Enhancing online yard crane scheduling through a two-stage rollout memetic genetic programming

Abstract

Over the past decade, the surge in global container port throughput has heightened the demand for terminal efficiency, with the container yard operations being central to the overall port performance. However, the unpredictable arrival of external trucks poses significant challenges for yard cranes which must simultaneously schedule operations for both internal and external tasks. Traditional yard crane scheduling methods often rely on outdated assumptions that fail to account for the dynamic impact of external tasks. In response, container terminals increasingly model the yard crane scheduling as an online problem. A notable advancement in online scheduling is the online rollout method, which evaluates the decisions based on the potential outcomes of their future rollout schedules rather than immediate priorities. Although this method outperforms the previous approach, it faces two main issues: the rollout simulation is time consuming, and decisions based solely on objective value of rollout schedules may not align with long-term scheduling objectives. To overcome these limitations, we have developed a two-stage adaptive rollout decision model. In the first stage, less desirable tasks are dynamically filtered out to reduce the number of rollout simulations required, while the second stage employs a genetic programming evolved evaluation function to infuse more refined forward-looking insights into the scheduling process. This approach has proven to significantly enhance yard scheduling efficiency and performance, as confirmed by experimental validation. Given the dynamic nature of yard crane operations, we believe this method could be beneficially applied to other dynamic scheduling contexts.