From Annual Throughput to Vessel Schedules: A Stochastic Generator for Transshipment Hub Simulation

Abstract

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.