Controlling a cargo ship without human experience based on deep Q-network

Human experience is regarded as an indispensable part of artificial intelligence in the process of controlling or decision making for autonomous cargo ships. In this paper, a novel Deep Q-Network-based (DQN) approach is proposed, which performs satisfactorily in controlling a cargo ship automatically without any human experience. At the very beginning, we use the model of KRISO Very Large Crude Carrier (KVLCC2) to describe a cargo ship. To manipulate this ship has to conquer great inertia and relatively insufficient driving force. Subsequently, customary waterways, regulations, conventions are described with Artificial Potential Field and value-functions in DQN. Based on this, the artificial intelligence of planning and controlling a cargo ship can be obtained by undertaking sufficient training, which can control the ship directly, while avoiding collisions, keeping its position in the middle of the route as much as possible. In simulation experiments, it is demonstrated that such an approach performs better than manual works and other traditional methods in most conditions, which makes the proposed method a promising solution in improving the autonomy level of cargo ships

Multimodal schedule design for synchromodal freight transport systems

Intermodal freight transport has been discussed for decades as an alternative to unimodal road transport. However, it still does not represent a significant portion of the total freight market. A new and promising possibility to improve the performance of freight systems is the synchromodal design of hinterland transport systems. A cornerstone for synchromodality is an integrated view in the design and operation of intermodal transport. A main benefit of this integrated view is an improved flexibility in mode choice in hinterland transport. This paper gives a detailed description of this integrated view for synchromodal freight transport. Based on this description, a mathematical model for designing service schedules for synchromodal freight transport systems is also presented. The benefits of providing integrated transport services compared to separately planned transport services are also discussed for a case in the hinterland network of the Port of Rotterdam

The Cargo Fare Class Mix problem for an intermodal corridor: revenue management in synchromodal container transportation

The intermodal hinterland transportation of maritime containers is under pressure from port authorities and shippers to achieve a more integrated, efficient network operation. Current optimisation methods in literature yield limited results in practice, though, as the transportation product structure limits the flexibility to optimise network logistics. Synchromodality aims to overcome this by a new product structure based on differentiation in price and lead time. Each product is considered as a fare class with a related service level, allowing to target different customer segments and to use revenue management for maximising revenue. However, higher priced fare classes come with tighter planning restrictions and must be carefully balanced with lower priced fare classes to match available capacity and optimise network utilisation. Based on the developments of intermodal networks in North West European, such as the network of European Gateway Services, the Cargo Fare Class Mix problem is proposed. Its purpose is to set limits for each fare class at a tactical level, such that the expected revenue is maximised, considering the available capacity at the operational level. Setting limits at the tactical level is important, as it reflects the necessity of long-term agreements between the transportation provider and its customers. A solution method for an intermodal corridor is proposed, considering a single intermodal connection towards a region with multiple destinations. The main purpose of the article is to show that using a limit on each fare class increases revenue and reliability, thereby outperforming existing fare class mix policies, such as Littlewood