柔性作业车间调度实例数据集

柔性作业车间调度是离散电子制造工业中的核心技术，它旨在利用现有生产条件实现最大化的生产效率。项目研究人员提出了一种基于强化学习的智能调度算法，本数据集服务于该调度算法，为算法提供了包括模型训练、验证与测试所需的柔性作业车间生产实例。本数据集主要由人工数据集和公开数据集两部分组成，其中人工数据集是通过程序生成的，主要使用场景为模型的训练及跨分布测试；公开数据集是通过网络搜集得到的，主要使用场景为所提出算法和之前的算法进行比较。数据集包含了多种分布和规模的生产实例，能充分反应不同产线的排产问题，具有典型性和有效性。

Flexible Job Shop Scheduling (FJSS) is a core technology in the discrete electronic manufacturing industry, which aims to maximize production efficiency utilizing existing production conditions. Project researchers have proposed an intelligent scheduling algorithm based on reinforcement learning, and this dataset is designed to support this algorithm, providing it with flexible job shop production instances required for model training, validation and testing. This dataset primarily comprises two parts: artificial dataset and public dataset. The artificial dataset is generated through computer programs, and its main application scenarios include model training and cross-distribution testing. The public dataset is collected from online resources, and its core use case is to conduct comparative experiments between the proposed algorithm and existing scheduling algorithms. The dataset covers production instances with diverse distributions and scales, which can fully represent production scheduling problems of different production lines, thus possessing typicality and effectiveness.

搜集汇总

背景与挑战

背景概述

该数据集是为柔性作业车间调度算法提供的实例数据，包含人工生成和公开搜集两部分，用于模型训练、验证和算法比较。数据集涵盖多种分布和规模的生产实例，具有典型性和有效性。