Heuristic, Hybrid, and LLM-Assisted Heuristics for Container Yard Strategies Under Incomplete Information: A Simulation-Based Comparison

Abstract

Efficient container stacking is a critical factor for the performance of intermodal terminals. This study evaluates how classical, hybrid, and LLM-assisted heuristic stacking strategies perform when terminals operate under incomplete or uncertain schedule information. A simulation model of a 4 × 5 × 3 yard was developed, comparing three strategies: a layer-based rule (LAY), a hybrid heuristic (SVD), and an adaptive heuristic supported by a large language model (ChatGPT-4), rather than a full ML/RL model. Each scenario (0%, 25%, 50%, and 100% schedule visibility) was repeated 10 times with controlled random seeds. Results show that under full schedule information, the LLM-assisted strategy reduced relocations by up to 35% and crane operating time by 28% compared to deterministic methods. However, its performance degraded with partial visibility, sometimes falling behind the hybrid strategy, which remained more stable across scenarios. Standard deviations confirmed that differences between methods were statistically significant. The findings highlight both the potential and the limitations of LLM-assisted heuristics: they can outperform classical approaches in data-rich environments but may overreact to incomplete inputs without explicit data quality assessment. This study should therefore be regarded as a simulation-based proof-of-concept, with further validation on real operational data required to confirm its applicability.