Antonio Cimino , Mohaiad Elbasheer , Francesco Longo , Giovanni Mirabelli , Vittorio Solina , Pierpaolo Veltri
{"title":"工业系统中自动仿真模型的生成:系统的文献综述和工业5.0中仿真技术的展望","authors":"Antonio Cimino , Mohaiad Elbasheer , Francesco Longo , Giovanni Mirabelli , Vittorio Solina , Pierpaolo Veltri","doi":"10.1016/j.jmsy.2025.03.027","DOIUrl":null,"url":null,"abstract":"<div><div>Simulation models are a crucial enabling technology for decision support in the ongoing industrial digitalization hype. Within Industry 4.0, simulations are extensively utilized, providing insights into industrial behavior and responses. As we progress towards Industry 5.0, simulation models continue to play a pivotal role in achieving sustainable, resilient, and human-oriented industrial systems. However, a persistent challenge within Industry 4.0/5.0 is the substantial dynamism of industrial environments. This dynamic and complex landscape necessitates the development of adaptive solutions capable of swiftly responding to the volatile process requirements of modern industrial systems. To this end, Automatic Simulation Model Generation (ASMG) offers an innovative methodological framework to address this practical challenge in the development of industrial simulation models. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this research systematically reviews the current state-of-the-art in ASMG. Complemented by a bibliometric and content analysis of 61 articles spanning more than two decades (from 2000 to 2023), the paper evaluates ASMG’s progression and application in manufacturing through four research questions focusing on ASMG development strategies, objectives, essential data, and developing environments. Ultimately, this article provides valuable insights into ASMG perspective for industrial simulation specialists and offers guidelines for future developments in the era of Industry 5.0.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 859-882"},"PeriodicalIF":12.2000,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic simulation models generation in industrial systems: A systematic literature review and outlook towards simulation technology in the Industry 5.0\",\"authors\":\"Antonio Cimino , Mohaiad Elbasheer , Francesco Longo , Giovanni Mirabelli , Vittorio Solina , Pierpaolo Veltri\",\"doi\":\"10.1016/j.jmsy.2025.03.027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Simulation models are a crucial enabling technology for decision support in the ongoing industrial digitalization hype. Within Industry 4.0, simulations are extensively utilized, providing insights into industrial behavior and responses. As we progress towards Industry 5.0, simulation models continue to play a pivotal role in achieving sustainable, resilient, and human-oriented industrial systems. However, a persistent challenge within Industry 4.0/5.0 is the substantial dynamism of industrial environments. This dynamic and complex landscape necessitates the development of adaptive solutions capable of swiftly responding to the volatile process requirements of modern industrial systems. To this end, Automatic Simulation Model Generation (ASMG) offers an innovative methodological framework to address this practical challenge in the development of industrial simulation models. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this research systematically reviews the current state-of-the-art in ASMG. Complemented by a bibliometric and content analysis of 61 articles spanning more than two decades (from 2000 to 2023), the paper evaluates ASMG’s progression and application in manufacturing through four research questions focusing on ASMG development strategies, objectives, essential data, and developing environments. Ultimately, this article provides valuable insights into ASMG perspective for industrial simulation specialists and offers guidelines for future developments in the era of Industry 5.0.</div></div>\",\"PeriodicalId\":16227,\"journal\":{\"name\":\"Journal of Manufacturing Systems\",\"volume\":\"80 \",\"pages\":\"Pages 859-882\"},\"PeriodicalIF\":12.2000,\"publicationDate\":\"2025-04-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0278612525000895\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Systems","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0278612525000895","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Automatic simulation models generation in industrial systems: A systematic literature review and outlook towards simulation technology in the Industry 5.0
Simulation models are a crucial enabling technology for decision support in the ongoing industrial digitalization hype. Within Industry 4.0, simulations are extensively utilized, providing insights into industrial behavior and responses. As we progress towards Industry 5.0, simulation models continue to play a pivotal role in achieving sustainable, resilient, and human-oriented industrial systems. However, a persistent challenge within Industry 4.0/5.0 is the substantial dynamism of industrial environments. This dynamic and complex landscape necessitates the development of adaptive solutions capable of swiftly responding to the volatile process requirements of modern industrial systems. To this end, Automatic Simulation Model Generation (ASMG) offers an innovative methodological framework to address this practical challenge in the development of industrial simulation models. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this research systematically reviews the current state-of-the-art in ASMG. Complemented by a bibliometric and content analysis of 61 articles spanning more than two decades (from 2000 to 2023), the paper evaluates ASMG’s progression and application in manufacturing through four research questions focusing on ASMG development strategies, objectives, essential data, and developing environments. Ultimately, this article provides valuable insights into ASMG perspective for industrial simulation specialists and offers guidelines for future developments in the era of Industry 5.0.
期刊介绍:
The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs.
With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.