Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916868675076096 |
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| author | Krellner, Florian Awasthi, Abhishek Kraus, Nico Braun, Sarah Poppel, Michael Porawski, Daniel |
| author_facet | Krellner, Florian Awasthi, Abhishek Kraus, Nico Braun, Sarah Poppel, Michael Porawski, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics Krellner, Florian Awasthi, Abhishek Kraus, Nico Braun, Sarah Poppel, Michael Porawski, Daniel Quantum Physics Optimization and Control 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. |
| title | Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics |
| topic | Quantum Physics Optimization and Control |
| url | https://arxiv.org/abs/2507.21701 |