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 , Akbar Jafari Shaerlar ²

1 - Department of Applied Mathematics, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran
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.  

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