Ship collision accidents not only endanger the safety of ships and personnel, but also may cause serious marine environmental pollution. To solve this problem, advanced technologies have been developed and applied in the field of intelligent ships in recent years. In this paper, a novel path planning algorithm is proposed based on particle swarm optimization (PSO) to construct a decision-making system for ship's autonomous collision avoidance using the process analysis which combines with the ship encounter situation and the decision-making method based on ship collision avoidance responsibility. This algorithm is designed to avoid both static and dynamic obstacles by judging the collision risk considering bad weather conditions by using BP neural network. When the two ships enter a certain distance, the optimal collision avoidance course and speed of the ship are obtained through the improved collision avoidance decision-making method. Finally, through MATLAB and Visual C++ platform simulations, the results show that the ship collision avoidance decision-making scheme can obtain reasonable optimal collision avoidance speed and course, which can ensure the safety of ship path planning and reduce energy consumption.

With the continuous development of science and technology, unmanned ship has gradually become a hot spot in the field of marine research. In practical applications, unmanned ships need to have long-range navigation and high efficiency, so that they can accurately perform tasks in the marine environment. As one of the key technologies of unmanned ship, path planning is of great significance to improve the endurance of unmanned ship. In order to meet the requirements, this paper proposes a path planning method for long distance unmanned ships based on reinforcement learning angle precedence ant colony improvement algorithm. Firstly, canny operator is used to automatically extract navigation environment information, and then MAKLINK graph theory is applied for environment modelling. Finally, the basic ant colony algorithm is improved and applied to the path planning of unmanned ship to generate an optimal path. The experimental results show that, compared with the traditional ant colony algorithm, the path planning method based on the improved ant colony algorithm can achieve a voyage duration of nearly 7 km for unmanned ships under the same sailing environment, which has certain practicability and popularization value.

This study developed an optimization model, defined as the IPS (Itinerary for Passenger Satisfaction), for a cruise ship to identify an itinerary that maximizes passenger satisfaction. A 0-1 integer programming model was developed to provide an optimal sequence of ports of call, assigning a destination to each day of the cruise. The concepts of the destination access network and the neighborhood of a destination were designed and manipulated to organize the complex network of destinations so that each next destination is selected within a practical overnight sail. The developed model can also be viewed as a reduced variant of the traveling salesperson problem with less constraints. A set of example tests shows that practical scenarios of the IPS with moderate cruise duration can be easily solved with light computation loads. Considering cruise ship passengers usually make their decisions not relying on only one destination but on an itinerary in its entirety, the purpose of this study was to identify itinerary alternatives to attract potential cruise passengers for attaining maximum occupancy level.

최근 컨테이너 선박은 조선 기술의 발달과 세계 경제교역 규모의 급속한 증가에 따라 대형화, 고속화가 빠르게 진행되고 있다. 이에 머지않아 15,000TEU급 컨테이너 선박의 등장이 예상되고 있다. 동북아시아에서는 경제의 글로벌화에 따른 급격한 물류환경 변화에 대응하여 물류 주도권 확보를 위한 물류거점화 경쟁이 치열하게 전개되고 있다. 이에 따라 주요항만에서는 대형 선사를 유치하기 위한 치열한 경쟁을 하고 있다. 이를 위해 본선작업을 신속하고 정확하게 처리하기 위한 노력이 절실히 필요하며 지금까지의 본선계획 시스템은 획기적으로 개선되어야 할 것이다. 그리하여 본 논문에서는 이러한 기존 본선작업의 개선을 위하여 다수의 플래너를 이용한 양?적하 작업계획을 수행하여 보다 신속하고 정확하게 하며, 플래너(Planner)간의 정보공유로 작업계획의 품질을 보다 향상시킬 수 있는 다중분산 본선작업 계획 시스템을 제안한다. 그리고 이의 효율성을 검증하기 위해 아레나(ARENA)를 통한 시뮬레이션을 수행하고자 한다.

컨테이너 터미널에서는 효율적인 선적 작업을 위해 선박이 입항하기 전 선박정보를 통해 선적 계획을 수립한다. 하지만 실제 선박이 터미널에 입항하여 안벽에 접안후의 실제 적재 상태가 계획 시점에서의 적재상태와 다르거나, 사전에 수립된 계획이 실행 불가능한 경우 등의 상황이 발생한다. 이에 따라 기존에 수립했던 계획을 즉각적으로 재수정 해야 하며, 이를 위한 해법의 연구가 필요하게 되었다. 이점에 착안하여 본 연구에서는 위에서 언급한 예기치 못한 상황에 대해 정리하고, 상황에 맞는 합리적이고 즉각적으로 활용이 가능한 실시간 선적계획 모델을 제시하였다.

This paper presents a decision making model for the cruise ship management. A network based optimization model has been developed for a single cruise ship operation. It gives optimal itinerary patterns over the planning period for the cruise ship managers wanting to maximize profit from the cruise ship operation. A network solution method to find the optimal solution is also developed. This network model can be equivalently transformed into a linear programming model, which makes the implementation of the model quite practical however complicated the given set of possible itineraries may be. The ship scheduling network developed in this study can also be used as a general framework to describe all possible cruise ship itineraries the cruise ship manager can figure out.

The purpose of this paper is to describe the design of the decision support system for container terminal ship planning and to introduce the implemented system. The ship planning in container terminals consists of three major decision processes -the working schedule of gantry cranes the discharging sequence of inbound containers the loading position and sequence of outbound containers. For making these decision the proposed system can provide two ship planning modes the interactive planning mode with user-friendly GUI and the automated planning made. To implement the automated planning routine we acquired the planning rules from the expert planner in container terminals and developed an expert system based on the rules. Finally we evaluated the system developed and the potential for commercialization by using container terminal data.