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

2026-08-07

AI Summary

This study addresses the limitations of existing simulation models, which rely on Poisson arrival assumptions and aggregate throughput data and thus fail to accurately capture vessel waiting times and yard occupancy at transshipment hubs. To overcome this, the authors propose a three-stage vessel scheduling generator that operates solely on publicly available port statistics. The approach first employs a two-layer Gamma distribution to jointly model periodic liner services and their operational disruptions. It then introduces GreedyDwellFit, a heuristic algorithm that efficiently matches transshipment cargo flows to connecting voyages using a gravity model. This work is the first to enable joint modeling of structured weekly liner schedules and stochastic disturbances without proprietary data. Empirical validation at the Busan and Singapore hubs demonstrates prediction errors below 1% for both throughput and vessel arrival frequency, substantially improving terminal performance forecasting accuracy.

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.

Problem

• transshipment hub simulation
• vessel schedules
• Poisson arrivals
• cargo pairing
• terminal performance

Innovation

• Gamma process
• GreedyDwellFit
• transshipment hub simulation
• liner schedule modeling
• gravity-based cargo allocation