Multi-fidelity simulation optimization for production releasing in re-entrant mixed-flow shops

IF 1.6 3区 工程技术 Q4 ENGINEERING, INDUSTRIAL
Zhengmin Zhang, Z. Guan, L. Yue
{"title":"Multi-fidelity simulation optimization for production releasing in re-entrant mixed-flow shops","authors":"Zhengmin Zhang, Z. Guan, L. Yue","doi":"10.5267/j.ijiec.2022.9.004","DOIUrl":null,"url":null,"abstract":"This research focuses on production releasing and routing allocation problems in re-entrant mixed-flow shops. Since re-entrant mixed flow shops are complex and dynamic, many studies evaluate release plans by developing discrete event simulation models and selecting the optimal solution according to the estimation results. However, a high-accurate discrete event simulation model requires a lot of computation time. In this research, we develop an effective multi-fidelity optimization method to address product release planning problems for re-entrant mixed-flow shops. The proposed method combines the advantages of rapid evaluation of analytical models and accurate evaluation of simulation models. It conducts iterative optimization using a low-fidelity mathematical estimation model to find good solutions and searches for the optimal solution via a high-fidelity simulation estimation model. Computational results of large-scale production releasing and routing allocation problems illustrate that the proposed approach is good at addressing large-scale problems in re-entrant mixed-flow shops.","PeriodicalId":51356,"journal":{"name":"International Journal of Industrial Engineering Computations","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Industrial Engineering Computations","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.5267/j.ijiec.2022.9.004","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0

Abstract

This research focuses on production releasing and routing allocation problems in re-entrant mixed-flow shops. Since re-entrant mixed flow shops are complex and dynamic, many studies evaluate release plans by developing discrete event simulation models and selecting the optimal solution according to the estimation results. However, a high-accurate discrete event simulation model requires a lot of computation time. In this research, we develop an effective multi-fidelity optimization method to address product release planning problems for re-entrant mixed-flow shops. The proposed method combines the advantages of rapid evaluation of analytical models and accurate evaluation of simulation models. It conducts iterative optimization using a low-fidelity mathematical estimation model to find good solutions and searches for the optimal solution via a high-fidelity simulation estimation model. Computational results of large-scale production releasing and routing allocation problems illustrate that the proposed approach is good at addressing large-scale problems in re-entrant mixed-flow shops.
可重入混流车间产品发布的多保真度仿真优化
本文主要研究可重入混合流车间的产品释放和路由分配问题。由于可重入混合流车间是复杂和动态的,许多研究通过建立离散事件仿真模型并根据估计结果选择最优解来评估释放计划。然而,高精度的离散事件仿真模型需要大量的计算时间。在本研究中,我们开发了一种有效的多保真度优化方法来解决可重新进入的混合流商店的产品发布计划问题。该方法结合了分析模型快速评估和仿真模型准确评估的优点。利用低保真度数学估计模型进行迭代优化,寻找好的解,利用高保真度仿真估计模型搜索最优解。大规模产品释放和路由分配问题的计算结果表明,该方法能够很好地解决可重入混合流车间的大规模问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
5.70
自引率
9.10%
发文量
35
审稿时长
20 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信