Marine voyage optimization determines the optimal route and speed to ensure timely arrival. The problem becomes particularly complex when incorporating a dynamic environment, such as future expected weather conditions along the route and unexpected disruptions. This study explores two model-free Deep Reinforcement Learning (DRL) algorithms: (i) a Double Deep Q Network (DDQN) and (ii) a Deep Deterministic Policy Gradient (DDPG). These algorithms are computationally costly, so we split optimization into an offline phase (costly pre-training for a route) and an online phase where the algorithms are fine-tuned as updated weather data become available. Fine tuning is quick enough for en-route adjustments and for updating the offline planning for different dates where the weather might be very different. The models are compared to classical and heuristic methods: the DDPG achieved a 4% lower fuel consumption than the DDQN and was only outperformed by Tabu Search by 1%. Both DRL models demonstrate high adaptability to dynamic weather updates, achieving up to 12% improvement in fuel consumption compared to the distance-based baseline model. Additionally, they are non-graph-based and self-learning, making them more straightforward to extend and integrate into future digital twin-driven autonomous solutions, compared to traditional approaches.

Digital twins can help ports, operators and their customers streamline processes, improve safety, and reduce emissions in transport operations that involve at least a maritime leg. This chapter discusses the necessary context for regulations, port operations, and hinterland capabilities; and frames a general methodology under this context for how port authorities, terminal operators, shipping companies, and logistics operators can implement collaborative, emission reducing practices such as slow steaming and synchro-modality. In short, a federation of digital twins that allows to move information downstream from the ship to the port, then to the hinterland infrastructure as well as upstream the opposite way can enable new levels of operational readiness and efficiency, and importantly environmental compliance. At the same time, apart from the improved general efficiency of all processes associated with transport, shipowners can minimise the need to retrofit existing ships for them to remain compliant with the emissions regulations which are bound to be stricter each passing year.