Solving rescheduling problems in heterogeneous urban railway networks using hybrid quantum-classical approach

TL;DR

This paper explores the use of a hybrid quantum-classical heuristic to efficiently solve complex railway rescheduling problems in real-world urban networks, demonstrating practical viability and benefits.

Contribution

It introduces a hybrid quantum-classical approach for railway rescheduling and compares its performance with classical solvers on a real urban network.

Findings

Hybrid solver provides acceptable, timely solutions in realistic scenarios.

Quantum-classical approach yields feasible, near-optimal solutions.

Method demonstrates readiness for practical railway management applications.

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 are 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.