摘要
针对传统的船舶信息管理任务调度模型的调度平均等待时间长的问题,研究群智能优化算法的船舶信息管理任务批量流水调度模型。根据群智能优化算法理论,将调度过程中的船舶看作粒子,根据实际调度需求设定调度模型的参数。为缩短船舶调度平均等待时间,规划船舶调度的目标函数以及约束条件。计算粒子适应度,将粒子与任务分配一一映射,根据船舶可信度计算得到局部粒子最优解,更新粒子位置直至出现最优调度解,完成船舶信息管理任务批量流水调度模型的构建。通过与传统调度模型的对比仿真实验,验证构建的基于群智能优化算法的船舶调度模型能够缩短传统调度模型2/3的平均调度等待时间,提高了调度效率。
Aiming at the problem of long average waiting time in traditional ship information management task scheduling model,the ship information management task batch scheduling model with swarm intelligent optimization algorithm is studied.According to the theory of swarm intelligent optimization algorithm,ships in the scheduling process are regarded as particles,and the parameters of the scheduling model are set according to the actual scheduling requirements.In order to shorten the average waiting time of ship dispatching,the objective function and constraint conditions of ship dispatching are planned.The particle fitness is calculated,the particle and task assignment are mapped one by one,the local particle optimal solution is calculated according to the ship reliability,the particle position is updated until the optimal scheduling solution appears,and the construction of batch scheduling model of ship information management task is completed.By comparing with the traditional scheduling model,the simulation results show that the ship scheduling model based on swarm intelligence optimization algorithm can shorten the average scheduling waiting time of the traditional scheduling model by 2/3 and improve the scheduling efficiency.
作者
张晶
潘玉霞
ZHANG Jing;PAN Yu-xia(School of Information and Intelligent Engineering,University of Sanya,Sanya 572000,China)
出处
《舰船科学技术》
北大核心
2019年第20期37-39,共3页
Ship Science and Technology
基金
海南省自然科学基金资助项目(617182).
关键词
群智能优化算法
船舶信息管理
任务批量流水调度
group intelligent optimization algorithm
management of ship information
task batch pipeline scheduling
作者简介
张晶(1983-),女,硕士,讲师,主要从事数据分析及量化分析与算法研究。
