Using PSO and GA to Optimize Schedule Reliability in Container Terminal

A schedule reliability problem (SRP) optimization model for dynamic berth allocation in container terminal (CT) is proposed. The model focuses on the minimum average schedule missed hours of ships between the ship schedule departure time and the actual departure time to enhance the schedule reliability (SR) of ships in CT, and the quay crane allocation is considered in this model. Particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed to optimize the berth allocation planning to improve the SR in CT. Both of PSO and GA perform well and make average schedule missed hours reduce 40%. Experimental results also show that PSO has faster convergence rate than GA in this case. The SRP model can also be used for related time windows problem optimization of the airport, railway station, bus station and other logistics industries.