淮安分拨中心不同车轴车装载率数据

通过分析淮安分拨中心不同车轴车的装载率，结果应用到调度系统和在线绩效评估体系中。（1）用于不同省区，不同分拨的内控管理，将车辆装载率是否达到指定值作为内部车辆调度管理的重要的量化依据。（2）合理的规划车辆类型和运输路线，提高单次运输的货物量，减少空驶和空载的情况，降低运输成本，提高整体运输效率。（3）通过对车辆装载率的监控和分析，可以发现运输过程中的浪费现象，并采取措施进行改进。（4）根据历史数据分析，预测未来的运输需求，合理调配车辆资源，避免资源闲置。（5）通过提高装载率提高运输效率，可以减少碳排放和其他污染物的排放，有助于实现绿色物流的目标。1、数据来源采集中通快运-汽运系统，数据驾驶舱平台，聚焦淮安分拨中心不同车轴数的重量装载率信息。包括车辆轴数，额定重量，实际重量，始发省区，始发分拨等核心字段。2、数据处理 （1）实际装载重量：对车辆状态进行筛查，区分在途运输和非运输。对车辆上货物称重获取每台车辆的实际装载重量。（2）额定装载重量：根据不同省区的车轴的车辆评估出额定装载重量。（3）实际装载率=实际装载重量/额定装载重量 （4）目标重量装载率：根据不同省区历史的运输情况，车辆安排情况采用分位算法确定不同省区车辆的目标装载率，从而降低运输成本，减少空驶和空载情况，辅助优化仓库的存储布局和货物出库安排。（5）对比实际重量装载率和目标装载率，分析偏差原因，制定改进方案，如调整装载方案和装载策略，调整运输路线，合并订单，与第三方物流合作，共享运输资源等，改进后持续对比分析。

This dataset analyzes the loading rates of vehicles with different axles at the Huaian Distribution Center, and applies the analysis results to the scheduling system and online performance evaluation system. (1) It is applied to internal control management for various provinces, regions and distribution centers, taking whether the vehicle loading rate meets the specified threshold as an important quantitative basis for internal vehicle scheduling management. (2) Reasonably plan vehicle types and transportation routes, increase the cargo volume per single trip, reduce deadheading and no-load situations, lower transportation costs, and enhance overall transportation efficiency. (3) By monitoring and analyzing vehicle loading rates, waste generated during transportation can be identified, and targeted improvement measures can be implemented. (4) Based on historical data analysis, forecast future transportation demand, rationally allocate vehicle resources, and avoid resource idling. (5) Improving transportation efficiency by increasing loading rates can reduce carbon emissions and other pollutant emissions, contributing to the achievement of green logistics goals. 1. Data Sources: Data is collected from ZTO Express Road Freight System and Data Cockpit Platform, focusing on weight-based loading rate information of vehicles with different axles at the Huaian Distribution Center. Core fields include the number of vehicle axles, rated loading weight, actual loading weight, origin province and region, origin distribution center, and other relevant fields. 2. Data Processing (1) Actual Loading Weight: Screen vehicle status to distinguish between in-transit and non-transport vehicles. Weigh the cargo onboard each vehicle to obtain its actual loading weight. (2) Rated Loading Weight: Calculate the rated loading weight of vehicles with different axles based on the assessment of each province and region. (3) Actual Loading Rate = Actual Loading Weight / Rated Loading Weight (4) Target Weight Loading Rate: Determine the target loading rate for vehicles in different provinces and regions using the quantile algorithm, based on historical transportation conditions and vehicle arrangements of each region, so as to reduce transportation costs, cut down deadheading and no-load situations, and assist in optimizing warehouse storage layout and cargo outbound arrangements. (5) Compare the actual weight-based loading rate with the target loading rate, analyze the causes of deviations, and formulate improvement plans such as adjusting loading plans and strategies, revising transportation routes, consolidating orders, cooperating with third-party logistics (3PL), sharing transportation resources, etc. Conduct continuous comparative analysis after implementing the improvements.

搜集汇总

数据集介绍

特点

该数据集记录了淮安分拨中心不同车轴车的装载率信息，包括日期、始发省区、车辆轴数、额定重量、实际重量等字段，共2159条数据，每日更新。应用场景包括车辆调度管理、运输路线规划、运输效率提升和绿色物流目标实现等。