{"ID":2889147,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.21701","arxiv_id":"2507.21701","title":"Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.21701","url_pdf":"https://arxiv.org/pdf/2507.21701v1","authors":"[\"Florian Krellner\",\"Abhishek Awasthi\",\"Nico Kraus\",\"Sarah Braun\",\"Michael Poppel\",\"Daniel Porawski\"]","published":"2025-07-29T11:25:13Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"math.OC\"]","methods":"[]","has_code":false}