ANticipatory Train Optimization with Intelligent maNagEment
|Duration||January 2020 – January 2024|
|Key areas||Transportation Engineering, DSS, Freight Trains Operation, Machine Learning|
ANTOINE is a data analytics platform & decision support tool that will empower the operational & tactical decisions of intermodal freight activities. Thanks to a Machine Learning approach, combined with stochastic optimisation & simulation, the tool will serve as a cockpit by forecasting operational issues, and identifying robust solutions to handle disruptions & stochasticity stemming from train delays, wagon breakdowns, etc. In the long run, by integrating rail processes with maintenance operations and costs, ANTOINE allows the reduction of overall system costs. The tool will be implemented in real life within the CFL freight activities, and especially on their intermodal terminal operations. Its performance will be evaluated and compared to the currently used heuristics. ANTOINE will contribute to the process of digitalization of CFL intermodal and to the country’s Smart Logistics specialization strategy priority.
List of publications
- Zheng, Wei, and William McDonald. 2021. “Understanding Intermodal Operations Reliability.” University of Luxembourg.
- Pineda-Jaramillo J, McDonald W, Zheng W, Viti F. 2021. Exploring significant predictors of freight rail intermodal operation delays using causal machine learning. In 2nd International Workshop on “Artificial Intelligence for RAILwayS” (AI4RAILS) 2021. Athens, Greece
- Bigi F, Bosi T, Pineda-Jaramillo J, D’Ariano A, Viti F. A dynamic choice methodology for shunting policies in freight train operations. 2021. In International Conference on Optimization and Decision Sciences (ODS2021). Rome, Italy
- Bigi, F. 2021. ANticipatory Train Optimization with Intelligent maNagEment (ANTOINE): Mathematical models and simulation methods for the train unit shunting problem – Railway Optimization Workshop, Rome, Italy
- Pineda-Jaramillo J, McDonald W, Zheng W, Viti F. 2022. Identifying the major causes associated to rail intermodal operation disruptions using causal machine learning. Accepted in Transportation Research Board (TRB) 101st Annual Meeting. Washington DC, USA.
- Bigi F., Bosi T., Pineda Jaramillo J., D’Ariano A., Viti F. 2022. The Wagon Assignment Policy problem: Policy Comparison on the Wagon Fleet optimization. European Association for Research in Transportation (hEART). 2-4 June 2022, Leuven, Belgium.
- Pineda Jaramillo J., Bigi F., Viti F. 2022. A data-driven model for short-term prediction of arrival delay times in freight rail operations. Presented at TRISTAN IX, June 2022, Mauritius Island.
- Bigi F., Bosi T., Pineda Jaramillo J., Viti F., D’Ariano A. 2023. Addressing the impact of maintenance in shunting operations through shunt-in policies for freight train operations. Transportation Research Board (TRB) 102nd Annual Meeting, Washington DC, USA
- Pineda Jaramillo J., Viti F. 2023. Identifying the rail operating features associated to intermodal freight rail operation delays. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2022.103993
- Pineda Jaramillo J., Bigi F., Bosi T., Viti F., D’Ariano A. 2023. Short-term arrival delay time prediction in freight rail operations using data-driven models. IEEE Access. 10.1109/ACCESS.2023.3275022
- Pineda Jaramillo J., Viti F. 2023. MLOps in freight rail operations. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2023.106222