发表机构
British Antarctic Survey; AI Lab（英国南极调查局; 人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对海洋自主航行器集群调度难题，提出混合整数线性规划模型，整合船舶航线实现高效调度，可快速求解大规模集群问题，兼具调度与仿真功能，支持战略决策。
AI 中文摘要
海洋科学界越来越依赖海洋自主航行器（Marine Autonomous Vehicles, MAVs）收集理解全球海洋系统所需的关键环境数据。然而，随着这类作业规模扩大，手动规划大型自主航行器集群的路径和调度会变得极其复杂且耗时。为解决这一问题，我们提出一种混合整数线性规划（mixed-integer linear programming, MILP）模型，旨在自动化并优化MAV的部署调度。该模型考虑严格的作业约束，包括电池容量和数据采集时间窗口，同时目标是最大化总数据采集量、最小化部署航行器数量及其能耗。该框架的一个关键创新点是整合常规船舶航线，使MAV能够为船舶提供任务中途换电支持，或在航路点间加速航行。计算实验表明，该模型具有高可扩展性，能在数秒内解决数十架MAV集群的路径规划问题，扩展至数百架航行器仅需数分钟。除作业调度外，该框架还可作为稳健的仿真工具，用于评估“假设”情景并分析不同参数对部署策略的影响。最后，该解决方案生成一套可视化工具，旨在提升可解释性并支持利益相关者的战略决策。
英文摘要
The marine science community increasingly relies on Marine Autonomous Vehicles (MAVs) to collect the critical environmental data required to understand global ocean systems. However, as these operations scale, manually routing and planning large autonomous fleets becomes exponentially complex and time-consuming. To address this, we propose a mixed-integer linear programming (MILP) model designed to automate and optimise MAV deployment schedules. The model accounts for strict operational constraints, including battery capacities and time windows for data collection, while aiming to maximise total data collection and minimise both the number of deployed vehicles and their energy consumption. A key novelty of this framework is integrating conventional ship itineraries, allowing MAVs to support vessels with mid-mission battery swapping or accelerated transit between waypoints. Computational experiments demonstrate that the model is highly scalable, solving routing problems for fleets of dozens of MAVs in seconds, and scaling to hundreds of vehicles in only a few minutes. Beyond operational scheduling, the framework serves as a robust simulation tool for evaluating 'what-if' scenarios and analysing the impact of varying parameters on deployment strategies. Finally, the solution generates a suite of visualisations designed to enhance explainability and support strategic decision-making for stakeholders.
