Scheduling of yard cranes and multi-load automated guided vehicles leveraging container substitutability

High-volume containers, which arise from extensive trade activities, often share identical attributes, thereby enabling their substitutability. Such substitutability enhances the optimization potential of scheduling yard cranes (YCs) and automated guided vehicles (AGVs) by allowing for the efficient handling of containers with the same size, weight, and destination. This study proposes a novel scheduling method to minimize handling time and reduce delays by optimizing the sequence of containers with identical attributes. Mixed-integer programming models are formulated to address the scheduling problem of non-cantilevered YCs and multi-load AGVs in a perpendicular automated container terminal. Subsequently, we designed a hybrid precise-heuristic algorithm. The YC scheduling and slot allocation models are solved by commercial software to obtain the optimal solution. Additionally, an adaptive variable neighborhood multi-population genetic algorithm is developed to generate high-quality satisfactory solutions for AGV scheduling within a feasible time frame. The results indicate that leveraging container substitutability during loading and unloading processes can effectively reduce the total handling cost by up to 8.9%.