面向多源扰动的产线动态调度数据集

该数据集主要面向智能制造动态调度优化研究和工业产线多源扰动应对需求建设，基于609所叶轮产线的实时生产数据构建，通过产线传感器、MES（制造执行系统）和工控设备采集，记录了加工任务参数（如工序时长）、设备状态（如负荷率、故障日志）、外部扰动事件（如订单变更、紧急插单）等关键观测值，数据量约179 KB，覆盖连续3个月的生产周期。 数据集通过仿真与真实数据融合生成：首先基于历史工单和排产规则构建基准调度方案，再注入随机扰动（如机器宕机、物料延迟）模拟动态环境，最终形成包含多目标优化标签（完工时间、机器均衡率）的样本。涵盖单件小批量与批量生产混合场景，支持动态重调度算法验证。 该数据的意义在于为扰动驱动的实时调度研究提供高保真基准，尤其适用于叶轮类高精度部件的柔性产线优化，可助力缩短生产周期15%以上并降低设备闲置率，为关键零部件制造的智能化升级提供数据支撑。

This dataset is developed for the research of dynamic scheduling optimization in intelligent manufacturing and the demand of addressing multi-source disturbances in industrial production lines. It is constructed based on real-time production data from 609 impeller production lines, collected via production line sensors, MES (Manufacturing Execution System) and industrial control equipment. The dataset records key observation values including processing task parameters (e.g., operation duration), equipment status (e.g., load rate, fault logs), and external disturbance events (e.g., order changes, emergency order insertions). It has a total data size of approximately 179 KB and covers a production cycle of 3 consecutive months.

This dataset is generated by fusing simulation and real-world data: first, a baseline scheduling scheme is built based on historical work orders and scheduling rules, then random disturbances (e.g., machine downtime, material delays) are injected to simulate dynamic production environments, and finally samples with multi-objective optimization labels (makespan, machine balance rate) are formed. It covers mixed production scenarios of single-piece small-batch and mass production, and supports the verification of dynamic rescheduling algorithms.

The significance of this dataset lies in providing a high-fidelity benchmark for disturbance-driven real-time scheduling research, which is especially suitable for flexible production line optimization of high-precision impeller components. It can help shorten the production cycle by more than 15% and reduce equipment idle rate, providing data support for the intelligent upgrading of key component manufacturing.

搜集汇总

背景与挑战

背景概述

该数据集面向智能制造动态调度优化和工业产线多源扰动应对需求，基于609所叶轮产线的实时生产数据构建，涵盖加工任务、设备状态和外部扰动等关键观测值。它通过仿真与真实数据融合生成，注入随机扰动以模拟动态环境，旨在为扰动驱动的实时调度研究提供高保真基准，支持缩短生产周期并降低设备闲置率。