GA-PSO solution for the joint scheduling problem of berths and gantry cranes at multi-purpose terminals considering scientific research

To address the joint scheduling of multi-purpose terminal berths and gantry cranes, this study prioritized scientific research vessels' berthing and enhanced the speed of vessel planning solutions to tackle the complexity of unpredictable research vessel arrivals. A multi-constraint linear programming model aimed at minimizing berthing time is developed, utilizing a GA-PSO hybrid algorithm that merges the effective iteration of Genetic Algorithm with the swift optimization of Particle Swarm Optimization. The algorithm's feasibility and effectiveness are tested against known-case scenarios and applied to a real-world case at Terminal N. Comparative analysis demonstrates the PSO-GA hybrid's superiority over standalone GA and PSO algorithms, showing significant improvements of 12.16%, 5.07%, and 10.32% in large, medium, and small instances, respectively, highlighting its practicality and efficiency in solving joint scheduling problems.