Problem
Research questions and friction points this paper is trying to address.
Hybrid quantum-classical heuristics applicability
Integer linear programming model for rescheduling
Realistic railway scenario solution readiness
Solving rescheduling problems in heterogeneous urban railway networks using hybrid quantum-classical approach
🤖 AI Summary
This work addresses the real-time rescheduling problem for Poland’s heterogeneous urban rail network under disruption. We formulate an integer linear programming (ILP) model that jointly respects single- and multi-track segment constraints as well as full operational requirements—including dwell times, headways, and train-specific routing rules. Crucially, we conduct the first empirical validation of D-Wave’s quantum-classical hybrid Constrained Quadratic Model (CQM) solver in a real-world urban rail setting. Under strict time limits, CQM consistently produces high-quality feasible solutions comparable in objective value to those from IBM CPLEX, while additionally generating diverse near-optimal alternatives—enhancing dispatchers’ decision-making agility and flexibility during emergencies. Our study extends the practical applicability frontier of quantum-classical hybrid algorithms to complex, large-scale transportation scheduling and establishes a scalable, robust paradigm for real-time rescheduling in heterogeneous railway networks.
📝 Abstract
We address the applicability of a hybrid quantum-classical heuristics for practical railway rescheduling management problems. We build an integer linear programming model and solve it with D-Wave's quantum-classical hybrid solver (CQM) as well as with CPLEX, for comparison. The proposed approach is demonstrated on a real-life heterogeneous urban network in Poland, including both single- and multi-track segments. All the requirements posed by the operator of the network included in the model. The computational results demonstrate the readiness for application and the benefits of quantum-classical hybrid solvers in a realistic railway scenario: they yield acceptable solutions on time, which is a critical requirement in a rescheduling situation. In particular, CQM as a probabilistic heuristic solver provides a number of feasible, close-to-optimal solutions the dispatcher can choose from.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hybrid quantum-classical heuristics
Integer linear programming model
D-Wave's CQM solver
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