汽车注塑件及零配件生产计划排程管理数据

数据集的专利加工过程：本数据以常见汽车零部件产品汪塑加工为基础，通过排程算法进行生产计划排程，生成不同订单需求的排程信息。其中，结合原始订单数据进行信息分解，并通过系数进行加工周期匹配，结合加工周期和工时计算模型得出计划折合时，再结合试验数据和操作数据的运算，得出实际折合时，由此构建达成率和不良频率指数并实时得出相关指标，辅助车间管理者决策。该数据集涉及多类汽车零部件产品，在汽车零部件生产加工领域具备较强的推广性，随着数字化转型的不断推进，各类零部件生产加工企业均可作为本数据算法服务对象，具备较强的市场推厂价值。
本数据涉及的生产计划排程算法较多， 主要包含以下几类，主要通过一定的模式和算法平衡三个车间的同步排产信息，做到三个车间同步有效衔接和运行：
一是订单先后顺序算法，即按照订单的优先级和时间先后顺序，进行不同订单的交期排产；
二是订单批量集中算法，即按照一个产品一道工序任务，满足交期的前提下，集中排在一个生产资源上， 尽量避免被拆分到两个或更多个资源上；
三是订单批量连续算法， 即按照一个产品的一道工序任务，一旦排在某个生产资源上，在时间方面要连续，中间不要断开被拆分，插单情况除外；
四是资源负荷均衡化（资源利用最大化）算法， 即所有的生产资源都尽量排上加工任务，所有的资源设备都利用起来，整体的生产周期跨度实现最小化。

Patent processing procedure of the dataset: This dataset is based on the common injection molding processing of automotive spare parts products. Production planning and scheduling are carried out through scheduling algorithms to generate scheduling information for different order demands. Specifically, information decomposition is performed combined with original order data, and matching of processing cycles is conducted via coefficients. The planned equivalent working hours are calculated by combining the processing cycles and labor-hour calculation models, while the actual equivalent working hours are obtained through operations on test data and operational data. Accordingly, the achievement rate and defect frequency index are constructed, and relevant indicators are generated in real time to assist workshop managers in decision-making. This dataset involves various types of automotive spare parts products, and has strong promotability in the field of automotive spare parts production and processing. With the continuous advancement of digital transformation, all kinds of spare parts production and processing enterprises can be the service objects of the algorithms in this dataset, giving it strong market promotion value.

The dataset involves a variety of production planning and scheduling algorithms, which mainly include the following categories. These algorithms balance the synchronized production scheduling information of three workshops through certain modes and algorithms, so as to achieve synchronized and effective connection and operation among the three workshops:
1. Order sequence algorithm: Scheduling for the delivery deadlines of different orders is carried out according to the priority and time sequence of orders;
2. Order batch concentration algorithm: Under the premise of meeting the delivery deadlines, one process task of a product is concentrated on one production resource as much as possible, to avoid being split into two or more resources;
3. Order batch continuity algorithm: Once a process task of a product is scheduled on a certain production resource, it should be continuous in terms of time, without being split by interruptions in the middle, except for emergency order insertion;
4. Resource load balancing (maximizing resource utilization) algorithm: All production resources are scheduled with processing tasks as much as possible, all resource equipment is utilized, and the overall production cycle span is minimized.

搜集汇总

数据集介绍

特点

该数据集提供了汽车注塑件及零配件生产计划排程的详细管理数据，包含多维度生产信息和多种排程算法，旨在优化生产计划、提升效率和交付率，适用于汽车零部件企业、研究机构及系统解决方案供应商。