通过强化学习解决可扩展多代理路由问题
概括
RouteMaker有效地解决了使用新型图形神经网络的多个仓库复杂的多代理路由问题. 它大大降低了成本,提高了大型物流和运输挑战的速度.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 多代理路由问题 (MARP) 在物流和运输中至关重要.
- 可扩展性挑战源于MARP中搜索空间的指数级增长.
- 现有的方法与涉及多个专用仓库的MARP斗争.
研究的目的:
- 介绍RouteMaker,这是一个用于多个专用仓库的多代理路由问题的新系统.
- 为代理人制定有效的位置分配和路径规划策略.
- 允许对大规模和现实世界的场景进行概括,而无需微调.
主要方法:
- 利用基于角色交互的图形神经网络 (RIGNN) 来进行位置分配.
- 集成一个先进的规划器来优化代理旅行路径.
- 训练模型在小规模问题上进行概括.
主要成果:
- 路线制造商产生与启发式基线相比或优于之的最佳解决方案.
- 与ORTools相比,实现了显著的速度改进 (超过600倍) 和成本降低 (超过88%).
- 向大规模 (40个代理,1000个位置) 和现实世界的问题展示无的概括.
结论:
- RouteMaker为多个仓库的多代理路由问题提供了高效和高效的解决方案.
- 在复杂的路由任务上,RIGNN方法可以实现强大的概括和卓越的性能.
- 路由制造商在解决大规模,现实世界的路由挑战方面显著推进了最先进的技术.
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