AI 中文总结
该研究针对集装箱中转枢纽仿真的现有模型预测失真问题，提出基于公共港口数据校准的Gamma-GDF随机生成器，经两大超级枢纽验证精度良好，为港口仿真提供稳健基础。
AI 中文摘要
模拟集装箱中转枢纽需要反映周期性班轮班期和起讫点（OD）货物配对的船舶到港情况。现有依赖泊松（Poisson）到港和总体中转量的模型会严重扭曲等待时间和堆场占用率的预测。我们提出一种完全基于公共港口统计数据校准的三相班期生成器，无需专有数据。该框架引入两级伽马（Gamma）数学模型，平衡结构化周度服务与运营扰动。对于货物路由，我们开发了GreedyDwellFit（GDF），一种使用引力模型将中转批次配对到特定衔接服务的快速分配启发式算法。经釜山和新加坡超级枢纽验证，该模型复现吞吐量和靠泊频率的误差低于1%。结果表明，用该Gamma-GDF框架替代泊松模型可消除码头性能预测中的显著扭曲，为港口仿真提供稳健、可推广的基础。
英文摘要
Simulating container transshipment hubs requires vessel arrivals reflecting cyclical liner schedules and origin-destination (OD) cargo pairing. Existing models relying on Poisson arrivals and aggregate transshipment volumes severely distort waiting-time and yard-occupancy predictions. We propose a three-phase schedule generator, calibrated entirely from public port statistics, eliminating the need for proprietary data. The framework introduces a two-level Gamma mathematical model that balances structured weekly services with operational perturbations. For cargo routing, we develop GreedyDwellFit (GDF), a fast allocation heuristic to pair transshipment batches to specific connecting services using a gravity model. Validated against Busan and Singapore mega-hubs, the model reproduces throughput and call frequencies with under 1\% error. Our results show that replacing Poisson models with this Gamma-GDF framework eliminates significant distortions in terminal performance projections, offering a robust, generalisable foundation for port simulation.
