Knowledge Commons of Institute of Automation,CAS
MILP Models for Flexible Job Shop Scheduling with Spatial Constraints and Sequence Flexibility | |
Han, Yunjun(韩云君)1![]() ![]() ![]() | |
2024 | |
会议名称 | 2024 IEEE 20th International Conference on Automation Science and Engineering |
会议录名称 | 2024 IEEE 20th International Conference on Automation Science and Engineering |
页码 | 6 |
会议日期 | 2024年8月28 |
会议地点 | Bari,Italy |
摘要 | Within the evolving landscape of Industry 4.0, the significance of flexible job shop scheduling problems is on the rise. This study addresses a novel category of NP- hard scheduling problems: the flexible job shop scheduling problems that incorporate site routing and operation sequenc- ing flexibility. These problems are crucial and applicable in various contexts, including carrier-based aircraft scheduling, shipbuilding assembly scheduling, and cutting tool operation scheduling. For these problems, we present two new MILP models: Model-1 is a time-indexed model, and Model-2 is a precedence variable-based model. These models are evaluated under ten cases of the problems. Our findings indicate that the two models exhibit varying solution efficiencies when deal- ing with problems of diverse magnitudes. Model-2 performs better than Model-1 on small-scale problems, while Model-1 outperforms Model-2 on medium-scale problems. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 决策智能理论与方法 |
国重实验室规划方向分类 | 虚实融合与迁移学习 |
是否有论文关联数据集需要存交 | 是 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57364 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Xiong, Gang |
作者单位 | 1.State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Han, Yunjun,Peng,Shaoming,Shen, Zhen,et al. MILP Models for Flexible Job Shop Scheduling with Spatial Constraints and Sequence Flexibility[C],2024:6. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
第一作者-CASE2024-MILP M(397KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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