Marine Autonomous Vehicle Fleet Scheduling to Maximise Scientific Impact

Summary

Abstract Summary

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.

Key Points

The marine science community increasingly relies on Marine Autonomous Vehicles (MAVs) to collect ...
However, as these operations scale, manually routing and planning large autonomous fleets becomes...
To address this, we propose a mixed-integer linear programming (MILP) model designed to automate ...
The model accounts for strict operational constraints, including battery capacities and time wind...
A key novelty of this framework is integrating conventional ship itineraries, allowing MAVs to su...

Marine Autonomous Vehicle Fleet Scheduling to Maximise Scientific Impact

|Authors: Mehdi El Krari, Jonathan Smith, Maria Fox

|Venue: arXiv preprint | Year: 2026

|arXiv: 2608.27271v1

Abstract

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.

Key Contributions

The marine science community increasingly relies on Marine Autonomous Vehicles (MAVs) to collect …
However, as these operations scale, manually routing and planning large autonomous fleets becomes…
To address this, we propose a mixed-integer linear programming (MILP) model designed to automate …
The model accounts for strict operational constraints, including battery capacities and time wind…
A key novelty of this framework is integrating conventional ship itineraries, allowing MAVs to su…

Topics

vision
reinforcement-learning
planning

Code & Data

No code repository linked in paper metadata.

BibTeX

@article{Krari2026_260827271v1,
  title     = {Marine Autonomous Vehicle Fleet Scheduling to Maximise Scientific Impact},
  author    = {Mehdi El Krari and Jonathan Smith and Maria Fox},
  year      = {2026},
  eprint    = {2608.27271v1},
  archivePrefix = {arXiv},
  primaryClass  = {math.OC},
  url       = {https://arxiv.org/abs/2608.27271v1}
}