# Optimising Rolling Stock Planning including Maintenance with Constraint Programming and Quantum Annealing

arXiv: https://arxiv.org/abs/2109.07212 · HTML: https://mlsanity.com/paper/2109.07212/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Quadratic and Higher-Order Unconstrained Binary Optimization of Railway Rescheduling for Quantum Computing](https://mlsanity.com/paper/2107.03234/) (2021)
- [Quantum computing approach to railway dispatching and conflict management optimization on single-track railway lines](https://mlsanity.com/paper/2010.08227/) (2020)

## Built on by (most cited first, 1 of 1)

- [Scalable Quantum-Inspired Optimization through Dynamic Qubit Compression](https://mlsanity.com/paper/2412.18571/) (2024)

## Abstract (arXiv)

We propose and compare Constraint Programming (CP) and Quantum Annealing (QA) approaches for rolling stock assignment optimisation considering necessary maintenance tasks. In the CP approach, we model the problem with an Alldifferent constraint, extensions of the Element constraint, and logical implications, among others. For the QA approach, we develop a quadratic unconstrained binary optimisation (QUBO) model. For evaluation, we use data sets based on real data from Deutsche Bahn and run the QA approach on real quantum computers from D-Wave. Classical computers are used to evaluate the CP approach as well as tabu search for the QUBO model. At the current development stage of the physical quantum annealers, we find that both approaches tend to produce comparable results.