Optimizing Container Movement: A Comparative Study of Genetic Algorithms and Ant Colony Optimization in Port Logistics

The systematic relocation of containers from quay cranes to storage regions is an integral part of port interactions that significantly affects the efficacy and cost improvement of the port tasks. “The Role of Optimization in the Automated Scheduling of Transportation Resources” analyzes optimization mechanisms for scheduling and routing container movement. This complicated problem requires several resources, including quay cranes, yard trucks, and reach stackers. We then propose the Quay Crane-Yard Truck Scheduling Problem (QCYTSP) to minimize the overall transfer time, idle times, and energy consumption, and satisfy the operational constraints. We employ two metaheuristics (Genetic Algorithm (GA) and Ant Colony Optimization (ACO)) and compare their performance. These results showcase both the strengths and limitations of each method, as GA has rapid convergence and is specialized for static large-scale problems, in contrast to A CO, which is superior in its adaptability to dynamic conditions and changes in the situation. The study's authors suggest that combining these approaches will further improve the efficiency and flexibility of container terminal operations. This study lays the groundwork for creating hybrid models that maximize planning and real-time.