Document Type : Original Article

Authors

1 Department of Mathematics, Rudsar-Amlash Branch, Islamic Azad University, Rudsar, Iran.

2 Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

3 Department of Mathematics, Tehran Science and Research, Islamic Azad University, Tehran, Iran.

Abstract

Purpose: The providing a proposed model pair for ranking interval data and their application to evaluate and improve the performance of a service system using results of simulation.
Methodology: Mathematical techniques (data envelopment analysis) and computer simulation.
Findings: By presenting proposed models pair, we were able to improve the performance of a service system by simulating different scenarios for that system. The results show that the introduced scenario could increase the efficiency of system by 22%.




Originality/Value: Introducing new applied methods using mathematical models (Data Envelopment Analysis) and simulations to improve the performance of systems

Keywords

Main Subjects

Al-Refaie, A., Fouad, R. H., Li, M. H., & Shurrab, M. (2014). Applying simulation and DEA to improve performance of emergency department in a Jordanian hospital. Simulation Modelling practice and theory41, 59-72.
Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management science39(10), 1261-1264.
Azadeh, A., Tohidi, H., Zarrin, M., Pashapour, S., & Moghaddam, M. (2016). An integrated algorithm for performance optimization of neurosurgical ICUs. Expert systems with applications43, 142-153.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science30(9), 1078-1092.
Cabrera, E., Taboada, M., Iglesias, M. L., Epelde, F., & Luque, E. (2011). Optimization of healthcare emergency departments by agent-based simulation. Procedia computer science4, 1880-1889.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research2(6), 429-444.
Cooper, W. W., Park, K. S., & Yu, G. (1999). IDEA and AR-IDEA: models for dealing with imprecise data in DEA. Management science45(4), 597-607.
Cooper, W. W., Park, K. S., & Yu, G. (2001). An illustrative application of IDEA (imprecise data envelopment analysis) to a Korean mobile telecommunication company. Operations research49(6), 807-820.
Despotis, D. K., & Smirlis, Y. G. (2002). Data envelopment analysis with imprecise data. European Journal of operational research140(1), 24-36.
Doyle, J. R., & Green, R. H. (1994). Efficiency and cross-efficiency in DEA: derivations, meanings and uses. Journal of the operational research society45(5), 567-578.
Doyle, J. R., & Green, R. H. (1995). Cross-evaluation in DEA: improving discrimination among dmus. INFOR: information systems and operational research33(3), 205-222.
Ebrahimi, B., Rahmani, M., & Ghodsypour, S. H. (2017). A new simulation-based genetic algorithm to efficiency measure in IDEA with weight restrictions. Measurement108, 26-33.
Entani, T., Maeda, Y., & Tanaka, H. (2002). Dual models of interval DEA and its extension to interval data. European journal of operational research136(1), 32-45.
Ghasemi, S., Aghsami, A., & Rabbani, M. (2021). Data envelopment analysis for estimate efficiency and ranking operating rooms: a case study. International journal of research in industrial engineering10(1), 67-86.
Ghasempoor Anaraki, M., Vladislav, D. S., Karbasian, M., Osintsev, N., & Nozick, V. (2021). Evaluation and selection of supplier in supply chain with fuzzy analytical network process approach. Journal of fuzzy extension and applications2(1), 69-88.
Gholami Golsefid, F., Daneshian, B., & Rostamy-Malkhalifeh, M. (2020a). Ranking of units by corrected cross-efficiency method using optimal weights in the smallest interval. Nexo revista científica33(02), 446-453.
Gholami Golsefid, F., Daneshian, B., & Rostamy-Malkhalifeh, M. (2020b). Improving the performance of a medical imaging center through simulation and fuzzy DEA. International journal of modeling, simulation, and scientific computing11(06), 2050059. https://doi.org/10.1142/S1793962320500592
He, F., Xu, X., Chen, R., & Zhu, L. (2016). Interval efficiency improvement in DEA by using ideal points. Measurement87, 138-145.
Hosseinzaheh Lotfi, F., Jahanshahloo, G. R., Shahverdi, R., & Rostamy-Malkhalifeh, M. (2007a). Cost efficiency and cost Malmquist productivity index with interval data. International mathematical forum, 2(9), 441-453.
Hosseinzaheh Lotfi, F., Navabakhs, M., Tehranian, A., Rostamy-Malkhalifeh, M., & Shahverdi, R. (2007b). Ranking bank branches with interval data—the application of DEA. International mathematical forum, 2(9), 429-440.
Jafari, H. (2020). Evaluation and estimation of outputs and inputs in inverse data envelopment analysis with interval data. Innovation management and operational strategies, 1(3), 297-309. (In Persian). http://www.journal-imos.ir/article_125406.html?lang=en  
Jahanshahloo, G. R., & Shahmirzadi, P. F. (2013). New methods for ranking decision making units based on the dispersion of weights and Norm 1 in Data Envelopment Analysis. Computers & industrial engineering65(2), 187-193.
Jahanshahloo, G. R., Lotfi, F. H., Jafari, Y., & Maddahi, R. (2011a). Selecting symmetric weights as a secondary goal in DEA cross-efficiency evaluation. Applied mathematical modelling35(1), 544-549.
Jahanshahloo, G. R., Lotfi, F. H., Rezaie, V., & Khanmohammadi, M. (2011b). Ranking DMUs by ideal points with interval data in DEA. Applied mathematical modelling35(1), 218-229.
Jahanshahloo, G. R., Lotfi, F. H., Malkhalifeh, M. R., & Namin, M. A. (2009). A generalized model for data envelopment analysis with interval data. Applied mathematical modelling33(7), 3237-3244.
Khalili, N., Shahnazari Shahrezaei, P., & Abri, A. G. (2020). A multi-objective optimization approach for a nurse scheduling problem considering the fatigue factor (case study: Labbafinejad Hospital). Journal of applied research on industrial engineering7(4), 396-423.
Laskowski, M., & Mukhi, S. (2008, September). Agent-based simulation of emergency departments with patient diversion. International conference on electronic healthcare (pp. 25-37). Springer, Berlin, Heidelberg.
Liang, L., Wu, J., Cook, W. D., & Zhu, J. (2008a). Alternative secondary goals in DEA cross-efficiency evaluation. International journal of production economics113(2), 1025-1030.
Liang, L., Wu, J., Cook, W. D., & Zhu, J. (2008b). The DEA game cross-efficiency model and its Nash equilibrium. Operations research56(5), 1278-1288.
Meng, L. Y. & Spedding, T. (2008). Modeling patient arrivals when simulating an accident and emergency unit. 2008 winter simulation conference (pp. 1509-1515). IEEE. DOI: 10.1109/WSC.2008.4736231
Pourhabib, A., Kordrostami, S., Amirteimoori, A., & Matin, R. K. (2018). Performance measurement in data envelopment analysis without slacks: an application to electricity distribution companies. RAIRO-operations research52(4-5), 1069-1085.
Ruohonen, T., Neittaanmaki, P., & Teittinen, J. (2006, December). Simulation model for improving the operation of the emergency department of special health care. Proceedings of the 2006 winter simulation conference (pp. 453-458). IEEE. DOI: 10.1109/WSC.2006.323115
Sarfaraj, N., Lingkon, M. L., & Zahan, N. (2021). Applying flexible job shop scheduling in patients management to optimize processing time in hospitals. International journal of research in industrial engineering10(1), 46-55.
Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: critique and extensions. New directions for program evaluation1986(32), 73-105.
Song, M., Zhu, Q., Peng, J., & Gonzalez, E. D. S. (2017). Improving the evaluation of cross efficiencies: a method based on Shannon entropy weight. Computers & industrial engineering112, 99-106.
Wang, Y. M., & Chin, K. S. (2010). Some alternative models for DEA cross-efficiency evaluation. International journal of production economics128(1), 332-338.
Wang, Y. M., Greatbanks, R., & Yang, J. B. (2005). Interval efficiency assessment using data envelopment analysis. Fuzzy sets and systems153(3), 347-370.
Waring, J. J., & Bishop, S. (2010). Lean healthcare: rhetoric, ritual and resistance. Social science & medicine71(7), 1332-1340.
Weng, S. J., Tsai, B. S., Wang, L. M., Chang, C. Y., & Gotcher, D. (2011, December). Using simulation and data envelopment analysis in optimal healthcare efficiency allocations. Proceedings of the 2011 winter simulation conference (WSC) (pp. 1295-1305). IEEE.