A Genetic Algorithm Based Dynamic Berth Allocation Strategy for Container Terminals

The container terminal serves as a significant node in a global supply chain. This paper investigates the berth and facility allocation issues from a systematic and integrated viewpoint, and provides a novel method to approach the terminal resource allocation problem. The authors introduce a dynamic berth allocation model and a genetic algorithm (GA) based heuristic method that improves the existing research on static berth allocation models. The GA-based approach, together with two encoding methods, is developed to tackle the non-linear integer programming problems. In particular, it explores detailed capabilities of a complex problem solution using these two methods. We apply the models to a case study on Shanghai Waigaoqiao Container Terminal Project Phase II. The optimized resource allocation results are compared with current situations, and demonstrated that this model application could significantly improve the operational efficiency and thereby lower operational costs.