Intelligent Algorithm for Yard Crane Scheduling in Multiple Container Blocks

This paper presents an intelligent algorithm for scheduling multiple yard cranes in multiple container blocks. The algorithm aims to optimize crane movement and minimize container handling time. The proposed algorithm considers various factors such as crane capacity, container size, and block layout. It employs a combination of genetic algorithms and simulated annealing to find optimal solutions. The algorithm is evaluated through simulation experiments, demonstrating its effectiveness in reducing operational costs and improving efficiency. The results show that the proposed algorithm significantly outperforms traditional scheduling methods.