A Multi-Constraint Planning Approach for Offshore Test Tasks for an Intelligent Technology Test Ship

A hierarchical population task planning method is presented to enhance the test efficiency and reliability of intelligent technology test ships under various tasks and complex limitations. Firstly, a mathematical model of the vehicle path problem for multi-voyage vessel testing is developed, which aims to minimize the ship's fixed and fuel costs, taking into account the energy and space constraints of an intelligent technology test vessel, as well as practical factors such as the dependencies and temporal relationships between test tasks. Second, to fairly minimize constraint complexity in the planning process, an offshore test task planning architecture based on the concept of hierarchical population is explored and built. This architecture separates task planning into four levels and allocates the tasks to distinct populations. Using this information, a grouping genetic algorithm is suggested based on the characteristics of the population. This algorithm uses a unique coding method to represent task clusters and narrows the range of possible solutions. The issue of the conventional grouping genetic algorithm's vast search space is resolved. Lastly, simulation verification is carried out, and the results show that the method can effectively solve the problem of offshore test task planning for intelligent technology test ships under multi-constraint conditions. It reduces test cost and improves test efficiency.