Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics
Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Krellner, Florian, Awasthi, Abhishek, Kraus, Nico, Braun, Sarah, Poppel, Michael, Porawski, Daniel
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6910752198
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