AI 中文总结
研究作业车间调度问题的工业变体，在三个平台上用多种方法求解，通过定制公式实现硬件-软件协同设计，经基准测试表明量子和量子启发优化能支持工业求解器相关工作及改进调度近似值。
AI 中文摘要
量子处理单元有望加速解决包括组合优化在内的特定计算问题，但其工业实用性仍是一个开放挑战。我们在三个平台（IBM量子、D-Wave量子退火器和富士通数字退火器）上使用量子、量子启发和经典方法研究作业车间调度问题的工业变体。通过针对硬件特定约束定制公式，表明硬件-软件协同设计对解决方案质量和可扩展性至关重要。我们将所有方法与精确的经典求解器和MILP公式进行基准测试，评估运行时间、解决方案质量和可扩展性。结果表明，量子和量子启发优化可支持工业求解器选择、集成到经典工作流程、建模决策和早期概念验证开发，同时暗示了改进工业调度近似值的潜在途径。
英文摘要
Quantum Processing Units promise speed-ups for selected computational problems, including combinatorial optimisation, but their industrial utility remains an open challenge. We study an industrial variant of the Job-Shop Scheduling Problem using quantum, quantum-inspired, and classical methods across three platforms: IBM Quantum, the D-Wave Quantum Annealer, and the Fujitsu Digital Annealer. By tailoring formulations to hardware-specific constraints, we show that hardware-software co-design is essential for solution quality and scalability. We benchmark all approaches against an exact classical solver and a MILP formulation, evaluating runtime, solution quality, and scalability. Our results indicate that quantum and quantum-inspired optimisation can support industrial solver selection, integration in classical workflows, modelling decisions, and early proof-of-concept development, while suggesting a potential path towards improved approximations for industrial scheduling.
