基于底层启发式算法的自适应旋律搜索算法,用于优化混合配料系统中的物料投料调度

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yufan Huang, Lingwei Zhao, Binghai Zhou
{"title":"基于底层启发式算法的自适应旋律搜索算法,用于优化混合配料系统中的物料投料调度","authors":"Yufan Huang,&nbsp;Lingwei Zhao,&nbsp;Binghai Zhou","doi":"10.1016/j.aei.2024.102855","DOIUrl":null,"url":null,"abstract":"<div><div>Facing highly diversified market demands in automotive industry, changing variants of components produced in mixed-model assembly lines (MMALs) has led to an increasing attention towards the material-feeding processes. Therefore, this paper originally proposes a novel type of material-feeding mode called hybrid kitting, leading to a better adaptation to MMALs. Since energy-saving and Just-in-time (JIT) principles are the two major concerns in production systems, a bi-objective mathematical model is established aiming to collaboratively minimize the multi-load automated guided vehicle (AGV) energy consumption as well as the kit conveyor depreciation cost in the hybrid kitting-based material-feeding system. Due to the non-deterministic polynomial hard (NP-hard) nature of the problem, a modified melody search-based hyper-heuristic algorithm (MMSA-HH) is proposed with seven low-level heuristic (LLH) operators. Based on the basic MSA, the melody composition rules are redesigned to enrich the diversity of solutions, adaptive adjustment of parameters is used to balance the local search and global search, and the fluctuated crowding distance calculation method is used in elite selection along with Pareto rank calculation. Computational experiment results reveal the effectiveness of the MMSA-HH when solving the problem. Finally, the managerial insights are given through comparing the impacts of kit container size, AGV type, and different kitting modes on the two objectives.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102855"},"PeriodicalIF":8.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An adaptive melody search algorithm based on low-level heuristics for material feeding scheduling optimization in a hybrid kitting system\",\"authors\":\"Yufan Huang,&nbsp;Lingwei Zhao,&nbsp;Binghai Zhou\",\"doi\":\"10.1016/j.aei.2024.102855\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Facing highly diversified market demands in automotive industry, changing variants of components produced in mixed-model assembly lines (MMALs) has led to an increasing attention towards the material-feeding processes. Therefore, this paper originally proposes a novel type of material-feeding mode called hybrid kitting, leading to a better adaptation to MMALs. Since energy-saving and Just-in-time (JIT) principles are the two major concerns in production systems, a bi-objective mathematical model is established aiming to collaboratively minimize the multi-load automated guided vehicle (AGV) energy consumption as well as the kit conveyor depreciation cost in the hybrid kitting-based material-feeding system. Due to the non-deterministic polynomial hard (NP-hard) nature of the problem, a modified melody search-based hyper-heuristic algorithm (MMSA-HH) is proposed with seven low-level heuristic (LLH) operators. Based on the basic MSA, the melody composition rules are redesigned to enrich the diversity of solutions, adaptive adjustment of parameters is used to balance the local search and global search, and the fluctuated crowding distance calculation method is used in elite selection along with Pareto rank calculation. Computational experiment results reveal the effectiveness of the MMSA-HH when solving the problem. Finally, the managerial insights are given through comparing the impacts of kit container size, AGV type, and different kitting modes on the two objectives.</div></div>\",\"PeriodicalId\":50941,\"journal\":{\"name\":\"Advanced Engineering Informatics\",\"volume\":\"62 \",\"pages\":\"Article 102855\"},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced Engineering Informatics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1474034624005032\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Engineering Informatics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1474034624005032","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

面对汽车行业高度多样化的市场需求,混合车型装配线(MMALs)上生产的零部件不断变化,导致人们越来越关注材料供给过程。因此,本文原创性地提出了一种新型的物料供应模式--混合装配,从而更好地适应 MMAL。由于节能和准时制(JIT)原则是生产系统的两大关注点,本文建立了一个双目标数学模型,旨在协同最小化基于混合配套的物料供应系统中的多负载自动导引车(AGV)能耗和配套输送机折旧成本。由于该问题具有非确定性多项式困难(NP-hard)的性质,因此提出了一种改进的基于旋律搜索的超启发式算法(MMSA-HH),其中包含七个低级启发式(LLH)算子。在基本 MSA 算法的基础上,重新设计了旋律组成规则以丰富解的多样性,采用自适应参数调整来平衡局部搜索和全局搜索,并在精英选择中使用波动拥挤距离计算方法和帕累托等级计算方法。计算实验结果揭示了 MMSA-HH 在解决问题时的有效性。最后,通过比较配套容器大小、AGV 类型和不同配套模式对两个目标的影响,给出了管理启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An adaptive melody search algorithm based on low-level heuristics for material feeding scheduling optimization in a hybrid kitting system
Facing highly diversified market demands in automotive industry, changing variants of components produced in mixed-model assembly lines (MMALs) has led to an increasing attention towards the material-feeding processes. Therefore, this paper originally proposes a novel type of material-feeding mode called hybrid kitting, leading to a better adaptation to MMALs. Since energy-saving and Just-in-time (JIT) principles are the two major concerns in production systems, a bi-objective mathematical model is established aiming to collaboratively minimize the multi-load automated guided vehicle (AGV) energy consumption as well as the kit conveyor depreciation cost in the hybrid kitting-based material-feeding system. Due to the non-deterministic polynomial hard (NP-hard) nature of the problem, a modified melody search-based hyper-heuristic algorithm (MMSA-HH) is proposed with seven low-level heuristic (LLH) operators. Based on the basic MSA, the melody composition rules are redesigned to enrich the diversity of solutions, adaptive adjustment of parameters is used to balance the local search and global search, and the fluctuated crowding distance calculation method is used in elite selection along with Pareto rank calculation. Computational experiment results reveal the effectiveness of the MMSA-HH when solving the problem. Finally, the managerial insights are given through comparing the impacts of kit container size, AGV type, and different kitting modes on the two objectives.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
×
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