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交付数据:用于最后一英里交付优化的现实世界数据集.
Anna Vrani1, Savvas D Apostolidis1, Athanasios Ch Kapoutsis1
1Information Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, Greece.
Data in brief
|June 30, 2025
概括
这一数据集提供了结构化的距离和时间矩阵,用于优化物流中的车辆路由问题 (VRP). 它使用真实世界的交通数据实现了交付操作的现实建模.
科学领域:
- 运营研究 运营研究
- 物流和供应链管理的物流和供应链管理.
- 数据科学数据科学数据科学
背景情况:
- 车辆路线问题 (VRP) 在物流中对于优化交付路线至关重要.
- 在现实世界中,VRP优化需要准确的行程时间和距离数据,并考虑到交通变化.
- 现有的数据集往往缺乏全面的,真实世界的交通影响的旅行时间信息.
研究的目的:
- 为优化车辆路线问题 (VRP) 构建了一个新的数据集.
- 从实际的物流运营中提供现实的行程时间和距离矩阵.
- 通过使用行业相关数据,促进VRP算法的比较和开发.
主要方法:
- 从第三方物流 (3PL) 提供商的订单管理系统中获取的数据.
- 使用包含历史和实时流量数据的API生成的距离和时间矩阵.
- 创建了四个矩阵:一个用于距离,三个用于旅行时间 (乐观,悲观,最可能的交通场景).
主要成果:
- 一个包含9天的结构化距离和时间矩阵的数据集,用于每天60-85次的交货停留.
- 矩阵为所有地点之间的旅行时间和距离提供了完整的视图,这对VRP至关重要.
- 数据通过考虑交通拥堵的变化来反映现实的旅行条件.
结论:
- 该数据集作为一个有价值的基准来评估和比较VRP算法.
- 允许统计分析和蒙特卡洛模拟来建模路由不确定性.
- 支持优化现实世界交付操作,并解决行业物流挑战.
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