Wind-constrained Pareto multi-objective reinforcement learning for low-carbon vessel traffic organization in port approach channels

Port approach channels concentrate vessel conflicts, waiting, and speed adjustments that can increase fuel use and CO2 emissions, yet real-time wind is rarely represented as both an emission driver and a scheduling constraint. This study develops a wind-constrained vessel traffic organization framework and a Pareto-based multi-objective proximal policy optimization algorithm (Pareto MO-PPO). A wind-aware propulsion model links vessel speed, transit time, relative wind, and CO2 emissions, while wind-dependent engine-load limits restrict the feasible speed range. A preference-conditioned actor-critic network learns policies across the efficiency and emission trade-off, and an external archive retains non-dominated policies. The framework was evaluated in a Caofeidian Port scenario. Relative to first-come, first-served (FCFS) scheduling, the balanced policy reduced emissions by 11.41% while increasing system time by 7.49%. Detailed results show how wind-aware Pareto policy learning can provide port operators with explicit operating choices rather than a single fixed weight solution.