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عنوان URL للمقالة


رقم المقالة : 536808 زيارة : 107 الصفحة: 73 - 86

نوع المخطوط: ابحاث

مدل‌های تحلیل پوششی داده‌های بازه‌ای مبتنی بر TOPSIS

الموضوعات :

Hossein Azizi 1 , Alireza Amirteimoori 2 , Sohrab Kordrostami 3

1 - Department of Applied Mathematics, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran.
2 - Department of Applied Mathematics, Rasht Branch, Islamic Azad University, Rasht, Iran
3 - Department of Applied Mathematics, Lahijan Branch, Islamic Azad University, Lahijan, Iran

تاريخ الإرسال : 13 الأحد , صفر, 1438 تاريخ التأكيد : 04 الأربعاء , ربيع الأول, 1439 تاريخ الإصدار : 06 الأحد , ربيع الثاني, 1439

الکلمات المفتاحية: Data envelopment analysis, TOPSIS, رتبه‌بندی, Ranking, Interval data, تحلیل پوششی داده‌ه, داده‌های بازه‌ای, واحدهای تصمیم‌گیری ایده‌آل و آنتی‌ایده‌آل, نزدیکی نسبی, Ideal and anti-ideal decision-making units, Relative closeness,

ملخص المقالة :

تحلیل پوششی داده‌ها (DEA) روشی برای سنجش عملکرد گروهی از واحدهای تصمیم‌گیری (DMUها) است که از ورودی‌های متعدد برای تولید خروجی‌های متعدد استفاده می‌کنند. این مقاله دو DMUی مجازی به نام DMUی ایده‌آل و DMUی آنتی‌ایده‌آل را وارد DEAی بازه‌ای می‌کند. مدل‌های DEAی بازه‌ای به دست آمده به ترتیب DEAی بازه‌ای با DMUهای ایده‌آل و آنتی‌ایده‌آل نامیده می‌شوند. یکی از آنها DMUها را از دیدگاه کارآیی خوشبینانه ارزیابی می‌کند، در حالی که دیگری آنها را از دیدگاه کارآیی بدبینانه ارزیابی می‌کند. این دو کارآیی بازه‌ای متمایز با هم ترکیب می‌شوند و یک شاخص جامع به نام نزدیکی نسبی به DMUی ایده‌آل را درست مانند رویکرد روش ترجیح ترتیب بر اساس شباهت به جواب ایده‌آل در تصمیم چندشاخصی تشکیل می‌دهند. سپس از شاخص نزدیکی نسبی به عنوان سنجش کلی هر DMU استفاده می‌شود و بر مبنای آن یک رتبه‌بندی کلی برای همه‌ی DMUها به دست می‌آید. یک مثال نیز در زمینه‌ی ارزیابی عملکرد بیست شعبه‌ی بانک ارائه خواهد شد که نشان می‌دهد که رویکرد DEAی بازه‌ای پیشنهادی یک روش ساده، مؤثر و عملی برای اندازه‌گیری عملکرد در موقعیت‌های زندگی واقعی است.

المصادر:


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_||_

  1. Azizi, Hossein, & Ganjeh Ajirlu, Hassan. (2011). Measurement of the worst practice of decision-making units in the presence of non-discretionary factors and imprecise data. Applied Mathematical Modelling, 35(9), 4149-4156. doi: http://dx.doi.org/10.1016/j.apm.2011.02.038
    2.
  2. Belton, Valerie, & Vickers, Stephen P. (1993). Demystifying DEA — A Visual Interactive Approach Based on Multiple Criteria Analysis. Journal of the Operational Research Society, 44(9), 883-896. doi: 10.1057/jors.1993.157
    3.
  3. Bouyssou, D. (1999). Using DEA as a tool for MCDM: some remarks. Journal of the Operational Research Society, 50(9), 974-978.
    4.
  4. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi: http://dx.doi.org/10.1016/0377-2217(78)90138-8
    5.
  5. Cook, Wade D., Kress, Moshe, & Seiford, Lawrence M. (1993). On the Use of Ordinal Data in Data Envelopment Analysis. Journal of the Operational Research Society, 44(2), 133-140.
    6.
  6. Cook, Wade D., Kress, Moshe, & Seiford, Lawrence M. (1996). Data Envelopment Analysis in the Presence of Both Quantitative and Qualitative Factors. J Oper Res Soc, 47(7), 945-953.
    7.
  7. Cooper, W. W., Park, K. S., & Yu, G. (2001a). IDEA (Imprecise Data Envelopment Analysis) with CMDs (Column Maximum Decision Making Units). Journal of the Operational Research Society, 52(2), 176-181. doi: 10.1057/palgrave.jors.2601070
    8.
  8. Cooper, W. W., Park, K. S., & Yu, G. (2001b). An Illustrative Application of Idea (Imprecise Data Envelopment Analysis) to a Korean Mobile Telecommunication Company. Operations Research, 49(6), 807-820. doi: 10.1287/opre.49.6.807.10022
    9.
  9. Cooper, William W., Park, Kyung Sam, & Yu, Gang. (1999). IDEA and AR-IDEA: Models for Dealing with Imprecise Data in DEA. Management Science, 45(4), 597-607. doi: 10.1287/mnsc.45.4.597
    10.
  10. Despotis, Dimitris K., & Smirlis, Yiannis G. (2002). Data envelopment analysis with imprecise data. European Journal of Operational Research, 140(1), 24-36. doi: http://dx.doi.org/10.1016/S0377-2217(01)00200-4
    11.
  11. Entani, Tomoe, Maeda, Yutaka, & Tanaka, Hideo. (2002). Dual models of interval DEA and its extension to interval data. European Journal of Operational Research, 136(1), 32-45. doi: https://doi.org/10.1016/S0377-2217(01)00055-8
    12.
  12. Hwang, Ching-Lai, & Yoon, Kwangsun. (1981). Multiple attribute decision making: methods and applications: a state-of-the-art survey. Berlin; New York: Springer-Verlag.
    13.
  13. Jahanshahloo, G. R., Hosseinzadeh Lotfi, F., Rostamy Malkhalifeh, M., & Ahadzadeh Namin, M. (2009). A generalized model for data envelopment analysis with interval data. Applied Mathematical Modelling, 33(7), 3237-3244. doi: 10.1016/j.apm.2008.10.030
    14.
  14. Kao, Chiang, & Liu, Shiang-Tai. (2000a). Data Envelopment Analysis with Missing Data: An Application to University Libraries in Taiwan. The Journal of the Operational Research Society, 51(8), 897-905. doi: 10.2307/254045
    15.
  15. Kao, Chiang, & Liu, Shiang-Tai. (2000b). Fuzzy efficiency measures in data envelopment analysis. Fuzzy Sets and Systems, 113(3), 427-437. doi: http://dx.doi.org/10.1016/S0165-0114(98)00137-7
    16.
  16. Kim, Soung-Hie, Park, Choong-Gyoo, & Park, Kyung-Sam. (1999). An application of data envelopment analysis in telephone officesevaluation with partial data. Computers & Operations Research, 26(1), 59-72. doi: http://dx.doi.org/10.1016/S0305-0548(98)00041-0
    17.
  17. Park, K. S. (2004). Simplification of the transformations and redundancy of assurance regions in IDEA (imprecise DEA). Journal of the Operational Research Society, 55(12), 1363-1366. doi: 10.1057/palgrave.jors.2601824
    18.
  18. Park, Kyung Sam, & Kim, Soung Hie. (1997). Tools for interactive multiattribute decisionmaking with incompletely identified information. European Journal of Operational Research, 98(1), 111-123. doi: http://dx.doi.org/10.1016/0377-2217(95)00121-2
    19.
  19. Sage, A. P., & White, C. C. (1984). ARIADNE: A knowledge-based interactive system for planning and decision support. Systems, Man and Cybernetics, IEEE Transactions on, SMC-14(1), 35-47. doi: 10.1109/TSMC.1984.6313267
    20.
  20. Stewart, Theodor J. (1996). Relationships between Data Envelopment Analysis and Multicriteria Decision Analysis. Journal of the Operational Research Society, 47(5), 654-665. doi: 10.1057/jors.1996.77
    21.
  21. Wang, Ying-Ming, Greatbanks, Richard, & Yang, Jian-Bo. (2005). Interval efficiency assessment using data envelopment analysis. Fuzzy Sets and Systems, 153(3), 347-370. doi: http://dx.doi.org/10.1016/j.fss.2004.12.011
    22.
  22. Wang, Ying-Ming, & Luo, Ying. (2006). DEA efficiency assessment using ideal and anti-ideal decision making units. Applied Mathematics and Computation, 173(2), 902-915. doi: http://dx.doi.org/10.1016/j.amc.2005.04.023
    23.
  23. Wang, Ying-Ming, & Yang, Jian-Bo. (2007). Measuring the performances of decision-making units using interval efficiencies. Journal of Computational and Applied Mathematics, 198(1), 253-267. doi: http://dx.doi.org/10.1016/j.cam.2005.12.025
    24.
  24. Zhu, Joe. (2003). Imprecise data envelopment analysis (IDEA): A review and improvement with an application. European Journal of Operational Research, 144(3), 513-529. doi: 10.1016/S0377-2217(01)00392-7
    25.
  25. Zhu, Joe. (2004). Imprecise DEA via Standard Linear DEA Models with a Revisit to a Korean Mobile Telecommunication Company. Operations Research, 52(2), 323-329. doi: 10.1287/opre.1030.0072
    26.

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