分层强化学习多阶段供应资源分配方法数据集

研究物资物流供应保障感知预警问题，设计基于时空图卷积神经网络的多阶段动态供给能力预测模型，实现多阶段细粒度应急物资供需精准预警。本数据集包含配送区域运力数据和物资需求量数据，包含2022年配送的快递员运力数据，数据采集自京东北京、上海等地的营业部的真实物流运力数据，数据量5MB。This study focuses on the perception and early warning issues of material logistics supply support. A multi-stage dynamic supply capacity prediction model based on Spatial-Temporal Graph Convolutional Neural Networks is developed to achieve accurate multi-stage fine-grained early warning for emergency material supply and demand. This dataset includes delivery area transport capacity data and material demand data, among which the 2022 courier delivery capacity data is collected from real logistics capacity data sourced from JD.com's business outlets in Beijing, Shanghai and other domestic regions, with a total data size of 5 MB.

搜集汇总

背景与挑战

背景概述

该数据集专注于物资物流供应保障的感知预警问题，采用基于时空图卷积神经网络的多阶段动态供给能力预测模型，以实现应急物资供需的精准预警。它包含2022年京东在北京、上海等地的真实物流运力数据以及物资需求量数据，数据量为5MB，涉及强化学习和资源分配等关键技术。