Maritime Autonomous Surface Ships (MASS) have been an important direction for the development of intelligent shipping. However, most current international research on MASS focuses on navigation assistance technologies such as perception and decision-making, ignoring MASS’s traffic organization and management. The traffic organization service (TOS) under the e-Navigation strategy also has not researched MASS. In this paper, we propose the notion of on-demand service to MASS with different degrees of autonomy (DoA) and develop a new maritime service (MS) applicable to the MASS with various (DoA) following the e-Navigation technical architecture. We first analyze MASS requirements with different degrees of autonomy in traffic organization to define the service information. Then, based on the traditional TOS, we developed the MASS traffic organization service (MTOS), consisting of an operational architecture, five subsystems, and four services. In particular, we proposed a phased service trigger mechanism to solve the problem of publishing untimely and redundant service information. Tianjin port and Huanghua port were selected as cases study for simulation experiments; the study finding revealed that MTOS could provide standardized, accurate, and efficient traffic organization service for MASS with different degrees of autonomy on demand. The contribution can be applied in the port operation to improve traffic safety.

In the existing literature, the berthing operations, the quay crane assignments, and the scheduling problems were usually conducted without considering the worker performances (WPs) and the yard truck tasks (YTTs). However, professionals in situ corroborate the crucial effect of WPs and the yard YTTs on quay crane performance and efficiency. This study introduced a new feasible model for investigating the berth and the quay crane assignments based on the scheduling problem, including worker performances and yard truck deployment constraints. First, a mixed-integer programming (MIP) model is implemented to reduce the vessel’s departure time. Then, a particle swarm optimization (PSO) algorithm is introduced to solve the problems. The Dar es Salaam port is selected as a case study to test the proposed model with a real-time dataset that was collected from a multinational company managing container terminals. The results show the efficiency and the accuracy of the proposed model. The $PSO$ algorithm is 86% and 62% more time-saving than $MILP$ and $T^{2}S$ solutions for a small number of containers, respectively. Additionally, the $PSO$ solution is 73% and 53% time-saving for a medium number of containers than $MILP$ and $T^{2}S$ models, respectively. Finally, the present study proposes consideration of the worker assignment and the yard truck deployment during the planning phase.