首页|考虑海风及恶劣天气的在航船舶尾气多机型无人机监测研究

考虑海风及恶劣天气的在航船舶尾气多机型无人机监测研究

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考虑海风及恶劣天气所造成的中断影响,研究了在航船舶尾气无人机监测问题.针对海洋气象复杂多变的特点,提出了滚动计划框架,重点考虑船舶可服务时间窗、船舶实时和动态位置、无人机最大遥传距离、飞行速度与电池电量限制及多机型无人机配置等现实因素,以船舶监测收益最大化为目标,构建了无人机-机站-船舶时空网络模型,设计了融入启发式策略的拉格朗日松弛算法求解模型,给出了问题的上界和下界.通过数值实验,验证了模型及算法的有效性,并分析了无人机机型和风力强度的影响,可为海风及恶劣天气下在航船舶尾气多机型无人机监测提供决策支持.
Scheduling of multi-type UAVs to monitor the exhaust of ships underway considering sea wind and severe weather
This paper investigates the UAV scheduling to monitor the exhaust of ships underway by considering the interruptions caused by sea wind and severe weather.In view of the complex and changing characteristics of marine meteorological conditions,a rolling planning framework is proposed.In each rolling planning horizon,we formulate the problem as a UAV-station-ship time-space network model,with the objective of maximizing the benefits of ship monitoring.Our model captures some realistic factors such as the serviceable time window of the ship,real-time and dynamic position of the ship,maximum telemetry distance of the UAVs,flight speed and battery power limitation of the UAVs,and multi-type UAV configuration.To solve this model,we design a Lagrangian relaxation algorithm incorporating several heuristic strategies,so that the upper and lower bounds of the problem can be obtained.Numerical experiments are conducted to validate the effectiveness of the proposed model and algorithm.Some managerial insights are offered by analyzing the effects of UAV configuration and wind intensity to provide decision support for scheduling multi-type UAVs to monitor the exhaust of ships underway under the sea wind and severe weather.

emission control areasship exhaust monitoringUAV schedulingtime-space net-work modelLagrangian relaxation algorithm

刘保利、李志纯、郑建风、余德平

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大连海事大学交通运输工程学院,大连 116026

华中科技大学管理学院,武汉 430074

排放控制区 船舶尾气监测 无人机调度 时空网络模型 拉格朗日松弛算法

国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金

7230105172371046721310087189097071890974

2024

10.12011/SETP2023-0955

系统工程理论与实践
中国系统工程学会

系统工程理论与实践

CSTPCDCSSCI北大核心
影响因子：1.575
ISSN：1000-6788
年,卷(期)：2024.44(5)
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