{"title":"Multi-objective human-robot collaborative disassembly line balancing considering components remanufacture demand and hazard characteristics","authors":"","doi":"10.1016/j.cie.2024.110621","DOIUrl":null,"url":null,"abstract":"<div><div>Disassembly enterprises face significant challenges in managing increasingly intensive workloads with a workforce alone. To address this, current study focuses on balancing a human-robot collaborative disassembly line. For the task operator allocation problem, a mapping constraint mechanism between the disassembly tasks and the task operators is established based on the remanufacture demand and hazard characteristics of components. A mixed-integer programming model is formulated, encompassing four optimization objectives: the number of workstations, the task operator idle balancing index, the demand index, and the hazard index. A novel modified teaching and learning optimization approach is proposed for the addressed human-robot collaborative disassembly line balancing problem. A two-layer encoding strategy is designed based on the solving characteristics of the problem and an embedded perturbation strategy based on randomly transforming the task operator is introduced to enhance the optimization performance of the proposed method. The validity of the mixed-integer programming model and the efficacy of the proposed algorithm are demonstrated through two small-scale human-robot collaborative disassembly case studies. The proposed algorithm is then applied to two real-life cases of human-robot collaborative disassembly lines with different scales. The results of the proposed optimization method are compared with other advanced algorithms, demonstrating its superior performance in both single-objective and multi-objective optimization based on multiple evaluation indicators.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Industrial Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360835224007435","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Disassembly enterprises face significant challenges in managing increasingly intensive workloads with a workforce alone. To address this, current study focuses on balancing a human-robot collaborative disassembly line. For the task operator allocation problem, a mapping constraint mechanism between the disassembly tasks and the task operators is established based on the remanufacture demand and hazard characteristics of components. A mixed-integer programming model is formulated, encompassing four optimization objectives: the number of workstations, the task operator idle balancing index, the demand index, and the hazard index. A novel modified teaching and learning optimization approach is proposed for the addressed human-robot collaborative disassembly line balancing problem. A two-layer encoding strategy is designed based on the solving characteristics of the problem and an embedded perturbation strategy based on randomly transforming the task operator is introduced to enhance the optimization performance of the proposed method. The validity of the mixed-integer programming model and the efficacy of the proposed algorithm are demonstrated through two small-scale human-robot collaborative disassembly case studies. The proposed algorithm is then applied to two real-life cases of human-robot collaborative disassembly lines with different scales. The results of the proposed optimization method are compared with other advanced algorithms, demonstrating its superior performance in both single-objective and multi-objective optimization based on multiple evaluation indicators.
期刊介绍:
Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.