Optimising Rolling Stock Planning including Maintenance with Constraint Programming and Quantum Annealing
This work addresses a domain-specific optimization problem for railway operators, but it is incremental as it compares existing methods without achieving clear superiority.
The paper tackled the problem of optimizing rolling stock assignment with maintenance tasks using Constraint Programming and Quantum Annealing, finding that both approaches produced comparable results on real data from Deutsche Bahn.
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.