UAV Remote Sensing Mission Scheduling for Vessel Emission Monitoring in Emission Control Areas

Maritime emissions in Emission Control Areas pose significant environmental and health concerns, and effective monitoring is essential for regulatory compliance. This paper addresses the scheduling of drones as a remote sensing platform for inspecting vessel emissions in these areas. A mixed-integer linear programming framework is proposed based on a time-expanded network representation. The model incorporates three practical features of the inspection environment. First, vessels are assumed to follow uniform linear motion within a narrow approach channel, enabling a closed-form expression for drone flying times. Second, inspection sub-windows are introduced to account for exhaust plume interference that may affect detection accuracy. Third, a time-dependent weighting scheme is adopted to encourage early inspections. The resulting optimisation model is solved directly using Gurobi. Computational experiments on instances with 30 vessels and a fleet of 15 drones demonstrate that the proposed formulation achieves an average optimality gap of 0.01%, with an average solution time of 5.91 s. Sensitivity analyses reveal that fleet size beyond 15 drones yields negligible additional benefit, while raising drone speed from 20 to 35 knots enables multi-vessel inspection tours and yields a relative increase of 85% in the number of vessels inspected. The results indicate that the proposed framework provides effective support for drone-based vessel inspection scheduling in Emission Control Areas.