Hybrid GA-ACO Algorithm for Optimizing Transportation Path of Port Container Cargo

Abstract

With the advancement of port transportation, optimizing the transportation path of container cargo has become a crucial consideration for logistics transportation companies. In this paper, a path optimization model was established for transporting container cargo from the yard to the customer, considering the customer's time window and aiming to minimize the total cost. A genetic algorithm-ant colony optimization (GA-ACO) algorithm was then devised to solve the model, and a case was analyzed to verify the effectiveness of this approach. It was found that the total cost of the path obtained by the GA-ACO algorithm was significantly lower than that of the GA and ACO individually (8.63% and 12.96%), reaching 7,458,268 yuan. Moreover, it used fewer vehicles. It suggested that the GA-ACO algorithm yielded a more efficient result. An analysis of different task quantities revealed that as the number of tasks increased, logistics transportation enterprises achieved higher vehicle utilization rates and better economic efficiency in completing container cargo transportation. These findings validate the reliability of the GA-ACO algorithm, affirming its applicability in real-world optimization of port container cargo transportation paths.