Longlong He , Jiani Gao , Jiewu Leng , Yue Wu , Kai Ding , Lin Ma , Jie Liu , Duc Truong Pham
{"title":"工业 5.0 设备退役的拆卸顺序规划:前景与展望","authors":"Longlong He , Jiani Gao , Jiewu Leng , Yue Wu , Kai Ding , Lin Ma , Jie Liu , Duc Truong Pham","doi":"10.1016/j.aei.2024.102939","DOIUrl":null,"url":null,"abstract":"<div><div>With the advent of Industry 5.0, the complexity and variety involved in disassembling decommissioned equipment have increased significantly, underscoring the growing importance of disassembly sequence planning (DSP) for resource recovery and reuse. Industry 5.0 emphasizes human-centricity, resilience, and sustainable development, raising new challenges and higher standards for DSP technologies and methods. While previous studies have highlighted the need to study DSP in the context of Industry 5.0, focusing on leveraging technological advancements to optimize the disassembly process, our work takes a different approach. We emphasize the integration of intelligent systems and human–machine collaboration to provide comprehensive solutions, from constructing information models to optimizing sequence algorithms, while also exploring emerging research directions to address the demands of this new era. In order to address the evolving challenges presented by Industry 5.0, this study seeks to reevaluate the pivotal role of DSP in the domain of retired equipment. It also intends to conduct a thorough investigation of DSP from the perspectives of humanism, resilience, and sustainability. By assessing the applicability of existing DSP approaches in the Industry 5.0 landscape, there is a specific focus on the integration of big data analytics and intelligent algorithms to enhance disassembly efficiency, optimize resource allocation, and achieve environmentally sustainable development goals. The research reveals certain limitations in the current state of DSP, namely in terms of intelligence, flexibility, and sustainability. For Industry 5.0, DSP should holistically consider human factors, robustness, and sustainability. Adopting these approaches enhances disassembly efficiency, optimizes resource utilization, mitigates environmental impact, and promotes the achievement of sustainable development goals.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102939"},"PeriodicalIF":8.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Disassembly sequence planning of equipment decommissioning for industry 5.0: Prospects and Retrospects\",\"authors\":\"Longlong He , Jiani Gao , Jiewu Leng , Yue Wu , Kai Ding , Lin Ma , Jie Liu , Duc Truong Pham\",\"doi\":\"10.1016/j.aei.2024.102939\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>With the advent of Industry 5.0, the complexity and variety involved in disassembling decommissioned equipment have increased significantly, underscoring the growing importance of disassembly sequence planning (DSP) for resource recovery and reuse. Industry 5.0 emphasizes human-centricity, resilience, and sustainable development, raising new challenges and higher standards for DSP technologies and methods. While previous studies have highlighted the need to study DSP in the context of Industry 5.0, focusing on leveraging technological advancements to optimize the disassembly process, our work takes a different approach. We emphasize the integration of intelligent systems and human–machine collaboration to provide comprehensive solutions, from constructing information models to optimizing sequence algorithms, while also exploring emerging research directions to address the demands of this new era. In order to address the evolving challenges presented by Industry 5.0, this study seeks to reevaluate the pivotal role of DSP in the domain of retired equipment. It also intends to conduct a thorough investigation of DSP from the perspectives of humanism, resilience, and sustainability. By assessing the applicability of existing DSP approaches in the Industry 5.0 landscape, there is a specific focus on the integration of big data analytics and intelligent algorithms to enhance disassembly efficiency, optimize resource allocation, and achieve environmentally sustainable development goals. The research reveals certain limitations in the current state of DSP, namely in terms of intelligence, flexibility, and sustainability. For Industry 5.0, DSP should holistically consider human factors, robustness, and sustainability. Adopting these approaches enhances disassembly efficiency, optimizes resource utilization, mitigates environmental impact, and promotes the achievement of sustainable development goals.</div></div>\",\"PeriodicalId\":50941,\"journal\":{\"name\":\"Advanced Engineering Informatics\",\"volume\":\"62 \",\"pages\":\"Article 102939\"},\"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/S1474034624005901\",\"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/S1474034624005901","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}
Disassembly sequence planning of equipment decommissioning for industry 5.0: Prospects and Retrospects
With the advent of Industry 5.0, the complexity and variety involved in disassembling decommissioned equipment have increased significantly, underscoring the growing importance of disassembly sequence planning (DSP) for resource recovery and reuse. Industry 5.0 emphasizes human-centricity, resilience, and sustainable development, raising new challenges and higher standards for DSP technologies and methods. While previous studies have highlighted the need to study DSP in the context of Industry 5.0, focusing on leveraging technological advancements to optimize the disassembly process, our work takes a different approach. We emphasize the integration of intelligent systems and human–machine collaboration to provide comprehensive solutions, from constructing information models to optimizing sequence algorithms, while also exploring emerging research directions to address the demands of this new era. In order to address the evolving challenges presented by Industry 5.0, this study seeks to reevaluate the pivotal role of DSP in the domain of retired equipment. It also intends to conduct a thorough investigation of DSP from the perspectives of humanism, resilience, and sustainability. By assessing the applicability of existing DSP approaches in the Industry 5.0 landscape, there is a specific focus on the integration of big data analytics and intelligent algorithms to enhance disassembly efficiency, optimize resource allocation, and achieve environmentally sustainable development goals. The research reveals certain limitations in the current state of DSP, namely in terms of intelligence, flexibility, and sustainability. For Industry 5.0, DSP should holistically consider human factors, robustness, and sustainability. Adopting these approaches enhances disassembly efficiency, optimizes resource utilization, mitigates environmental impact, and promotes the achievement of sustainable development goals.
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.