Truck Service Optimisation in Port Areas by Comparing Genetic Algorithm and Cuckoo Search Algorithm

Efficient truck service optimization in port areas is essential for minimizing congestion, reducing delays and improving overall logistics efficiency. This study presents a comparative analysis of Genetic Algorithm (GA) and Cuckoo Search Algorithm (CSA) for optimizing truck scheduling and resource allocation in dynamic port environments. GA explores a broad solution space using evolutionary operators, while CSA leverages Lévy flight-based search mechanisms to enhance solution quality and escape local optima. The comparison evaluates both algorithms based on key performance metrics, including waiting times and server utilization. Simulation results indicate that the GA produces smaller waiting times, while the CSA exhibits higher. Moreover, the server utilization of the GA is significantly lower than with the CSA. The findings highlight the strengths and limitations of each method, providing valuable insights into their applicability for real-time truck service optimization in smart port management systems.