A New Hybrid Methodology Based on Data Envelopment Analysis and Neural Network for Optimization of Performance Evaluation
محورهای موضوعی : مجله بین المللی ریاضیات صنعتی
A. Namakin
1
(Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
S. E. Najafi
2
(Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
M. Fallah
3
(Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
M. Javadi
4
(Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
کلید واژه: Efficiencyو Linear Programming, Levenberg–Marquardt (LM), Artificial Neural Network, Data Envelopment Analysis,
چکیده مقاله :
In this paper, a new method of combining ANN and DEA (ANN-DEA) presented in which the input and output values for a large number of DMUs determined as neural network inputs. We have also compared the new model with the existing approach of ANN-DEA. To illustrate the ability of the proposed methodology some case studies are used, including a set of 500 Iranian bank branches.
در این مقاله ، یک روش جدید ترکیبی از شبکه های عصبی پرسپترون چند لایه و تحلیل پوششی داده ها ارائه می شود که در آن مقادیر ورودی و خروجی برای تعداد زیادی واحد تصمیم گیرنده به عنوان ورودی های شبکه عصبی تعیین می شود. می توان دید که با بکارگیری شبکه عصبی برای حل مسائل تحلیل پوششی داده ها نیاز به حل مدل مورد نظر برای هر واحد تصمیم گیرنده نیست و لذا الگوریتم ارائه شده زمان پردازش و استفاده از حافظه را نسبت به آنچه مورد نیاز روش متعارف در تحلیل پوششی داده ها است، به مقدار زیادی کاهش می دهد.جهت بررسی دقت شبکه ارائه شده، چندمطالعه موردی از جمله مجموعه ای از 500شعبه بانک مورد استفاده قرار می گیرد.نتایج نشان دهنده دقت بالا وزمان محاسباتی کمتر(اعتبارلازم) مدل ترکیبی پیشنهادی است.
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