文献类型：期刊文献

英文题名：Large-scale crude oil scheduling: A framework of hybrid optimization based on plan decomposition

作者：Zhang, Wanting[1]; Du, Wei[1]; Yu, Guo[1]; He, Renchu[1]; Du, Wenli[1]

机构：[1] East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, China

年份：2022

外文期刊名：2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings

收录：EI(收录号：20223912809414)

基金：ACKNOWLEDGEMENTS This research was supported by the National Key Research & Development Program - Intergovernmental International Science and Technology Innovation Cooperation Project (2021YFE0112800), National Natural Science Fund for Distinguished Young Scholars (61725301), National Natural Science Foundation of China (62173144, 62103150) and Shanghai AI Lab.

语种：英文

外文关键词：Crude oil - Mathematical programming - Scheduling

摘要：In large refineries, the resource-oriented plan of crude oil is commonly required to be tractable and decomposable for practical operation scheduling, especially for large-scale scheduling. To this end, a framework of hybrid optimization based on plan decomposition (FHO/PD) is proposed, which mainly depends on evolutionary algorithms to realize the flexible decomposition from large-scale planning to scheduling and takes advantage of mathematical programming to improve the solving efficiency synchronously. Finally, the experimental results on a practical case suggest that the proposed method has shown great flexibility and applicability in crude oil scheduling. ? 2022 IEEE.