This study evaluates the performance of a quantum-classical metaheuristic and a traditional classical mathematical programming solver, applied to two mathematical optimization models for an industry-relevant scheduling problem with autonomous guided vehicles (AGVs). The two models are: (1) a time-indexed mixed-integer linear program, and (2) a novel binary optimization problem with linear and quadratic constraints and a linear objective. Our experiments indicate that optimization methods are very susceptible to modeling techniques and different solvers require dedicated methods. We show in this work that quantum-classical metaheuristics can benefit from a new way of modeling mathematical optimization problems. Additionally, we present a detailed performance comparison of the two solution methods for each optimization model.
@article{arxiv.2507.21701,
title = {Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics},
author = {Florian Krellner and Abhishek Awasthi and Nico Kraus and Sarah Braun and Michael Poppel and Daniel Porawski},
journal= {arXiv preprint arXiv:2507.21701},
year = {2025}
}
Comments
To be published in the proceedings of the 2025 IEEE International Conference on Quantum Computing and Engineering (QCE)