Genetic Algorithm Optimization in Ship Rapid Loading Planning

With the vigorous development of the global maritime industry, rapid ship loading planning is of great significance for improving transportation efficiency and reducing costs. However, traditional loading planning methods often find it difficult to achieve optimization in the face of large-scale and complex tasks. In order to improve the planning effectiveness of ship rapid loading planning, this study uses simulated annealing algorithm to improve genetic algorithm and obtain optimized algorithm, which is applied to the ship rapid loading planning model. The algorithm comparison results showed that compared with the comparison algorithm, the loss value and prediction fitting coefficient of the optimized genetic algorithm were 0.003 and 0.9632, respectively, which were better than the comparison algorithm. In addition, in the empirical analysis of optimizing genetic algorithms, it was found that the minimum and maximum planning satisfaction rates of SA-GA algorithm were 82.3% and 87.2%, respectively, which were superior to the comparative algorithm. Results indicate that the optimized genetic algorithm has good planning performance in ship rapid loading planning and has good application prospects. This study provides new solutions and methods for optimization problems in the field of ship transportation.