Two-Level Genetic Algorithm for Integrated Equipment Scheduling and Optimization in Container Terminals

The rapid growth of international trade has increased the importance of efficient operations in automated container terminals (ACTs). This study addresses equipment management in automated container terminals. The problem is divided into scheduling and routing of multiple Automated Guided Vehicles (AGVs) handling simultaneous inbound and outbound container tasks to minimize vessel berthing time and enhance terminal efficiency. The problem is formulated as an Integer Linear Programming (ILP) model, and a simplex network approach is adopted to represent the complexity of container-handling tasks, with start and end nodes and travel and handling times represented on network edges. To solve the problem, an integrated two-level genetic algorithm is developed to determine optimal AGV paths and task sequences. Simulation results demonstrate that the proposed two-level genetic algorithm effectively improves terminal throughput, reduces operational time, and enhances energy efficiency, while accounting for stochastic container arrivals. This work highlights the novel application of a two-level genetic optimization for coordinated AGV management in ACTs.