BERS: Locally Optimal Continuous Algorithm for Maritime Weather Routing with Just-in-Time Arrival

2026-05-29

AI Summary

This study addresses the challenge of maritime route planning under dynamic wind and wave conditions, balancing obstacle avoidance, on-time arrival, and energy efficiency. The authors propose BERS, a two-stage optimization framework that first employs CMA-ES to perform global evolutionary search over Bézier curve-parameterized routes and then refines trajectory smoothness and feasibility through local variational optimization using the FMS method. This approach uniquely integrates Bézier-based route modeling with local variational refinement, enabling flexible multi-objective design and robust convergence under complex ocean currents and geographic constraints. Experiments based on ERA5 meteorological data and a physics-based vessel dynamics model demonstrate that route optimization alone reduces propulsion energy consumption by 23%–59% on real transoceanic voyages compared to great-circle baselines; when combined with rigid wing sails, total energy savings reach up to 75%.

Abstract

Maritime weather routing must optimize route geometry under dynamic wind-wave conditions, obstacle constraints, and fixed-arrival requirements. We present Bézier Evolve and Refine Strategy (\name{}), a two-stage framework that combines global evolutionary search (CMA-ES) with local variational refinement (FMS). Routes are parametrized as Bézier curves and evaluated with dense along-path sampling, enabling smooth trajectories while preserving practical feasibility constraints and accounting for mid-segment effects. We evaluate \name{} on synthetic benchmarks designed to stress seven operational criteria: continuity, obstacle avoidance, dynamic adaptation, flexible objective design, constant-load feasibility, just-in-time arrival, and local optimality. Across these tests, \name{} matches or improves published baselines while maintaining robust convergence under challenging flow fields and land geometries. We then validate the method on real ocean data using hourly ERA5 forcing over 366 daily departures in 2024 for two trans-oceanic corridors (Atlantic and Pacific), with a physics-based model of an 88~m cargo vessel with optional rigid wingsails. In real-ocean experiments, route optimization alone reduces mean propulsive energy by 23--59\% versus great-circle baselines of the same propulsion mode. Combined with wind-assisted propulsion, total savings reach up to 75\%. These results show that \name{} provides a practical and scalable foundation for just-in-time, energy-efficient weather routing in maritime decarbonization workflows.

Problem

• maritime weather routing
• just-in-time arrival
• route optimization
• dynamic wind-wave conditions
• obstacle constraints

Innovation

• Bézier curves
• weather routing
• CMA-ES
• variational refinement
• just-in-time arrival