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This paper introduces a mixed-integer linear programming (MILP) model that automates and optimizes the scheduling of Marine Autonomous Vehicles (MAVs) for environmental data collection. By integrating conventional ship itineraries and considering operational constraints such as battery capacities and time windows, the model aims to maximize data collection while minimizing vehicle deployment and energy consumption. Computational experiments reveal that the model efficiently handles routing for fleets of dozens of MAVs in seconds and scales to hundreds of vehicles within minutes, providing a valuable tool for both operational scheduling and strategic decision-making in marine science.
MAV scheduling can be automated to maximize data collection efficiency while minimizing energy use, revolutionizing marine research operations.
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.
