Reducing Port Container Congestion with Reinforcement Learning: The Serial Mediation Role of Operational Learning Stability and Logistics Efficiency

Container congestion remains a persistent operational challenge in seaports because berth, yard, and gate processes are tightly coupled, demand is volatile, and control actions often operate under delayed feedback. Reinforcement learning (RL) is increasingly proposed for adaptive terminal decision support, yet the literature still says little about the mechanism through which RL may reduce congestion in practice. This study therefore develops a simulation-based mechanism framework in which RL improves congestion outcomes primarily by increasing Operational Learning Stability (OLStab), defined here as the consistency and governability of learning-enabled operational decisions under variability and disruption. A queueing-based, gate-focused terminal simulator is used as the data-generating process, with gate congestion treated as a reduced-form proxy for broader terminal congestion pressure. The statistical layer is interpreted cautiously as an internal mechanism consistency check within synthetic data rather than as empirical causal identification. Results show that RL is strongly associated with higher OLStab and that OLStab is the dominant pathway linking RL to lower congestion pressure in the simulated environment. Logistics Efficiency (LE) is directionally consistent with congestion reduction in bivariate analysis but adds limited incremental mediation once OLStab is jointly modeled. The theorized moderation by Decision Latency Sensitivity (DLS) is not robustly recovered within the examined latency range. Overall, the study contributes a more bounded explanation of how RL may reduce congestion in a designed gate-focused terminal control environment and highlights learning stability as a practical screening criterion for future digital twin and pilot deployment studies.