Speaker
Description
Sea transport handles more than 90% of global trade, with over 15% relying on container shipment. Consequently, container transport plays a crucial role in global trade. Containers await loading onto ships in container yards, where limited capacity often leads to stacking containers on top of each other. The sequence for loading these containers onto ships is usually unknown, making it impossible to prearrange them to avoid relocations when loading them. The Container Relocation Problem (CRP) is used to find the sequence for relocating containers to fulfill a defined order for loading while optimizing various criteria. Because CRP belongs to NP-hard problems, exact solutions to CRP are typically unattainable, which is why heuristic approaches are mainly used. Relocation Rules (RRs) are straightforward heuristic methods for CRP, offering speed and simplicity. However, creating RRs is usually a trial-and-error process for which domain expertise is needed. In this presentation, Genetic Programming (GP) will be used to automate this process and mitigate this challenge. Achieved results showed that GP-evolved RRs outperform manually designed rules and have strong generalization ability, making this approach a good choice for solving CRP.
