پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,532 |
| تعداد مقالات | 70,501 |
| تعداد مشاهده مقاله | 124,111,767 |
| تعداد دریافت فایل اصل مقاله | 97,215,389 |
Determining the Exact Stability Region and Radius Through Efficient Hyperplanes | ||
| Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
| دوره 15، شماره 2، تیر 2022، صفحه 287-303 اصل مقاله (754.54 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijms.2021.317297.674405 | ||
| نویسندگان | ||
| Nasim Arabjazi؛ Mohsen Rostamy-Malkhalifeh* ؛ Farhad Hosseinzadeh Lotfi؛ Mohammad Hasan Behzadi | ||
| Department of Mathematics, Faculty of Science, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| چکیده | ||
| The main goal of this study was to address the sensitivity analysis fora specific efficient decision-making unit (DMU), which is under evaluation, by the variable returns to scale (VRS) technology to extend the efficiency stability region. Variations in inputs or outputs of any DMU can change the efficiency classification of that DMU as well as other DMUs, i.e., an efficient DMU can become inefficient and vice versa. This study considered the largest performance stability region for an extreme efficient DMU whose data could be changed in all directions of input/output space, including both directions of improving the situation and worsening the situation such that under these changes, the efficiency classification of all extreme DMUs would be preserved. We found the largest symmetric cell to the center of the extreme efficient DMU under evaluation, leading to an efficiency stability radius. In addition, data changes were only applied for the extreme efficient DMU, and the data for the other DMUs were assumed fixed. This stability region was determined by the defining hyperplanes of production possibility set (PPS) of VRS technology and the corresponding half-spaces. The suggested method is illustrated using real-world data. | ||
| کلیدواژه‌ها | ||
| data envelopment analysis؛ sensitivity analysis؛ stability region؛ stability radius؛ defining hyperplane | ||
| عنوان مقاله [English] | ||
| تعیین دقیق ناحیه پایداری و شعاع پایداری توسط ابرصفحه های کارا | ||
| نویسندگان [English] | ||
| نسیم عرب جزی؛ محسن رستمی مال خلیفه؛ فرهاد حسین زاده لطفی؛ محمد حسن بهزادی | ||
| گروه ریاضی، دانشکده علوم پایه، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران، ایران | ||
| چکیده [English] | ||
| هدف اصلی این تحقیق، تحلیل حساسیتیک واحد تصمیم گیرنده(DMU) کارا با استفاده از فناوری بازده به مقیاس متغیر(VRS) برای توسعه ناحیه پایداری کارایی می باشد. تغییرات در ورودییا خروجی هر DMU می تواند طبقه بندی کارایی آن DMU و دیگرDMU ها را تغییر دهد، یعنییکDMU کارا می تواند ناکارا شود و بالعکس. این مطالعه بزرگترین ناحیه پایداری کارایی را براییکDMU کارای رأسی در نظر می گیرد که داده‌های آن در تمام جهات فضای ورودی/ خروجی تغییر می‌کند: شاملِهر دو جهت بهبود وضعیت و بدتر شدن وضعیت واحد رأسی، به گونه ای که تحت این تغییرات طبقه بندی کارایی همه واحدهای‌های رأسی ثابت خواهد ماند. علاوه بر این، بزرگترین سلول متقارن به مرکز DMU کارای رأسی را ارزیابی خواهیم کرد که منجر به شعاع پایداری کارایی می شود. همچنین، تغییرات داده ها فقط برایDMU رأسی اعمال می شود و داده های دیگر واحدها ثابت است. این ناحیه پایداری با مفهوم ابرصفحه‌های تعریف کننده مجموعه امکان تولید(PPS)با فناوریVRS و نیم فضاهای مربوطه تعیین می شود. روش پیشنهادی با استفاده از داده های دنیای واقعی در صنعت بانکداری نشان داده شده است. | ||
| کلیدواژه‌ها [English] | ||
| تحلیل پوششی داده ها, تحلیل حساسیت, ناحیه پایداری, شعاع پایداری, ابرصفحه های تعریف کننده | ||
| مراجع | ||
|
Abri, G. A., Shoja, N., & Fallah, J. M. (2009). Sensitivity and stability radius in data envelopment analysis. International Journal of Industrial Mathematics, 1(3), 227-243.
Agarwal, S., Yadav, S. P., & Singh, S. (2014). Sensitivity analysis in data envelopment analysis. International Journal of Operational Research, 19(2), 174-185.
Ahn, T., & Seiford, L. M. (1993). Sensitivity of DEA to models and variable sets in a hypothesis test setting: The efficiency of university operations. Creative and Innovative Approaches to the Science of Management, 6, 191-208.
Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261-1264.
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.
Banker, R. D., Kotarac, K., & Neralić, L. (2015). Sensitivity and stability in stochastic data envelopment analysis. Journal of the Operational Research Society, 66(1), 134-147.
Boljunčić, V. (2006). Sensitivity analysis of an efficient DMU in DEA model with variable returns to scale (VRS). Journal of Productivity Analysis, 25(1-2), 173-192.
Charnes, A., Cooper, W., & Thrall, R. M. (1991). A structure for classifying and characterizing efficiency and inefficiency in data envelopment analysis. Journal of Productivity Analysis, 2(3), 197-237.
Charnes, A., Cooper, W. W., Lewin, A. Y., Morey, R. C., & Rousseau, J. (1985). Sensitivity and stability analysis in DEA. Annals of Operations Research, 2(1), 139-156.
Charnes, A., Cooper, W. W., Lewin, A. Y., & Seiford, L. M. (2013). Data envelopment analysis: Theory, methodology, and applications. Springer Science & Business Media.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
Charnes, A., Haag, S., Jaska, P., & Semple, J. (1992). Sensitivity of efficiency classifications in the additive model of data envelopment analysis. International Journal of Systems Science, 23(5), 789-798.
Charnes, A., & Neralić, L. (1990). Sensitivity analysis of the additive model in data envelopment analysis. European Journal of Operational Research, 48(3), 332-341.
Charnes, A., Rousseau, J. J., & Semple, J. H. (1996). Sensitivity and stability of efficiency classifications in data envelopment analysis. Journal of Productivity Analysis, 7(1), 5-18.
Cooper, W. W., Li, S., Seiford, L. M., Tone, K., Thrall, R. M., & Zhu, J. (2001). Sensitivity and stability analysis in DEA: Some recent developments. Journal of Productivity Analysis, 15(3), 217-246.
Daneshvar, S., Izbirak, G., & Javadi, A. (2014). Sensitivity analysis on modified variable returns to scale model in Data Envelopment Analysis using facet analysis. Computers & Industrial Engineering, 76, 32-39.
Dar, Q. F., Pad, T. R., Tali, A. M., Hamid, Y., & Danish, F. (2017). data envelopment analysis with sensitive analysis and super-efficiency in Indian banking sector. International Journal of Data Envelopment Analysis, 5(2), 1193-1206.
Ghazi, N. E., Lotfi, F. H., Rostamy-Malkhalifeh, M., Jahanshahloo, G., & Namin, M. A. (2018). Finding an improved region of efficiency via DEA-efficient hyperplanes. Scientia Iranica, 25(5), 2852-2866.
Hatami-Marbini, A., & Toloo, M. (2019). Data envelopment analysis models with ratio data: A revisit. Computers & Industrial Engineering, 133, 331-338.
He, F., Xu, X., Chen, R., & Zhang, N. (2016). Sensitivity and stability analysis in DEA with bounded uncertainty. Optimization Letters, 10(4), 737-752.
Hladík, M. (2019). Universal efficiency scores in data envelopment analysis based on a robust approach. Expert Systems with Applications, 122, 242-252.
Huang, Z., Li, S., & Rousseau, J. (1997). Determining rates of change in data envelopment analysis. Journal of the Operational Research Society, 48(6), 591-593.
Jahanshahloo, G., Lotfi, F. H., & Akbarian, D. (2010). Finding weak defining hyperplanes of PPS of the BCC model. Applied Mathematical Modelling, 34(11), 3321-3332.
Jahanshahloo, G. R., Lofti, F. H., & Moradi, M. (2004). Sensitivity and stability analysis in DEA with interval data. Applied Mathematics and Computation, 156(2), 463-477.
Jahanshahloo, G. R., Lotfi, F. H., Rezai, H. Z., & Balf, F. R. (2007). Finding strong defining hyperplanes of production possibility set. European Journal of Operational Research, 177(1), 42-54.
Jahanshahloo, G. R., Lotfi, F. H., Shoja, N., Abri, A. G., Jelodar, M. F., & Firouzabadi, K. J. (2011). Sensitivity analysis of inefficient units in data envelopment analysis. Mathematical and Computer Modelling, 53(5-6), 587-596.
Jahanshahloo, G. R., Lotfi, F. H., Shoja, N., Sanei, M., & Tohidi, G. (2005a). Sensitivity and stability analysis in DEA. Applied Mathematics and Computation, 169(2), 897-904.
Jahanshahloo, G. R., Lotfi, F. H., Shoja, N., Tohidi, G., & Razavyan, S. (2005b). A one-model approach to classification and sensitivity analysis in DEA. Applied Mathematics and Computation, 169(2), 887-896.
Khalili-Damghani, K., & Taghavifard, B. (2013). Sensitivity and stability analysis in two-stage DEA models with fuzzy data. International Journal of Operational Research, 17(1), 1-37.
Khodabakhshi, M., Rashidi, S., Asgharian, M., & Neralić, L. (2015). Sensitivity analysis of input relaxation super efficiency measure in data envelopment analysis. Data Envelopment Analysis Journal, 1(2), 113-134.
Khoveyni, M., &Eslami, R. (2021). DEA efficiency region for variations of inputs and outputs. International Journal of Information Technology & Decision Making, 20(02), 707-732.
Liu, J. S., Lu, L. Y., Lu, W. -M., & Lin, B. J. (2013). A survey of DEA applications. Omega, 41(5), 893-902.
Lotfi, F. H., Jahanshahloo, G. R., Mozaffari, M., & Gerami, J. (2011). Finding DEA-efficient hyperplanes using MOLP efficient faces. Journal of Computational and Applied Mathematics, 235(5), 1227-1231.
Mozaffari, M., Gholami, K., & Dehghand, F. (2009). Sensitivity and stability analysis in DEA on interval data by using MOLP methods. Applied Mathematical Sciences, 3(18), 891-908.
Neralić, L. (2004). Preservation of efficiency and inefficiency classification in data envelopment analysis. Mathematical Communications, 9(1), 51-62.
Nurcan, E., & Deniz Köksal, C. (2021). Determination of financial failure indicators by gray relational analysis and application of data envelopment analysis and logistic regression analysis in BIST 100 Index. Iranian Journal of Management Studies, 14(1), 163-187.
Peykani, P., Mohammadi, E., Emrouznejad, A., Pishvaee, M. S., & Rostamy-Malkhalifeh, M. (2019). Fuzzy data envelopment analysis: An adjustable approach. Expert Systems with Applications, 136, 439-452.
Sarfi, E., Noroozi, E., & Lotfi, F. H. (2015). Sensitivity analysis and finding the stability region with adding DMUs in DEA. International Journal of Data Envelopment Analysis, 3(4), 841-848.
Seiford, L. M., & Zhu, J. (1998a). Stability regions for maintaining efficiency in data envelopment analysis. European Journal of Operational Research, 108(1), 127-139.
Seiford, L. M., & Zhu, J. (1998b). Sensitivity analysis of DEA models for simultaneous changes in all the data. Journal of the Operational Research Society, 49(10), 1060-1071.
Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation, 1986(32), 73-105.
Singh, S. (2010). Multiparametric sensitivity analysis of the additive model in data envelopment analysis. International Transactions in Operational Research, 17(3), 365-380.
Thompson, R., Dharmapala, P., & Thrall, R. M. (1994). Sensitivity analysis of efficiency measures with applications to Kansas farming and Illinois coal mining. In A. Charnes, W. W. Cooper, A. Y. Lewin, & L. M. Seiford, (Eds.), Data envelopment analysis: Theory, methodology, and applications(pp. 393-422). Springer.
Thompson, R. G., Dharmapala, P., Diaz, J., González-Lima, M. D., & Thrall, R. M. (1996). DEA multiplier analytic center sensitivity with an illustrative application to independent oil companies. Annals of Operations Research, 66(2), 163-177.
Tohidi, G., Banihashemi, S., & Sanei, M. (2014). Sensitivity analysis of efficient and inefficient units in integer-valued data envelopment analysis. International Journal of Mathematical Modelling & Computations, 4(1), 45-53.
Wen, M., Qin, Z., & Kang, R. (2011). Sensitivity and stability analysis in fuzzy data envelopment analysis. Fuzzy Optimization and Decision Making, 10(1), 1-10.
Wilson, P. W. (1995). Detecting influential observations in data envelopment analysis. Journal of Productivity Analysis, 6(1), 27-45.
Zamani, P., & Borzouei, M. (2016). Finding stability regions for preserving efficiency classification of variable returns to scale technology in data envelopment analysis. Journal of Industrial Engineering International, 12(4), 499-507.
Zhou, P., Poh, K. L., & Ang, B. W. (2016). Data envelopment analysis for measuring environmental performance. In S. N. Hwang, H. S. Lee, & J. Zhu, (Eds.), Handbook of operations analytics using data envelopment analysis(pp. 31-49). Springer, Boston, MA. .
Zhu, J. (1996). Robustness of the efficient DMUs in data envelopment analysis. European Journal of Operational Research, 90(3), 451-460.
Zhu, J. (2001). Super-efficiency and DEA sensitivity analysis. European Journal of Operational Research, 129(2), 443-455. | ||
|
آمار تعداد مشاهده مقاله: 746 تعداد دریافت فایل اصل مقاله: 702 |
||