تخصیص منابع در تحلیل پوششی داده ها بر روی ورودی ها و خروجی های فازی

محورهای موضوعی : تحقیق در عملیات

عصمت نوروزی ¹ , حمید شرفی ²

1 - گروه ریاضی ،واحد تهران شرق ،دانشگاه آزاد اسلامی ،تهران
2 - گروه ریاضی،واحد علوم وتحقیقات ،دانشگاه آزاد اسلامی ،تهران

تاریخ دریافت : 1400/11/26 تاریخ پذیرش : 1401/09/09 تاریخ انتشار : 1402/05/01

کلید واژه: Efficiency, Fuzzy theory, Decision Making Unit, Data Envelopment Analysis, Allocation,

چکیده مقاله :

تکنیک تحلیل پوششی داده ها برای ارزیابی کارایی نسبی مجموعه ای از واحدهای تصمیم گیری استفاده می شود. تجزیه و تحلیل پوششی داده ها در زمینه های مختلف مورد مطالعه قرار گرفته است، به عنوان مثال، تجزیه و تحلیل حساسیت در DEA. تحلیل حساسیت مدل های تحلیل پوششی داده ها بسیار مهم است. متعاقباً مقالات زیادی در این زمینه ارائه شده است،در بعضی مواقع مدیران به مسایلی بر خورد می‌کنندکه تخصیص یک هزینه‌ی ثابت به واحدهای تصمیم‌گیرنده مهم می‌باشدواز آنجا که در اغلب مسایل واقعی داده ها و اطلاعات اولیه دقیق نیستند بلکه کیفی، بازه ای و یا ترتیبی می باشندلذا سعی شده است که در این مقاله این موضوع را مورد بحث قرار داده و مدلی‌ ارائه ‌شودکه تخصیص یک هزینه‌ی ثابت که از نوع فازی است را به واحدهای تصمیم‌گیرنده مورد بررسی قرار دهد. علاوه بر این فرض می‌شود تمامی ورودی‌ها و خروجی‌های واحدها فازی هستند و تخصیص هزینه‌ی جدید باید به گونه‌ای باشد که بیشترین تعدادواحد ناکارا کارا شود و در انتها با دو مثال عددی مورد استفاده قرار گرفته شده و نتایج ارائه شده است.

چکیده انگلیسی:

Abstract: Data envelopment analysis technique is used to evaluate the relative efficiency of a set of decision-making units that have been studied in different fields. One of the important issues in data envelopment analysis is sensitivity analysis. Many articles have been presented in this field by researchers, sometimes managers are concentrating on issues that would be critical to allocate a fixed cost to decision-making units. Since in real problems the primary data are not precise but interval, ordinal, and qualitative therefore this study have been discussed this issue and present a model for assigning a fuzzy fixed cost to decision-making units. Moreover, the inputs and outputs of all units are assumed to be fuzzy and the allocation of new costs should be such that the highest number of inefficient units become efficient. At the end, this model has been utilized in two numerical examples and the results have been presented.

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