., Moises, J., & Valdmanis, V. G. (2014). How do payer mix and technical inefficiency affect hospital profit? A weighted DEA            approach. Operations Research for Health Care, 3(4), 231-237.

Kou, M., Chen, K., Wang, S., & Shao, Y. (2016). Measuring efficiencies of multi-period and multi-divisionsystems associated with DEA: An      application to OECD countries. national innovation systems. Expert Systems with Applications. 46, 494-510.

Li, Y., Chen, Y., Liang, L., & Xie, J. (2012). DEA models for extended two-stage network structures. Omega, 40(5), 611-618.

Liang, L., Cook, W. D., & Zhu, J. (2008). DEA models for two‐stage processes: Game approach and efficiency decomposition. Naval Research   Logistics (NRL), 55(7), 643-653.

Liu, J. S., Lu, L. Y., & Lu, W. M. (2016). Research Fronts and Prevailing Applications in Data Envelopment Analysis. In Data Envelopment       Analysis (pp. 543-574). Springer, Boston, MA.

Lu, W.M. & Lo, S.F. (2007). A closer look at the economic– environmental disparities for regional development in China, European Journal       of Operational Research, 183(2), 882–894.

Patra, A., & Ray, P. K. (2018). Measurement of Efficiency and Productivity Growth of Hospital Systems: A Indian Case Study. In Healthcare      Systems Management: Methodologies and Applications (pp. 13-22). Springer, Singapore.

Shabanpour, H., Yousefi, S. & Farzipoor Saen, R. (2017). Future planning for benchmarking and ranking sustainable suppliers using goal            programming and robust double frontiers DEA, Transportation Research Part D: Transport and Environment, 50:129–143.

Tone, K., & Tsutsui, M. (2009). Network DEA: a slacks-based measure approach. European Journal of Operational Research, 197 (1): 243-       252.

Wang, K., Yu, S., & Zhang, W., (2013). China’s regional energy and environmental efficiency. A DEA Window Analysis Based Dynamic          Evaluation, Mathematical and Computer Modelling, 58,1117–1127.

Wang, M., Luo, Y., & Liang, L. (2009). Fuzzy data envelopment analysis based upon fuzzy arithmetic with an application to performance         assessment of manufacturing enterprises. Expert Systems with Applications, 36, 5205–5211.

Wang, Y. M., Greatbanks, R., & Yang, J. B. (2005). Interval efficiency assessment using data envelopment analysis. Fuzzy sets and Systems,       153(3), 347-370.

Wang, W., Lu, W., & Liu, P. (2014). A fuzzy multi-objective two-stage DEA model for evaluating the performance of US bank holding              companies. Expert Systems with Applications, 41, 4290-4297.

Wu, J., Lv, L., Sun, J., & Ji, X. (2015). A comprehensive analysis of China's regional energy saving and emission reduction efficiency: from        production and treatment perspectives. Energy Policy, 84, 166-176.

Wu, J., Zhu, Q., Ji, X., Chu, J., & Liang, L. (2016). Two-stage network processes with shared resources and resources recovered from undesirable outputs. European Journal of Operational Research, 251(1), 182-            197.

Yousefi, S., Soltani, R., Saen, R. F., & Pishvaee, M. S. (2017). A robust fuzzy possibilistic programming for a   new network GP-DEA               model to evaluate sustainable supply chains. Journal of Cleaner Production, 166, 537-549.

Zhou, X., Luo, R., Tu, Y., Lev, B., & Pedrycz, W. (2018). Data envelopment analysis for bi-level systems with multiple followers. Omega,        77, 180-188.