{"title":"平台聚合制造服务协作基准:方法与实施","authors":"Jiawei Ren , Ying Cheng , Yongping Zhang , Fei Tao","doi":"10.1016/j.rcim.2024.102853","DOIUrl":null,"url":null,"abstract":"<div><p>In light of the global economic downturn and the intricate division of labor in manufacturing, the imperative for advanced manufacturing services and Manufacturing Service Collaboration (MSC) has escalated significantly. As manufacturing services gravitate towards aggregation on manufacturing service platforms, platform-aggregated MSC has emerged as an inevitable and compelling trend, capturing the attention of researchers worldwide. However, despite the existence of numerous frameworks, models, operational mechanisms, and algorithms proposed for the platform-aggregated MSC, drawing comparisons between these studies remains a complex endeavor. To address this predicament, this article proposes and explores a novel benchmarking methodology for platform-aggregated MSC. By employing complex network theory, a comprehensive model of platform-aggregated MSC is constructed and supplemented with corresponding methodologies for data generation and the configuration of optimization algorithms. Moreover, pertinent performance evaluation metrics are scrutinized to assess their applicability in the context of platform-aggregated MSC. The article culminates with the execution of a series of platform operation experiments designed to test the effectiveness and practicality of the proposed benchmarking system, thereby contributing to the ongoing evolution of the MSC domain.</p></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"91 ","pages":"Article 102853"},"PeriodicalIF":9.1000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Benchmarking for platform-aggregated manufacturing service collaboration: Methodology and implementation\",\"authors\":\"Jiawei Ren , Ying Cheng , Yongping Zhang , Fei Tao\",\"doi\":\"10.1016/j.rcim.2024.102853\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In light of the global economic downturn and the intricate division of labor in manufacturing, the imperative for advanced manufacturing services and Manufacturing Service Collaboration (MSC) has escalated significantly. As manufacturing services gravitate towards aggregation on manufacturing service platforms, platform-aggregated MSC has emerged as an inevitable and compelling trend, capturing the attention of researchers worldwide. However, despite the existence of numerous frameworks, models, operational mechanisms, and algorithms proposed for the platform-aggregated MSC, drawing comparisons between these studies remains a complex endeavor. To address this predicament, this article proposes and explores a novel benchmarking methodology for platform-aggregated MSC. By employing complex network theory, a comprehensive model of platform-aggregated MSC is constructed and supplemented with corresponding methodologies for data generation and the configuration of optimization algorithms. Moreover, pertinent performance evaluation metrics are scrutinized to assess their applicability in the context of platform-aggregated MSC. The article culminates with the execution of a series of platform operation experiments designed to test the effectiveness and practicality of the proposed benchmarking system, thereby contributing to the ongoing evolution of the MSC domain.</p></div>\",\"PeriodicalId\":21452,\"journal\":{\"name\":\"Robotics and Computer-integrated Manufacturing\",\"volume\":\"91 \",\"pages\":\"Article 102853\"},\"PeriodicalIF\":9.1000,\"publicationDate\":\"2024-08-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Robotics and Computer-integrated Manufacturing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0736584524001406\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics and Computer-integrated Manufacturing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0736584524001406","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Benchmarking for platform-aggregated manufacturing service collaboration: Methodology and implementation
In light of the global economic downturn and the intricate division of labor in manufacturing, the imperative for advanced manufacturing services and Manufacturing Service Collaboration (MSC) has escalated significantly. As manufacturing services gravitate towards aggregation on manufacturing service platforms, platform-aggregated MSC has emerged as an inevitable and compelling trend, capturing the attention of researchers worldwide. However, despite the existence of numerous frameworks, models, operational mechanisms, and algorithms proposed for the platform-aggregated MSC, drawing comparisons between these studies remains a complex endeavor. To address this predicament, this article proposes and explores a novel benchmarking methodology for platform-aggregated MSC. By employing complex network theory, a comprehensive model of platform-aggregated MSC is constructed and supplemented with corresponding methodologies for data generation and the configuration of optimization algorithms. Moreover, pertinent performance evaluation metrics are scrutinized to assess their applicability in the context of platform-aggregated MSC. The article culminates with the execution of a series of platform operation experiments designed to test the effectiveness and practicality of the proposed benchmarking system, thereby contributing to the ongoing evolution of the MSC domain.
期刊介绍:
The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.