A self-learning approach for optimal detailed scheduling of multi-product pipeline

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:327

Article

Zhang, Haoran¹  Liang, Yongtu¹  Liao, Qi¹  Shen, Yun¹  Yan, Xiaohan¹ 

[1] China Univ Petr, Natl Engn Lab Pipeline Safety, Beijing Key Lab Urban oil & Gas Distribut Technol, Fuxue Rd 18, Beijing 102249, Peoples R China

关键词: Multi-product pipeline;    Self-learning approach;    Detailed scheduling;    Mixed-integer linear programming (MILP);    Fuzzy clustering analysis;    Ant colony optimization (ACO);   

DOI  :  10.1016/j.cam.2017.05.040

来源: Elsevier

【 摘 要 】

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. (C) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

Free