基于量子回火的路线优化,用于大型物流仓库中的商业AGV操作系统
Thinh Nguyen Quang1,2, Kosuke Matsuyama3, Keisuke Shimizu3
1Smart Business Solutions Division, Sharp Corporation, Nara, Japan. nguyenquang.thinh@mail.sharp.
Scientific reports
|December 2, 2025
概括
本研究介绍了用于优化仓库中自动引导车辆 (AGV) 路由的量子化. 新方法通过减少大型AGV车队的计算复杂性来提高安全性和效率.
科学领域:
- 物流和供应链管理的物流和供应链管理.
- 量子计算应用 量子计算应用
- 运营研究 运营研究
背景情况:
- 自动引导车辆 (AGV) 的高效路由对于大型物流运营至关重要,特别是在高密度环境中.
- 现有的方法与实时路由,安全和可靠性的复杂性作斗争,对于广泛的AGV车队而言.
- 随着仓库自动化规模和复杂性的增加,对先进的优化技术的需求正在增长.
研究的目的:
- 开发一种新的方法来优化使用量子化在大型物流仓库中的AGV路由.
- 通过实时优先级的改进成本函数来提高路由性能和运营安全.
- 为了减少计算复杂性,并实现可扩展的,大型AGV车队的安全控制.
主要方法:
- 实施了一个增强的成本函数,其中包含了AGV路由的实时优先级因素.
- 开发一种安全且计算效率高的候选路线生成方法.
- 优化问题集群的应用,将大型问题分解成更小,更易于管理的子问题.
- 使用量子回火机进行验证,与经典和量子启发的解决方案进行基准测试.
- 与商业 AGV 操作系统 (AOS) 集成,以实现无连接.
主要成果:
- 拟议的路线生成方法在小型/中型系统中降低了高达96%的优化变量,在大型系统中降低了78%,改善了路线质量.
- 优化问题聚类将最大问题大小减少到不到10,000个变量,从而实现可扩展的AGV车队控制.
- 基于复杂性的问题制定使得采样时间比传统的基于大小的方法减少了大约28.2%.
- 用1000台AGV进行的模拟显示了效率和安全的显著改善.
结论:
- 量子化对于管理大规模的AGV操作系统来说几乎是可行的.
- 提出的方法提供了一个可扩展和安全的解决方案,用于在现实世界仓库环境中进行先进的物流优化.
- 这项研究为更高效,更可靠的自动化物流运营铺平了道路.
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