نوع مقاله : مقاله پژوهشی - کاربردی
نویسندگان
گروه ریاضی و علوم کامپیوتر، دانشکده علوم پایه، دانشگاه لرستان، خرم آباد، ایران.
چکیده
هدف: این مقاله روشی هوشمند برای به‌کارگیری تحلیل پوششی داده‌ها (DEA) در طراحی زنجیره‌تامین پایدار ارایه می‌کند.
روش‌شناسی پژوهش: در روش پیشنهادی، برای غیرفازی‌سازی مدل خطی‌شده ERM، از تکنیک α-برش استفاده می‌شود. سپس، برای اندازه‌گیری بهره‌وری در محیط نامعین با α-سطح‌های مختلف، از الگوریتم ژنتیک برای یافتن α-برش بهینه استفاده می‌شود و با جایگزینی α-برش بهینه در مدل پیشنهادی، یک مدل تحلیل پوششی داده‌های هوشمند برای رتبه‌بندی شرکت‌های تامین‌کننده طراحی می‌شود.
یافته‌ها: این مقاله یک مدل جدید تحلیل پوششی داده‌های فازی ژنتیکی برای ارزیابی بهره‌وری تامین‌کنندگان در زنجیره‌تامین پایدار ارایه می‌کند.
اصالت/ارزش‌افزوده علمی: در روش پیشنهادی، ازآنجاکه α-برش به‌دست‌آمده از الگوریتم ژنتیک بهینه‌ترین α-برش است، دیگر نیازی به محاسبه بهره‌وری به ازای α-برش‌های مختلف به‌صورت سعی‌وخطا نیست؛ بنابراین، مزیت روش پیشنهادی این است که علاوه بر بهره‌وری بیشتر برای هر تامین‌کننده، رتبه‌بندی پایدارتری ارایه می‌دهد. مثال ارایه‌شده در این مقاله، برتری و مزایای روش پیشنهادی را نشان می‌دهد.
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
Design an intelligent model for suppliers productivity evaluation in sustainable supply chain
نویسندگان [English]
- Majid Yarahmadi
- Saeedeh Sakiniya
Department of Mathematics and Computer Sciences, Faculty of Base Sciences, Lorestan University, Khorramabad, Iran.
چکیده [English]
Purpose: This paper presents an intelligent method for applying Data Envelopment Analysis (DEA) to design a sustainable supply chain.
Methodology: In the proposed method, for defuzzification of the ERM model, we used the -cutting technique. Then, to measure the productivity in the presence of environmental uncertainty via different -levels, a genetic algorithm is implemented to find an optimal -cutting. Finally, an intelligent DEA model for ranking the supplier companies via optimal value is designed.
Findings: This paper presents a new fuzzy DEA model based on a Genetic Algorithm for evaluating the productivity of suppliers in a sustainable supply chain.
Originality/Value: In the proposed method, since the -cut obtained from the Genetic Algorithm is optimal, there is no longer a need to calculate the efficiency for different α-cuts through trial and error. Therefore, the proposed method's advantage is that it offers a more sustainable ranking in addition to increasing productivity for each supplier. The example presented in this article demonstrates the method's superiority and advantages.
کلیدواژه‌ها [English]
- Data envelopment analysis
- Genetic algorithm
- Integrated enhanced Russell measure model
- Supply chain management
- Sustainable supplier selection
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