{"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, Lingwei Zhao, 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":null,"pages":null},"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, Lingwei Zhao, 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\":null,\"pages\":null},\"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}
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.
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.