To relieve pressure from large vessels and intensive arrival of trucks, an increasing number of marine terminals require trucks to make appointments prior to delivering containers. The quotas are supposed not to impede truck deliveries but should be sufficiently used. For terminals where trucks are allowed to make appointments and arrive on the same day, a critical problem is the simultaneous allocation of appointment quotas and yard cranes. A bi-objective integer model is set up to balance the trade-off between terminals and trucks. A three-dimensional quota is used for characterizing the variabilities in delivery operation. It also overcomes the challenges involved, including the inside workload, and the yard closing deadline. A non-dominated genetic algorithm II-based approach is developed to solve the problem. The method is validated through extensive numerical experiments, and the results show the advantages of simultaneously balancing the workload of yard cranes instead of merely smoothing truck arrivals. Parameter analysis then reveals the influence of both larger vessel size and an increase in trade, as well as the utility ratio of quotas.

Accurate truck arrival prediction is complex but critical for container terminals. A deep learning model combining Gated Recurrent Unit (GRU) and Fully Connected Neural Network (FCNN), is proposed to predict daily truck arrivals using fusion technology. The model can efficiently analyze sequence and cross-section data sets. The new feature in the new model lies in that it, for the first time, incorporates the new parameters influencing traffic volumes such as the vessel-related information, arrival weekdays, and weather conditions into the long-time series of truck arrivals. Furthermore, truck arrivals are predicted in three groups based on their movement purposes: pick-up, delivery, and dual. it also contributes to the literature in a sense that the performance of the model is tested using real big data from a world-leading container port in Southern China. The results generate insightful managerial implications for guiding port traffic management in a generic manner. It reveals the relation of export container arrivals with the Container Yard (CY) closing time of a specific vessel. It is demonstrated the proposed model outperforms the currently available methods with an improved accuracy rate of prediction by 23.44% (dual), 32.09% (pick-up), and 26.99% (delivery), respectively. As a result, the model can better reflect reality compared to the existing ones in the literature. It is also evident that the 3-categorized prediction model can significantly help increase prediction accuracy in comparison with the 2-categorized methods used in practice.

This study examines the evolving landscape of the shipping industry in the context of Maritime Autonomous Surface Ships (MASS), with a focus on the critical role of Maritime Education and Training (MET). As the sector undergoes rapid transformation, there is a pressing need for MET providers to adapt their curricula and training programs to meet emerging industry standards. Despite growing research interest in future skills and competencies for the MASS workforce, a comprehensive framework for assessing and ranking these skills remains lacking. To address this gap, we propose the application of multi-criteria decision-making (MCDM) techniques, specifically fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to evaluate and prioritise proficiency requirements for MASS. The analysis, based on the responses of 174 experts, yields consistent and robust results, identifying ‘Operational Skills’, ‘Digital Skills’, and ‘Maritime Competency’ as the most crucial skills and competencies for MASS operations. A number of insights and recommendations are provided to guide MET institutions in updating their educational offerings to meet the demands of the evolving maritime industry.

As the key member states of the RCEP, China and Thailand will achieve more stable economic cooperation. The further prosperity of the Sino-Thai bilateral trade provides an opportunity for the development of multimodal transport between them. Since selecting a suitable multimodal transport route for the bilateral trade is important, this paper establishes a model based on Analytic Hierarchy Process (AHP) and Zero-One Goal Programming (ZOGP) for evaluating and selecting the multimodal transport route between China and Thailand by taking cost, time and risk as affecting factors into account. A numerical analysis by taking Thailand rubber export to China as an example has been done. The results show that the weight of transportation cost is the largest, followed by the transportation risk. By evaluating the seven typical routes from Songkhla (Thailand) to Qingdao (China), an optimal route is found, which proves the effectiveness and rationality of the modeling, and provides decision basis for decision makers.