Inferring Robust Plans with a Rail Network Simulator

Master thesis (2023)

Authors

R.J. Gardos Reid Electrical Engineering, Mathematics and Computer Science

Contributors

M.M. de Weerdt Algorithmics - EEMCS (supervisor 1)
S. Dumančić Algorithmics - EEMCS (supervisor 1)
I.K. Hanou Algorithmics - EEMCS (supervisor 1)
R.M.P. Goverde Transport and Planning - CEG (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Reuben Gardos Reid
More Info
expand_more

Published Date

21-07-2023

Language

English

Reuse Rights

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.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Over 700 trains in the Netherlands are used daily for passenger transportation. Train operations involve tasks like parking, recombination, cleaning, and maintenance, which take place in shunting yards. The train unit shunting problem (TUSP) is a complex planning problem made more difficult by uncertainties such as delays. Most existing approaches overlook these disturbances and the approaches that consider them incorporate heuristics to enhance the robustness of their solutions to disturbances. This thesis proposes an alternative approach: utilizing probabilistic programming to turn an existing planning algorithm and simulator into a generative model of the TUSP. The model introduces disturbances without the need to modify the planning algorithm or simulator. Through two types of inference, we infer a distribution of robust solutions for the TUSP. Empirical results demonstrate the effectiveness of our approach for inferring robust plans in small-scale scenarios.