کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10321835 | 660756 | 2015 | 32 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Two-stage production modeling of large U.S. banks: A DEA-neural network approach
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
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چکیده انگلیسی
The purpose of this paper is to explore an innovative performance model for a two-stage sequential production process by combining data envelopment analysis (DEA) and back propagation neural network (BPNN). Recent literature shows a growing interest on performance modeling of two-stage production process using DEA. But, most previous studies on the scope of two-stage modeling are still limited to the efficiency measurement and also have neglected the progressive direction of predictive value and capacity. As an optimization technique, two-stage DEA model lacks predictive capacity. Despite an adaptive prediction model being a practical necessity, this area has rarely been addressed in the previous studies. This paper demonstrates an integrative approach to constructive performance modeling of a two-stage sequential production process by exploring predictive capacity of BPNN in conjunction with DEA. The effectiveness of our jointly integrated performance model through this study is empirically supported by its practical application to the financial banking operations across large U.S. banks.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 42, Issue 19, 1 November 2015, Pages 6758-6766
Journal: Expert Systems with Applications - Volume 42, Issue 19, 1 November 2015, Pages 6758-6766
نویسندگان
He-Boong Kwon, Jooh Lee,