Forecasting and optimization of rail planning using deep learning

In today’s logistics, rail transport progressively shows competitiveness in connecting deep-sea port with the hinterland. As an example, Port of Antwerp-Bruges - the second largest port in Europe – attempts to double the cargo throughput via this modality to 15% by 2030. Capability to carry large volume, various types of freight, along with high reliability and sustainability are making trains advantageous over traditional trucks.

The recent boom of freight railways transport has, however, put more pressure on both inland and deep-sea port infrastructure. In fact, many rail hubs at seaport are still operating sub-optimally due to the incapacity of maximizing their resources utilization. Subsequently, while some bundles are frequently overloaded, others remain rarely used for months. Certain long tracks are blocked to park single wagons during hours, causing shortage when long wagon compositions require service. Train path reservation slot remains fixed for all wagon moving tasks (e.g., 8 hours), resulting in struggles in this resource allocation. These shortcomings originate from the fact that the rail resource planning is being conducted in a first-come-first-serve, random-pick-up and manual fashion, without having insights of wagon flows in the near future. As a result, even owning large-scale rail resources, some ports still face unworthy shortage or serious delay, which finally adds overheads in total transport cost. Facing this, the optimization of resource allocation based on prediction of up-coming cargo flow will foster the rail infrastructure management, and thus the overall rail operation efficiency.

This PhD proposal researches a wide range of Machine Learning models to enhance the end-to-end visibility of wagon journey to the deep-sea port. These models forecast the complete path of wagon, from the moments when the long-haul trains are still hours before arrival. The most crucial stages include: arrival time at the main hub, service delay and service time at the shunting yard, train path to move wagon to bundle, public track and time slot to park each wagon, bundling dwelling (such as electric – diesel locomotive shift, which necessitates locomotive and its path allocation), and terminal service (loading/unloading). Next step, the insights learnt from these predictive indicators will be then act as outputs of Optimization phase, to propose the planning of rail tracks, train path, shunting yards and terminal slots which will avoid future shortage, mitigate idle time, and maximize the served cargo volume. Various optimization methods (traditional vs combinatorial neural, single vs multiple objective) will be benchmarked for better accuracy – computational complexity trade-off.