Creating Robust Train Unit Shunting Plans using Probabilistic Programming
G.B.J. van Zwienen (TU Delft - Electrical Engineering, Mathematics and Computer Science)
S. Dumancic – Mentor (TU Delft - Algorithmics)
R.J. Gardos Reid – Mentor (TU Delft - Algorithmics)
I.K. Hanou – Mentor (TU Delft - Algorithmics)
N.M. Gürel – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Train Unit Shunting is a complex process that directs trains through a shunting yard. In real-world railway operations, disturbances are common, requiring shunting schedules to be robust against uncertainties such as delays. Previous research has proposed algorithms for the Train Unit Shunting Problem (TUSP) and one study attempted to create robust shunting plans by defining a probabilistic model of the uncertainties involved and inferring a distribution of robust solutions for the TUSP. Following this approach, this paper investigates the use of probabilistic programming in increasing robustness of shunting plans using an advanced TUSP solver. This research develops a model for uncertain shunting scenarios, solves these scenarios, and applies importance sampling to infer the posterior distribution, producing a distribution of robust shunting plans instead of a single plan. The paper presents examples demonstrating that it is beneficial to use one of the output robust plans over the plan made for the deterministic scenario, revealing the potential of integrating probabilistic programming techniques into the planning process to improve railway efficiency and reduce delays.