Informed simulation for dynamic path finding in human-operated container terminals

Path finding for human-operated vehicles in a maritime container terminal can be very demanding, especially when pursued under dynamic traffic conditions and based on decisions that are self-governing. Preventing collision and interference among internal transfer vehicles is a major task for operations managers in organizing container transfers from the quay to the yard at minimal congestion. To this matter, we propose a novel simulation framework as an effective tool for managing conflicting goals among straddle carrier (SC) drivers. The framework embeds both local and global search strategies that are used to determine the best path between any two vertices of the grid-like transfer network. Furthermore, it also allows to make path finding de-cisions based on either dynamic or static data. Numerical experiments prove the effectiveness of this informed simulation-based approach. Specifically, when critical extraordinary events arise and force, for instance, SCs to cover greater distances during container transfer, the difference in performance achieved by using the local/global path finding strategy in conjunction with static/ dynamic data becomes more significant.

Automated summary

Path finding for human-operated vehicles in a maritime container terminal can be challenging due to dynamic traffic conditions and self-governing decisions. Preventing collision and interference among internal transfer vehicles is crucial for minimizing congestion during container transfers. The proposed simulation framework combines local and global search strategies, allowing for path finding decisions based on dynamic or static data. Numerical experiments demonstrate the effectiveness of this approach, particularly in critical extraordinary events where the performance difference between strategies becomes more significant.