Measuring the performances of Medical Diagnostic Laboratories based on interval efficiencies

Journal of Optimization in Industrial Engineering

مقاله 11، دوره 14، شماره 2 - شماره پیاپی 31، اسفند 2021، صفحه 137-154   

نوع مقاله: Review Paper

شناسه دیجیتال (DOI): 10.22094/joie.2019.1864217.1635

نویسنده

Ehsan Vaezi^*

Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

چکیده

The classic data envelopment analysis (DEA) models have overlooked the intermediate products, internal interactions and the absence of data certainty; and deal with analyzing the network within the “Black Box” mode. This results in the loss of important information and at times a considerable modification occurs in efficiency results. In this paper, a Three-stage network model is considered with additional inputs and undesirable outputs and obtains the efficiency of the network, as interval efficiency in presence of the imprecise datum. The proposed model simulates the internal structure of a diagnostic lab (the pre-test, the test and the post-test). In this study, the criteria for evaluation are obtained by using the Fuzzy Delphi method. Due to the social, economic and environmental problems of health care organizations, the importance of sustainability criteria is evident in the case study indicators. We utilized the multiplicative DEA approach to measure the efficiency of a general system and a heuristic technique was used to convert non-linear models into linear models. Ultimately, this paper concentrates on the interval efficiency to rank the units.

کلیدواژه‌ها

Network DEA؛ Medical Diagnostic Laboratories؛ Sustainability, Imprecise data؛ Additional inputs؛ Undesirable outputs

مراجع

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.