文献类型：期刊文献

中文题名：基于偏好的原油移动路径多目标优化

英文题名：A Preference-based Multi-objective Optimization for Crude Oil Movement Path

作者：王舒涵[1,2,3];堵威[1,2,3];唐漾[1,2,3];钟伟民[2,3]

机构：[1]华东理工大学工业控制技术全国重点实验室,上海200237;[2]华东理工大学能源化工过程智能制造教育部重点实验室,上海200237;[3]华东理工大学信息科学与工程学院,上海200237

年份：2024

卷号：50

期号：12

起止页码：2380

中文期刊名：自动化学报

外文期刊名：Acta Automatica Sinica

收录：CSTPCD;;EI(收录号：20250117620594);Scopus;北大核心:【北大核心2023】；CSCD:【CSCD2023_2024】；PubMed;

基金：国家杰出青年科学基金(61925305);国家自然科学基金(62173144,62203173);中央高校基本科研业务费专项资金(222202417006);上海人工智能实验室资助。

语种：中文

中文关键词：大规模原油调度;路径规划;多目标优化;偏好策略

外文关键词：Large-scale crude oil scheduling;path planning;multi-objective optimization;preference-based strategy

摘要：原油移动路径规划是原油调度中至关重要的子任务,直接影响到生产过程中原油供给的稳定性和付油的高效性.由于此任务需要考虑大规模罐区内复杂的设备条件,并受到严格的工业生产约束,同时需要兼顾途径阀门数量与泵机组运力,导致目前依然倚重调度人员的人工经验来制定路径规划方案,对传统算法和进化算法的应用提出了挑战.据此,本研究基于有向图结构对大规模原油罐区进行细致数学建模,并提出一种基于偏好的原油移动路径多目标优化(Preference-based multi-objective optimization for crude oil movement path,PB-MOO)算法,突破了过去高度依赖人工方法的局限性,为原油移动路径规划提供智能化解决方案.实验证明该算法能够在满足实际约束的条件下,找到复杂任务的高质量候选解,验证了其在此领域的可行性和有效性.
The planning of crude oil movement path is a crucial subtask within crude oil scheduling,directly impacting the stability of crude oil supply and the efficiency of oil delivery in the production process.Given the need to consider complex equipment conditions within large-scale tank areas and strict industrial production constraints,while also balancing the number of valves along the route with pump unit capacity,the current reliance on manual experience of schedulers for developing path planning schemes poses a challenge to the application of traditional algorithms and evolutionary algorithms.Therefore,this study undertakes a meticulous mathematical modeling of large-scale crude oil tank areas based on directed graph structures.It proposes a preference-based multi-objective optimization algorithm for crude oil movement path(PB-MOO),overcoming the limitations associated with past heavily manual methods and providing an intelligent solution for crude oil movement path planning.Experimental results demonstrate that the algorithm can identify high-quality candidate solutions for complex tasks while meeting practical constraints.This validates its feasibility and effectiveness in this field.