ارزیابی و انتخاب تأمین کننده به وسیله‌ی مدل‌های DEAی بازه‌ای با ناحیه‌ی اطمینان: یک رویکرد DEA با مرزهای کارآ و ناکارآ

محورهای موضوعی : مدیریت صنعتی

Hossein Azizi ¹ (Department of Applied Mathematics, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran)
Akbar Jafari Shaerlar ² (Department of Applied mathematics, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran)

تاریخ دریافت : 1396/10/17 تاریخ پذیرش : 1396/10/17 تاریخ انتشار : 1392/09/01

کلید واژه: Data envelopment analysis, انتخاب تأمین کننده, کارآیی‌های خوشبینانه و بدبینانه, Optimistic and pessimistic efficiencies, تحلیل پوششی داده‌ها, Supplier selection, ناحیه‌ی اطمینان, عملکرد کلی, assurance region, overall performance,

چکیده مقاله :

به طور سنتی، مدل‌های ارزیابی و انتخاب تأمین کننده مبتنی بر داده‌های اصلی با تأکید کمتر بر روی داده‌های ترتیبی بوده‌اند. اما با استفاده‌ی گسترده از فلسفه‌های تولید، مانند تولید بهنگام، تأکید بیشتری بر لحاظ کردن همزمان داده‌های اصلی و ترتیبی در فرآیند انتخاب تأمین کننده می‌شود. کاربرد تحلیل پوششی داده‌ها (DEA) برای مسایل ارزیابی و انتخاب تأمین کننده مبتنی بر انعطاف‌پذیری کامل وزن‌ها است. با این حال، مشکل مجاز دانستن انعطاف‌پذیری کامل وزن‌ها آن است که مقادیر وزن به دست آمده با حل برنامه‌ی DEAی نامقید غالباً با نظرات قبلی یا اطلاعات موجود اضافی در تعارض است. هدف این مقاله پیشنهاد مدل‌های DEAی بازه‌ای با ناحیه‌ی اطمینان برای ارزیابی و انتخاب بهترین تأمین کننده در حضور محدودیت‌های وزنی و داده‌های نادقیق است. این مقاله رویکرد جدیدی مبتنی بر DEA با مرزهای کارآ و ناکارآ را برای ارزیابی و انتخاب بهترین تأمین کننده در حضور محدودیت‌های وزنی و داده‌های نادقیق پیشنهاد می‌کند. در این رویکرد، همزمان کارآیی‌های خوشبینانه و بدبینانه‌ی هر تأمین کننده در نظر گرفته می‌شوند. وقتی که قیود ناحیه‌ی اطمینان به مدل‌های خوشبینانه‌ی DEA بازه‌ای اضافه می‌شوند، نمرات بازه‌ی کارآیی محاسبه شده بدتر می‌شوند، و یک تأمین کننده که قبلاً به عنوان کارآی خوشبینانه تعیین شده بود، ممکن است غیرکارآی خوشبینانه شناخته شود. وقتی که قیود ناحیه‌ی اطمینان به مدل‌های بدبینانه‌ی DEAی بازه‌ای اضافه می‌شوند، نمرات بازه‌ی کارآیی محاسبه شده بهبود می‌یابند، و یک تأمین کننده که قبلاً به عنوان ناکارآی بدبینانه شناسایی می‌شد، ممکن است غیرناکارآی بدبینانه شناخته شود. در مقایسه با DEAی سنتی، رویکرد DEA با مرزهای کارآ و ناکارآ می‌تواند بهترین تأمین کننده را به درستی و به آسانی شناسایی کند. یک مثال عددی کاربرد رویکرد پیشنهادی را نشان می‌دهد..

چکیده انگلیسی:

Traditionally, suppliers evaluation and selection models based on basic data put less emphasis on ordinal data; however, with the extensive use of production philosophies, such as Just In Time (JIT) production, further emphasis is put on considering cardinal and ordinal data simultaneously through the supplier selection process. Application of Data Envelopment Analysis (DEA) for the issues concerning the evaluation and selection of the supplier is based on the complete flexibility of weights. Yet, the problem is permissibility of complete flexibility of their weights, as the weight values obtained through the solving unrestricted DEA program are often contrary to the earlier viewpoints or the additional information. The present paper aims to propose interval DEA models with assurance region for evaluation and selection of the best supplier in the presence of weight restrictions and imprecise data. It proposes a new approach based on “DEA with efficient and inefficient frontiers” for evaluation and selection of the best supplier in the presence of weight restrictions and imprecise data. In this approach, optimistic and pessimistic efficiencies are considered simultaneously for each supplier. When the assurance region constraints are added to the interval DEA optimistic models, scores of calculated efficiency interval become worse and a supplier previously determined as the optimistic efficient supplier may be recognized as optimistic non-efficient. When the assurance region constraints are added to the interval DEA pessimistic models, scores of calculated efficiency interval are improved and a supplier previously recognized as a pessimistic inefficient supplier may be recognized as pessimistic non-inefficient. Comparing traditional DEA, DEA approach with efficient and inefficient frontiers may recognize the best supplier correctly and conveniently. A numerical example shows the application of the proposed approach.  

منابع و مأخذ:

1-      Azizi, Hossein. (2011). The interval efficiency based on the optimistic and pessimistic points of view. Applied Mathematical Modeling, 35(5), 2384-2393.

2-      Azizi, Hossein, & Fathi Ajirlu, Shahruz. (2010). Measurement of overall performances of decision-making units using ideal and anti-ideal decision-making units. Computers & Industrial Engineering, 59(3), 411-418.

3-      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.

4-      Azizi, Hossein, & Jahed, Rasul. (2011). Improved data envelopment analysis models for evaluating interval efficiencies of decision-making units. Computers & Industrial Engineering, 61(3), 897-901.

5-      Azizi, Hossein, & Wang, Ying-Ming. (2013). Improved DEA models for measuring interval efficiencies of decision-making units. Measurement, 46, 1325-1332.

6-      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.

7-      Braglia, Marcello, & Petroni, Alberto. (2000). A quality assurance-oriented methodology for handling trade-offs in supplier selection. International Journal of Physical Distribution & Logistics Management, 30(2), 96-112.

8-      Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.

9-      Charnes, A., Cooper, W. W., Wei, Q. L., & Huang, Z. M. (1989). Cone ratio data envelopment analysis and multi-objective programming. International Journal of Systems Science, 20(7), 1099-1118.

10-  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.

11-  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.

12-  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.

13-  Despotis, Dimitris K., & Smirlis, Yiannis G. (2002). Data envelopment analysis with imprecise data. European Journal of Operational Research, 140(1), 24-36.

14-  Farzipoor Saen, Reza. (2008). Supplier selection by the new AR-IDEA model. The International Journal of Advanced Manufacturing Technology, 39(11-12), 1061-1070.

15-  Forker, Laura B., & Mendez, David. (2001). An analytical method for benchmarking best peer suppliers. International Journal of Operations & Production Management, 21(1/2), 195-209.

16-  Kahraman, Cengiz, Cebeci, Ufuk, & Ulukan, Ziya. (2003). Multi-criteria supplier selection using fuzzy AHP. Logistics Information Management, 16(6), 382-394.

17-  Liu, Jian, Ding, Fong-Yuen, & Lall, Vinod. (2000). Using data envelopment analysis to compare suppliers for supplier selection and performance improvement. Supply Chain Management: An International Journal, 5(3), 143-150.

18-  Moore, R.E., & Bierbaum, F. (1979). Methods and applications of interval analysis: Siam.

19-  Noorul Haq, A., & Kannan, G. (2006). Design of an integrated supplier selection and multi-echelon distribution inventory model in a built-to-order supply chain environment. International Journal of Production Research, 44(10), 1963-1985.

20-  Pi, Wei-Ning, & Low, Chinyao. (2006). Supplier evaluation and selection via Taguchi loss functions and an AHP. The International Journal of Advanced Manufacturing Technology, 27(5-6), 625-630.

21-  Sarrico, C. S., & Dyson, R. G. (2004). Restricting virtual weights in data envelopment analysis. European Journal of Operational Research, 159(1), 17-34.

22-  Sengupta, Atanu, & Pal, Tapan Kumar. (2000). On comparing interval numbers. European Journal of Operational Research, 127(1), 28-43.

23-  Shin, Hojung, Collier, David A., & Wilson, Darryl D. (2000). Supply management orientation and supplier/buyer performance. Journal of Operations Management, 18(3), 317-333.

24-  Talluri, Srinivas, & Baker, R. C. (2002). A multi-phase mathematical programming approach for effective supply chain design. European Journal of Operational Research, 141(3), 544-558.

25-  Talluri, Srinivas, Narasimhan, Ram, & Nair, Anand. (2006). Vendor performance with supply risk: A chance-constrained DEA approach. International Journal of Production Economics, 100(2), 212-222.

26-  Talluri, Srinivas, & Sarkis, Joseph. (2002). A model for performance monitoring of suppliers. International Journal of Production Research, 40(16), 4257-4269.

27-  Thompson, Russell G., Langemeier, Larry N., Lee, Chih-Tah, Lee, Euntaik, & Thrall, Robert M. (1990). The role of multiplier bounds in efficiency analysis with application to Kansas farming. Journal of Econometrics, 46(1–2), 93-108.

28-  Wang, Ying-Ming, & Chin, Kwai-Sang. (2009). A new approach for the selection of advanced manufacturing technologies: DEA with double frontiers. International Journal of Production Research, 47(23), 6663-6679.

29-  Wang, Ying-Ming, Chin, Kwai-Sang, & Yang, Jian-Bo. (2007). Measuring the performances of decision-making units using geometric average efficiency. Journal of the Operational Research Society, 58(7), 929-937.

30-  Wang, Ying-Ming, Greatbanks, Richard, & Yang, Jian-Bo. (2005). Interval efficiency assessment using data envelopment analysis. Fuzzy Sets and Systems, 153(3), 347-370. 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.

31-  Weber, Charles A. (1996). A data envelopment analysis approach to measuring vendor performance. Supply Chain Management: An International Journal, 1(1), 28-39.

32-  Weber, Charles A., Current, John, & Desai, Anand. (2000). An optimization approach to determining the number of vendors to employ. Supply Chain Management: An International Journal, 5(2), 90-98.

33-  Wong, Y. H. B., & Beasley, J. E. (1990). Restricting Weight Flexibility in Data Envelopment Analysis. Journal of the Operational Research Society, 41(9), 829-835.

34-  Zhu, Joe. (2003). imprecise data envelopment analysis (IDEA): A review and improvement with an application. European Journal of Operational Research, 144(3), 513-529.