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arXiv · 2507.21701

Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics

Florian Krellner·Abhishek Awasthi·Nico Kraus·Sarah Braun·Michael Poppel·Daniel Porawski
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Abstract

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.

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Florian Krellner, Abhishek Awasthi, Nico Kraus, Sarah Braun, Michael Poppel, Daniel Porawski. 2025-07-29. Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics. https://arxiv.org/abs/2507.21701

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