考虑岸桥作业的集装箱船配载多目标优化
Multi-objective optimization of container ship stowage considering quay crane operation
李俊 1赵雅洁 2肖笛 2温想2
作者信息
- 1. 天津港(集团)有限公司,天津 300461;武汉科技大学汽车与交通工程学院,湖北武汉 430070
- 2. 武汉科技大学汽车与交通工程学院,湖北武汉 430070
- 折叠
摘要
为进一步提高集装箱码头作业效率,降低船舶在港停留时间,将码头前沿负责装卸作业的岸桥设备纳入集装箱船配载决策考虑范围内,通过降低航线上各港口的岸桥作业不均衡量保证船舶在港作业效率.考虑船舶运输安全性、经济性、适航性等需求,以岸桥作业不均衡量、船舶阻塞箱数量、稳心高度、横倾角和纵倾值为目标,构建集装箱船配载多目标优化模型.为有效求解多目标优化问题,采用灰熵并行分析法改进第三代非支配排序遗传算法(non-dominated sorting genetic algorithm Ⅲ,NSGA-Ⅲ).实验结果表明:改进算法在求解性能上优于一般的带精英选择策略的算法,对算例参数设置变化具有较好鲁棒性,可为制订岸桥作业量均衡的集装箱船配载计划提供一定决策支持.
Abstract
In order to further improve the operation efficiency of container terminals and reduce the stay time of ships in ports,the quay crane equipment in charge of loading and unloading at the front of terminals is included in the consideration of container ship stowage decision,and the operation efficiency of ships in a port is guaranteed by reducing the unbalance of quay crane operation at various ports in the route.Considering the requirements of ship transportation safety,economy and seaworthiness,a multi-objective optimization model of container ship stowage is constructed with the objectives of the quay crane operation unbalance,the number of ship blocking containers,the metacentric height,the heel angle and the trim value.In order to solve the multi-objective optimization problem effectively,the gray entropy parallel analysis method is used to improve the non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ).The experimental results show that the improved algorithm is better than the general algorithm with elitist selection strategy in solving performance,and has good robustness to the change of example parameter setting,which can provide a certain decision support for the formulation of container ship stowage plan with operation balance of quay cranes.
关键词
船舶配载/多目标优化/第三代非支配排序遗传算法(NSGA-Ⅲ)/岸桥/作业量均衡Key words
ship stowage/multi-objective optimization/non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ)/quay crane/operation balance引用本文复制引用
基金项目
湖北省教育厅科学技术研究计划(Q20211110)
出版年
2024
NETL
NSTL
万方数据