Order Picking Dataset from a Warehouse of a Footwear Manufacturing Company

This dataset originates from a real-world footwear manufacturing warehouse and provides a comprehensive foundation for benchmarking research in warehouse order-picking operations. Data was collected via SQL queries on the company’s Warehouse Management System (WMS), resulting in diverse formats such as CSV files, CAD layouts, and Python scripts. The dataset includes geometric representations of the warehouse layout, with Cartesian-mapped storage locations, aisles, and central depots, detailed product classifications, storage positions, picking wave information, and routing paths. It supports evaluating various storage strategies, including Random, Class-Based, Dedicated, and Hybrid configurations, enabling the analysis of their impact on order-picking efficiency. Temporal data captures operational trends, including timestamps and operator-specific performance, offering insights into workflow efficiency and workload balancing. Anonymization and randomization techniques were applied while retaining realistic operational patterns to preserve confidentiality. This dataset is highly versatile and suitable for developing optimization algorithms for picker routing, order batching, wave generation, and intralogistics, as well as for advancing automation and robotics research through navigation-specific data for autonomous guided vehicles (AGVs) and robotic systems. This dataset significantly contributes to warehouse logistics research and operational optimization by supporting a wide range of applications.

本数据集源自真实的鞋类制造仓库，为仓库订单拣选作业的基准测试研究提供了完备的研究基础。本数据集通过对企业仓库管理系统（Warehouse Management System，WMS）执行SQL查询采集得到，涵盖CSV文件、CAD布局图纸、Python脚本等多种数据格式。本数据集包含仓库布局的几何表征数据，涵盖经笛卡尔坐标映射的存储位置、通道与中央存储区，以及详细的产品分类、存储点位、拣选波次信息与路径规划数据。该数据集可用于评估多种存储策略，包括随机存储、基于分类存储、专属存储与混合存储等配置模式，助力分析各类策略对订单拣选效率的影响。时序数据记录了运营趋势，包含时间戳与作业人员的个体绩效数据，可为作业流程效率与作业负载均衡的分析提供参考依据。为保障数据保密性，本数据集在保留真实运营模式的前提下，采用了匿名化与随机化处理技术。本数据集通用性极强，可用于开发拣选路径规划、订单分批、波次生成与内部物流等场景的优化算法，同时也可通过面向自动导引车（Autonomous Guided Vehicles）与机器人系统的导航专用数据，推动自动化与机器人技术的研究进展。本数据集可支撑多种应用场景，对仓库物流研究与作业优化具有重要的推动价值。