优化城市最后一英里交付效率,通过动态车辆路线启发学和交通流分析
1School of Economy and Trade Management, Yibin Vocational and Technical College, Yibin, 644003, Sichuan, China. luliu@rediffmail.com.
Scientific reports
|December 4, 2025
概括
这项研究引入了一个新的交通意识优化框架,用于城市最后一英里交付,在动态条件下提高效率和可靠性. 基于推广的实时调度 (T-ALNS-RRD) 的 Tabu 引导的自适应性大邻里搜索显著降低了成本,并提高了按时交付率.
科学领域:
- 运营研究 运营研究
- 运输科学 运输科学
- 计算机科学 计算机科学
背景情况:
- 城市最后一英里的交付面临着交通波动,有限的交付窗口和中断的挑战.
- 静态和单一战略的车辆路由方法在动态的城市环境中是无效的.
- 现有的动态VRP模型提供部分解决方案,缺乏集成的适应性搜索,拥堵意识成本和积极的干扰管理.
研究的目的:
- 引入一个新的交通意识优化框架,以实现动态的城市最后一英里交付.
- 将适应性搜索,对拥堵敏感的成本评估和主动的中断管理整合到一个统一的系统中.
- 提高城市交付运营的效率,可靠性和弹性.
主要方法:
- 开发了一个以 Tabu 为导向的自适应性大邻里搜索与基于推广的实时调度 (T-ALNS-RRD).
- 增强了ALNS核心的动态拥堵处罚成本功能.
- 实施了多层的塔布内存系统,用于多样化的勘探和基于推广的调度,用于预防性干扰响应.
主要成果:
- T-ALNS-RRD实现了24.3%的总运营成本降低,并将按时交付率从68.1%提高到92.8%.
- 减少了54.4%的拥堵暴露,并在极端流量变化下将性能恶化限制在15.1%.
- 成功处理了每种场景的27.4个破坏性事件,具有94.2%的解决率和143.7ms的平均响应时间.
结论:
- 该T-ALNS-RRD框架显示了动态城市交付优化的显著性能进步.
- 该方法为未来在物流领域的大规模部署提供了一个可扩展的方法基础.
- 经过验证的中等规模框架在复杂的城市交付场景中提供了更好的效率,可靠性和弹性.
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