توسعه یک مدل تحلیل پوششی دادههای شبکه‌ای مضربی جهت بررسی ساختار درونی واحدهای تصمیم‌گیرنده
الموضوعات :
1 - گروه مدیریت، دانشکده علوم اداری و اقتصاد، دانشگاه ولی عصر(عج) رفسنجان
الکلمات المفتاحية: مدلی های مضربی, تحلیل پوششی دادههای شبکه ای, ساختارهای شبکه ای, واحدهای تصمیم گیرنده,
ملخص المقالة :
تحلیل پوششی داده‌ها به دلیل عدم نیاز به تابع تولید، از زمان ارائه موردتوجه محققین جهت ارزیابی عملکرد بوده است. اما مدل‌های اولیه تحلیل پوششی داده‌ها جهت بررسی ساختار درونی واحدها ناتوان هستند و دیدگاه جعبه سیاه دارند. از جمله متداول‌ترین ساختارهای شبکه‌ای، دومرحله‌ای متوالی است. مدل‌های موجود جهت ارزیابی این ساختار عمدتا دارای رویکرد تجزیه می‌باشند، به عبارت دیگر اولویت آن‌ها کارایی کل است و از تجزیه کارایی کل، کارایی مراحل بدست می‌آید. در این مقاله تلاش شده است تا یک مدل چندهدفه مضربی که همزمان کارایی کل و کارایی مراحل را مدنظر قرار می‌دهد توسعه داده شود. همچنین برای حالت جواب چندگانه، مدل‌هایی جهت محاسبه کارایی‌ها ارائه و اثبات شد که در تمامی مدل‌ها نمرات کارایی بین صفر تا یک می‌شود و تنها در صورتی یک واحد کارای شبکه‌ای می‌شود که در هر دومرحله کارا باشد. مدل‌های توسعه‌داده شده به ساختار دومرحله‌ای با ورودی و خروجی مازاد نیز تعمیم داده شد. از مدل ارائه شده در یک مثال کاربردی استفاده شد و نتایج نشان داد که مدل موجود نسبت به مدل‌های سنتی ارزیابی واقع‌بینانه‌تری انجام می‌دهد.
المصادر:
Amirteimoori, A., Despotis, D. K., Kordrostami, S. & Azizi, H. (2016). Additive models for network data envelopment analysis in the presence of shared resources. Transportation Research Part D, http://dx.doi.org/10.1016/j.trd.2015.12.016.
An, Q., Chen, H., Xiong, B., Wu, J., & Liang, L., (2017). Target intermediate products setting in a two-stage system with fairness concern. Omega, 73, 49-59.
Asilfarid, H., Khalaj, M. & Zaeri, M. (2018). Analysis of Human Resources Performance Using the Two-Stage Data Envelopment Analysis Approach-Case Study: Azar Noosh Shokofeh Co., Journal of Industrial Management, Faculty of Humanities, Islamic Azad University, Sanandaj Branch, 13, 43.
Azar, A., Moghbel, A., Zarei, M. & Khadivar, A. (2014). Bank Branch Productivity Measurement Using Network Data Envelopment Analysis(One of the banks of Guilan province). Journal of Monetary and Banking Research, 7, 20.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978), “Measuring the efficiency of decision-making units”. European Journal of Operational Research, 2, 429-444.
Chen, Y., Cook, W. D., & Zhu, J. (2010). Deriving the DEA frontier for two-stage processes. European Journal of Operational Research, 202, 138-142.
Chen, Y., Cook, W. D., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Reaearch, 196, 1170-1176.
Cook, W. D., Zhu, J., Bi, G. B., & Yang, F. (2010). Network DEA: Additive efficiency decomposition. European Journal of Operational Research, 207, 1122-1129.
Despotis, K., D., Koronakos, G., Sotiros, D. (2016). Composition versus decomposition in two-stage network DEA: a reverse approach. Journal of Productivity Analysis, 45, 71-87.
Emrouznejad, A. & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016. Socio-Economic Planning Sciences, 61, 4-8.
Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: A frontier approach. Economics Letters, 50, 65–70.
Fare, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34, 35-49.
Färe, R., & Whittaker, G. (1995). An intermediate input model of dairy production using complex survey data. Journal of Agricultural Economics, 46, 201–213.
Fukuyama, H., & Matousek, R. (2017). Modelling Bank Performance: A Network DEA Approach, European Journal of Operational Research, 259, 721-732.
Fukuyama, H., & Weber, W.L. (2016). Japanese bank productivity, 2007-2012: A dynamic network approach. Mimeo.
Guan, J. C., & Chen, K. H. (2012). Modeling the relative efficiency of national innovation systems. Research Policy, 41, 102–115.
Huang, T.H., Lin, C. I., Chen, K, C. (2017). Evaluating Efficiencies of Chinese Commercial Banks in the Context of Stochastic Multistage Technologies. Pacific-Basin Finance Journal, 906.
Kao, C. (2009a). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192, 949-962.
Kao, C. (2009b). Efficiency measurement for parallel production systems. European Journal of Operational Research, 196, 1107-1112.
Kao, C. (2014a). Efficiency decomposition for general multi-stage systems in data envelopment analysis. European Journal of Operational Research, 232, 117-124.
Kao, C. (2014b). Network data envelopment analysis: A review. European Journal of Operational Research, 239, 1-16.
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, 418-429.
Kao, C., & Hwang, S. N. (2011). Decomposition of technical and scale efficiencies in two-stage production systems. European Journal of Operational Research, 211, 515–519.
Khalaj, M., Asilfarid, H., R. (2017). A Two-stage DEA Model to Evaluate and Improve the Systems Function (Casestudy: Sorena System Sharq Company Projects). Journal of Industrial Management, Faculty of Humanities, Islamic Azad University, Sanandaj Branch, 12, 41.
Lewis, H, F., & Sexton, T. R. (2003). Network DEA: Efficiency analysis of organizations with complex internal structure. Computer and Operations Research, 31, 1365-1410.
Liu, J. S., & Lu, W. M. (2012). Network-based method for ranking of efficient units in two-stage DEA models. Journal of the Operational Research Society, 63, 1153-1164
Momeni, M., Safari, H., Rostami, M., Mostafaee, M. A. (2017). Designing a Non-oriented NDEA for Performance Evaluation: An Applied Study of Banks, Management Studies in Development & Evolution, 26, 86.
Noora, A., Hosseinzadeh, F. & Khodadadi, M. (2018). Determine the most productive scale of a production unit using a two-stage process based on the demand level.Journal of Decisions and Operations Research, 2, 2.
Premachandra, I. M., Zhu, J., Watson, J., & Galagedera, D. U. A. (2012). Bestperforming US mutual fund families from 1993 to 2008: Evidence from a novel two-stage DEA model for efficiency decomposition. Journal of Banking and Finance, 36, 3302–3317.
Razavi, S., M., Shahriari, S., Ahmadpor, D., M. (2016). Evaluation of Innovative Performance of Knowledge based Company by Network Data Envelopment Analysis-Game Theory Approach. Industrial Management Journal, 7, 4.
Rezaei, A., Mahmudinejad, E., Bakhshi, P. (2018). The Evaluation of Relative Efficiency of all provinces in Terms of human Development Using NDEA Method, Journal of Economic Growth and Development Research, 8, 29.
Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 US commercial banks. Management Science, 45, 1270-1288.
Shafiei, M., Yakideh, K., Oveisi, O. (2017). A combined approach of data envelopment analysis with a variety of outputs and window analysis in evaluating the efficiency of the electricity industry, Journal of Industrial Management Perspective, 6, 23.
Soleymani-Damaneh, R., Momeni, M., Mostafaee, A. & Rostami, M. (2017). Developing of a Dynamic Network Data Envelopment Analysis Model for Evaluating Banking Sector, Journal of Industrial Management Perspective, 25.
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197, 243-252.
Wacker, J. G. (1998). A definition of theory: research guidelines for different theory-building research methods in operations management. Journal of Operations Management, 16(4), 361-385.
Wang, K, Huang, W, Wu, J, Liu, YN. (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, 44, 5-20.
Zarei M., M. (2016). Multilevel Measuring of Efficiency in Banking Industry (Network Slacks-Based Measure Approach). Industrial Management Journal, 8, 3.
Zhang, L., & Chen, Y. (2018). Equivalent solutions to additive two-stage network data envelopment analysis. European Journal of Operation Research, 264(3), 1189-91.
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Amirteimoori, A., Despotis, D. K., Kordrostami, S. & Azizi, H. (2016). Additive models for network data envelopment analysis in the presence of shared resources. Transportation Research Part D, http://dx.doi.org/10.1016/j.trd.2015.12.016.
An, Q., Chen, H., Xiong, B., Wu, J., & Liang, L., (2017). Target intermediate products setting in a two-stage system with fairness concern. Omega, 73, 49-59.
Asilfarid, H., Khalaj, M. & Zaeri, M. (2018). Analysis of Human Resources Performance Using the Two-Stage Data Envelopment Analysis Approach-Case Study: Azar Noosh Shokofeh Co., Journal of Industrial Management, Faculty of Humanities, Islamic Azad University, Sanandaj Branch, 13, 43.
Azar, A., Moghbel, A., Zarei, M. & Khadivar, A. (2014). Bank Branch Productivity Measurement Using Network Data Envelopment Analysis(One of the banks of Guilan province). Journal of Monetary and Banking Research, 7, 20.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978), “Measuring the efficiency of decision-making units”. European Journal of Operational Research, 2, 429-444.
Chen, Y., Cook, W. D., & Zhu, J. (2010). Deriving the DEA frontier for two-stage processes. European Journal of Operational Research, 202, 138-142.
Chen, Y., Cook, W. D., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Reaearch, 196, 1170-1176.
Cook, W. D., Zhu, J., Bi, G. B., & Yang, F. (2010). Network DEA: Additive efficiency decomposition. European Journal of Operational Research, 207, 1122-1129.
Despotis, K., D., Koronakos, G., Sotiros, D. (2016). Composition versus decomposition in two-stage network DEA: a reverse approach. Journal of Productivity Analysis, 45, 71-87.
Emrouznejad, A. & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016. Socio-Economic Planning Sciences, 61, 4-8.
Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: A frontier approach. Economics Letters, 50, 65–70.
Fare, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34, 35-49.
Färe, R., & Whittaker, G. (1995). An intermediate input model of dairy production using complex survey data. Journal of Agricultural Economics, 46, 201–213.
Fukuyama, H., & Matousek, R. (2017). Modelling Bank Performance: A Network DEA Approach, European Journal of Operational Research, 259, 721-732.
Fukuyama, H., & Weber, W.L. (2016). Japanese bank productivity, 2007-2012: A dynamic network approach. Mimeo.
Guan, J. C., & Chen, K. H. (2012). Modeling the relative efficiency of national innovation systems. Research Policy, 41, 102–115.
Huang, T.H., Lin, C. I., Chen, K, C. (2017). Evaluating Efficiencies of Chinese Commercial Banks in the Context of Stochastic Multistage Technologies. Pacific-Basin Finance Journal, 906.
Kao, C. (2009a). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192, 949-962.
Kao, C. (2009b). Efficiency measurement for parallel production systems. European Journal of Operational Research, 196, 1107-1112.
Kao, C. (2014a). Efficiency decomposition for general multi-stage systems in data envelopment analysis. European Journal of Operational Research, 232, 117-124.
Kao, C. (2014b). Network data envelopment analysis: A review. European Journal of Operational Research, 239, 1-16.
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, 418-429.
Kao, C., & Hwang, S. N. (2011). Decomposition of technical and scale efficiencies in two-stage production systems. European Journal of Operational Research, 211, 515–519.
Khalaj, M., Asilfarid, H., R. (2017). A Two-stage DEA Model to Evaluate and Improve the Systems Function (Casestudy: Sorena System Sharq Company Projects). Journal of Industrial Management, Faculty of Humanities, Islamic Azad University, Sanandaj Branch, 12, 41.
Lewis, H, F., & Sexton, T. R. (2003). Network DEA: Efficiency analysis of organizations with complex internal structure. Computer and Operations Research, 31, 1365-1410.
Liu, J. S., & Lu, W. M. (2012). Network-based method for ranking of efficient units in two-stage DEA models. Journal of the Operational Research Society, 63, 1153-1164
Momeni, M., Safari, H., Rostami, M., Mostafaee, M. A. (2017). Designing a Non-oriented NDEA for Performance Evaluation: An Applied Study of Banks, Management Studies in Development & Evolution, 26, 86.
Noora, A., Hosseinzadeh, F. & Khodadadi, M. (2018). Determine the most productive scale of a production unit using a two-stage process based on the demand level.Journal of Decisions and Operations Research, 2, 2.
Premachandra, I. M., Zhu, J., Watson, J., & Galagedera, D. U. A. (2012). Bestperforming US mutual fund families from 1993 to 2008: Evidence from a novel two-stage DEA model for efficiency decomposition. Journal of Banking and Finance, 36, 3302–3317.
Razavi, S., M., Shahriari, S., Ahmadpor, D., M. (2016). Evaluation of Innovative Performance of Knowledge based Company by Network Data Envelopment Analysis-Game Theory Approach. Industrial Management Journal, 7, 4.
Rezaei, A., Mahmudinejad, E., Bakhshi, P. (2018). The Evaluation of Relative Efficiency of all provinces in Terms of human Development Using NDEA Method, Journal of Economic Growth and Development Research, 8, 29.
Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 US commercial banks. Management Science, 45, 1270-1288.
Shafiei, M., Yakideh, K., Oveisi, O. (2017). A combined approach of data envelopment analysis with a variety of outputs and window analysis in evaluating the efficiency of the electricity industry, Journal of Industrial Management Perspective, 6, 23.
Soleymani-Damaneh, R., Momeni, M., Mostafaee, A. & Rostami, M. (2017). Developing of a Dynamic Network Data Envelopment Analysis Model for Evaluating Banking Sector, Journal of Industrial Management Perspective, 25.
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197, 243-252.
Wacker, J. G. (1998). A definition of theory: research guidelines for different theory-building research methods in operations management. Journal of Operations Management, 16(4), 361-385.
Wang, K, Huang, W, Wu, J, Liu, YN. (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, 44, 5-20.
Zarei M., M. (2016). Multilevel Measuring of Efficiency in Banking Industry (Network Slacks-Based Measure Approach). Industrial Management Journal, 8, 3.
Zhang, L., & Chen, Y. (2018). Equivalent solutions to additive two-stage network data envelopment analysis. European Journal of Operation Research, 264(3), 1189-91.
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