دانلود مقاله ISI انگلیسی شماره 51651
ترجمه فارسی عنوان مقاله

یک مدل بهینه سازی برای شبکه لجستیک معکوس در محیط تصادفی با استفاده از الگوریتم ژنتیک

عنوان انگلیسی
An optimization model for reverse logistics network under stochastic environment by using genetic algorithm
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
51651 2014 9 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Manufacturing Systems, Volume 33, Issue 3, July 2014, Pages 348–356

ترجمه کلمات کلیدی
شبکه لجستیک معکوس ؛ الگوریتم ژنتیک؛ اولویت مبتنی بر کدگذاری - برنامه نویسی تصادفی
کلمات کلیدی انگلیسی
Reverse logistics network; Genetic algorithm (GA); Priority-based encoding; Stochastic programming
پیش نمایش مقاله
پیش نمایش مقاله  یک مدل بهینه سازی برای شبکه لجستیک معکوس در محیط تصادفی با استفاده از الگوریتم ژنتیک

چکیده انگلیسی

Recovery of used products has become increasingly important recently due to economic reasons and growing environmental or legislative concern. Product recovery, which comprises reuse, remanufacturing and materials recycling, requires an efficient reverse logistic network. One of the main characteristics of reverse logistics network problem is uncertainty that further amplifies the complexity of the problem. The degree of uncertainty in terms of the capacities, demands and quantity of products exists in reverse logistics parameters. With consideration of the factors noted above, this paper proposes a probabilistic mixed integer linear programming model for the design of a reverse logistics network. This probabilistic model is first converted into an equivalent deterministic model. In this paper we proposed multi-product, multi-stage reverse logistics network problem for the return products to determine not only the subsets of disassembly centers and processing centers to be opened, but also the transportation strategy that will satisfy demand imposed by manufacturing centers and recycling centers with minimum fixed opening cost and total shipping cost. Then, we propose priority based genetic algorithm to find reverse logistics network to satisfy the demand imposed by manufacturing centers and recycling centers with minimum total cost under uncertainty condition. Finally, we apply the proposed model to a numerical example.