摘要
船舶舾装装配作业是船舶生产设计中重要的一个环节,现有造船模式下的舾装装配工作仍存在效率低等问题,其原因在于最优装配顺序的规划和舾装托盘资源配置的浪费。针对以上问题,以提高舾装装配效率为目的,建立了相应舾装装配约束的数学模型,研究了基于花朵授粉算法(FPA)的管路舾装单元装配序列规划问题,并结合现实中船舶舾装装配存在的两点问题,对原始花朵授粉算法加以改进,以最小化舾装装配资源分配为目标函数,旨在解决装配序列的多模态化问题,经过改进后的算法运算获得多个唯一最优装配序列,并将其结果与遗传算法(GA)和蚁群优化算法(ACO)进行比较,验证了此改进算法在装配序列多模态化问题上的优越性。
Ship outfitting and assembly operations are an important part of ship production and design,but the outfitting and assembly work in the existing shipbuilding model is still inefficient due to the planning of the optimal assembly sequence and the wasteful allocation of outfitting pallet resources.In order to improve the efficiency of outfitting assembly,a mathematical model of the corresponding outfitting assembly constraint is established,and the planning of the assembly sequence of pipeline outfitting units based on the flower pollination algorithm(FPA)is studied.Combined with two problems existing in ship outfitting assembly in reality,the original flower pollination algorithm is improved to minimize the allocation of outfitting assembly resources as the objective function,aiming to solve the multimodality problem of assembly sequence.After the improved algorithm operation,multiple unique optimal assembly sequences are obtained,The results are compared with genetic algorithm(GA)and ant colony optimization algorithm(ACO)to verify the superiority of this improved algorithm in the multimodality problem of assembly sequence.
作者
茅云生
万佳磊
宋利飞
孙学荣
MAO Yunsheng;WAN Jialei;SONG Lifei;SUN Xuerong(School of Naval Architecture Ocean and Energy Power Engineering,Wuhan University of Technology,Wuhan 430063,China;China Merchants Heavy Industry(Jiangsu)Co.,Ltd.,Haimen 226100,Jiangsu,China)
出处
《船舶工程》
CSCD
北大核心
2022年第S01期565-570,共6页
Ship Engineering
基金
工信部及财政部“中型邮轮设计建造技术”项目(MC-201917-C09)
作者简介
茅云生(1962—),男,教授。研究方向:船舶先进制造技术;通信作者:宋利飞(1989—),男,副教授。研究方向:智能船舶。
