深度强化学习用于解决车辆路由问题与回程.
IEEE transactions on neural networks and learning systems
|March 29, 2024
概括
本研究引入了一种新的深度强化学习 (DRL) 方法,用于解决车辆路由问题. 拟议的神经启发式有效地解决了VRPB变体,在物流优化中展示了强大的性能和概括能力.
科学领域:
- 运营研究 运营研究
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 车辆回程路由问题 (VRPB) 是一个复杂的物流挑战,具有重要的现实应用.
- 现有的方法经常与VRPB变体的双重交付和接收要求作斗争.
研究的目的:
- 开发一种用于解决传统和改进的VRPB变体的新型神经启发式.
- 为了利用深度强化学习 (DRL) 在物流中有效地构建路线.
主要方法:
- 制定VRPB作为马尔科夫决策过程 (MDP).
- 在DRL政策网络中设计一个基于注意力的双阶段编码器.
- 采用自我注意力和异质注意力机制,以改善节点表示.
主要成果:
- 拟议的神经启发术的性能优于传统和现有的神经启发术基线.
- 在随机生成的VRPB实例和基准VRPB实例上都表现出良好的表现.
- 该模型在不同的问题大小和分布上表现出强大的概括性.
结论:
- 基于DRL的神经启发式为VRPB提供了有效的解决方案.
- 基于注意力的编码器显著提高了路由解决方案的质量.
- 这种方法对实际的物流优化和规划充满希望.
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