基于深度强化学习的低能耗调度方法设计城市电力物流车辆网络的设计
Pengfei Sun1,2,3, Jingbo He4, Jianxiong Wan4,5
1College of Data Science and Application, Inner Mongolia University of Technology, Hohhot, 010080, China. pfsun@imut.edu.cn.
Scientific reports
|March 16, 2025
概括
本研究引入了电动物流车 (ELV) 的新模型,以减少能源消耗. 该方法优化了路线和充电时间表,减少了至少1.64%的能源消耗.
科学领域:
- 物流和供应链管理的物流和供应链管理.
- 人工智能的人工智能
- 可持续的运输可持续的运输
背景情况:
- 物流业面临着日益增加的碳排放,需要可持续的解决方案.
- 电动物流车 (ELV) 提供了一条减少城市物流排放的途径,但由于电池和负载限制而面临挑战.
- 优化充电时间表和旅行时间对于高效的ELV运行至关重要.
研究的目的:
- 解决异质ELVs的低能耗调度 (LECS) 问题.
- 通过优化多个充电站的路线和充电策略来最大限度地降低总能耗.
- 开发一种高效有效的方法来管理复杂物流网络中的ELV操作.
主要方法:
- 提出基于编码器-解码器架构 (HAMEDA) 的异质注意力模型.
- 采用异质图表注意网络和新的解码程序,以提高解决方案质量和学习效率.
- 使用深度强化学习 (DRL) 进行无监督训练,以自主推导最佳运输路线.
主要成果:
- 与传统算法相比,HAMEDA显示了至少1.64%的整体能源利用率下降.
- 该模型有效地平衡了执行速度和解决方案质量,适合大规模应用.
- 模拟证实了HAMEDA在优化ELVs的能源消耗方面的有效性.
结论:
- 哈梅达为电力物流中的LECS问题提供了强大的解决方案.
- 拟议的模型提高了城市物流运营的效率和可持续性.
- 这种方法非常适合时间敏感的大规模物流任务,需要快速决策.
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