ارائه یک مدل ترکیبی از DEA دو مرحله‌ای و PROMETHEE در محیط خاکستری جهت ارزیابی عملکرد
محورهای موضوعی : آمار
علیرضا علی نژاد
1
(دانشیار گروه مهندسی صنایع ، دانشکده مهندسی صنایع و مکانیک، واحد قزوین ، دانشگاه آزاد اسلامی، قزوین،ایران)
امیر امینی
2
(دانش آموخته کارشناسی ارشد مهندسی صنایع، دانشکده مهندسی صنایع، دانشگاه
صنعتی ارومیه، ارومیه، ایران)
کلید واژه: Two-Stage DEA, Promethee, Performance evaluation, MADM, Gray Numbers,
چکیده مقاله :
یکی از چالش‌های عمده ارزیابی عملکرد در سازمان‌ها و کلیه سیستم‌ها، غیرمنطقی بودن و دقیق نبودن روش‌ها و معیارهای بکار گرفته شده است. روش‌های سنتی ارزیابی عملکرد عمدتا یک سطحی بوده، لذا فاقد توان لازم بمنظور ارائه بازخورد کافی جهت شناسایی واحدهای ناکارا هستند. تحلیل پوششی داده‌ها، یک تکنیک برنامه‌ریزی ریاضی است که کارایی نسبی چندین واحد تصمیم‌گیرنده را بر مبنای ورودی‌ها و خروجی‌های مشاهده شده که ممکن است با انواع مقیاس‌های مختلف بیان شوند، محاسبه می‌کند. در عمل، بسیاری از واحدهای تصمیم‌گیری در درون خود به بخش‌های کوچک‌تری تقسیم می‌شوند و با مدل‌های استاندارد تحلیل پوششی داده‌ها که سازمان را به صورت یک بخش کلی در نظر می‌گیرند، نتایج منطقی حاصل نمی‌شود. بنابراین لازم است از مدل‌هایی همچون مدل DEA دومرحله‌ای متناسب با چنین شرایطی برای ارزیابی دقیق‌تر واحدهای تحت بررسی استفاده شود. همچنین در شرایطی که تعداد زیادی ورودی و خروجی وجود داشته باشد، این روش چندان کارآمد نمی‌باشد و تعداد زیادی از واحدها را کارا اعلام می‌کند لذا برای رفع این مشکل تحقیق حاضر با استفاده از روش PROMETHEE ، ورودی‌ها و خروجی‌ها رتبه‌بندی می‌کند، سپس با مهم‌ترین آنها کار ارزیابی ادامه خواهد یافت. از آنجایی که همواره اطلاعات در دسترس کامل و دقیق نیستند این مهم در فضای خاکستری انجام خواهد شد. یافته‌های به دست آمده نشان‌دهنده کاهش چشمگیر واحدهای شناسایی شده کاراست که در نتیجه بهبود قدرت تمایز روش DEA را نشان می‌دهد. در کنار این، استفاده از محیط عدم اطمینان به ارزیابی‌ها و برآوردهای دقیق‌تر نسبت به مدل‌های قطعی انجامیده است.
One of the main challenges of performance evaluation in organizations and all systems is the irrationality and inaccuracy of the methods and criteria used. Traditional performance evaluation methods are mostly one-level, so they usually fail to provide sufficient feedback to identify inefficient units. Data envelopment analysis is a mathematical programming technique that compares the relative efficiency of several decision-making units based on observed inputs and outputs expressed by a variety of different scales. In practice, since many decision-making units are subdivided into smaller parts, with standard data envelopment analysis models that consider the organization as a whole, logical results are not obtained. Therefore, it would be better to use developed models like the two-stage DEA model to more accurately evaluate under investigation units in these conditions. Moreover, in cases that there are a large number of inputs and outputs, traditional DEA is not very efficient and it may consider a large number of units as efficient one. To deal with the problem, this study uses PROMETHEE method to rank criteria. After that, the efficiency evaluation problem is continued with most important inputs and outputs. Since the available information is usually incomplete and inaccurate, the problem is solved in the gray environment. The findings indicate a significant decrease in the number of identified efficient units which shows the improvement in discrimination power of DEA method. Additionally, the use of uncertain environment has led to more accurate estimates than previous definite models.
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