کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5776162 | 1631964 | 2018 | 32 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A self-learning approach for optimal detailed scheduling of multi-product pipeline
ترجمه فارسی عنوان
یک رویکرد خودآموز برای برنامه ریزی دقیق و دقیق خط لوله چند محصول
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
چکیده انگلیسی
Pipeline transportation is cost-optimal in refined product transportation. However, the optimization of multi-product pipeline scheduling is rather complicated due to multi-batch sequent transportation and multi-point delivery. Even though many scholars have conducted researches on the issue, there is hardly a model settling the discontinuous constraints in the model as a result of batch interface migration. Moreover, through investigation, there is no self-learning approach to pipeline scheduling optimization at present. This paper considers batch interface migration and divides the model into time nodes sequencing issue and a mixed-integer linear programming (MILP) model with the known time node sequence. And a self-learning approach is proposed through the combination of fuzzy clustering analysis and ant colony optimization (ACO). This algorithm is capable of self-learning, which greatly improves the calculation speed and efficiency. At last, a real pipeline case in China is presented as an example to illustrate the reliability and practicability of the proposed model.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Computational and Applied Mathematics - Volume 327, 1 January 2018, Pages 41-63
Journal: Journal of Computational and Applied Mathematics - Volume 327, 1 January 2018, Pages 41-63
نویسندگان
Zhang Haoran, Liang Yongtu, Liao Qi, Shen Yun, Yan Xiaohan,