遇见数据集

Cooperative trucks and drones for rural last-mile delivery with steep roads

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DataCite Commons2025-04-01 更新2024-08-18 收录
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The following instances are originally presented in the paper <em>Cooperative trucks and drones for rural last-mile delivery with steep roads</em>. The data includes 96 instances corresponding to different scales and scenarios. Each instance is named "<em>n</em>. <em>d</em>. <em>m</em>. <em>h</em>", where <em>n</em> is the number of customers, <em>d</em> represents the dimension of the grid, <em>m</em> is the generic name of the scenarios with different coordinates and demands, and <em>h</em> refers to the generic name of the altitude scenarios. The first column of each instance file indicates <em>Coordinate X</em> of each customer. The second and third columns are <em>Coordinate Y</em> and <em>Demand, </em>respectively<em>. </em>The last column is <em>Altitude</em>. Finally, there is an extra file, named <strong>Supplemental online material,</strong> which provides the following appendices: Appendix A: Provides calculations about the main parameters of drone energy consumption. Appendix B: Discusses the differences between the proposed problem with GVRPD and GVRP-SR. Appendix C: Provides the detailed principles for generating benchmark instances. Appendix D: Provides information about the value of the parameters in the main configuration of the problem.

本数据集的所有算例源自论文《面向陡坡路况农村末端配送的协作式卡车与无人机系统》(Cooperative trucks and drones for rural last-mile delivery with steep roads)。数据集共包含96个对应不同规模与场景的算例,每个算例的命名格式为<em>n</em>.<em>d</em>.<em>m</em>.<em>h</em>,其中<em>n</em>为客户总数量,<em>d</em>代表配送网格的空间维度,<em>m</em>为不同坐标与需求场景的通用命名标识,<em>h</em>指代海拔场景的通用命名标识。每个算例数据文件的第一列为各客户的X坐标(Coordinate X),第二、第三列分别为Y坐标(Coordinate Y)与需求(Demand),最后一列为海拔(Altitude)。另有一个名为《附加在线材料》(Supplemental online material)的额外文件,其中包含如下附录:附录A:给出了无人机能耗核心参数的相关计算过程;附录B:探讨了本文所提出的优化问题与GVRPD、GVRP-SR之间的差异;附录C:给出了基准测试算例生成的详细准则;附录D:给出了问题主要配置方案中的参数取值信息。

提供机构:
figshare
创建时间:
2023-04-04
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Cooperative trucks and drones for rural last-mile delivery with steep roads 数据集图片
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