{"title":"下一代制造系统的智能调度:系统文献综述","authors":"Shriprasad Chorghe, Rishi Kumar, Makarand S. Kulkarni, Vibhor Pandhare, Bhupesh Kumar Lad","doi":"10.1007/s10845-024-02484-2","DOIUrl":null,"url":null,"abstract":"<p>In the current scenario, smart scheduling has become an essential requirement to generate dynamic schedules, prescribe, and adjust scheduling plans in response to dynamic events such as machine failures, unpredictable demand, customer order cancellations, worker unavailability, and mass customization. Such scheduling techniques must also take advantage of intelligence continuously being built for next-generation manufacturing systems. This study presents a systematic literature review on smart scheduling, analysing 123 identified literature from 2010 to May 2024 using the PRISMA technique. The analysis includes scientometric and content analysis to identify paradigm shifts in development (concepts, methodologies, practices) along with their maturity levels, and provides recommendations for the next generation of smart scheduling. This study is significant for advancing knowledge and addressing current and future needs/requirements in smart scheduling. This would serve as a reference in understanding the maturity status of various developments, assist researchers and practitioners in identifying research gaps, and direct future advancements in the smart scheduling domain.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"59 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Smart scheduling for next generation manufacturing systems: a systematic literature review\",\"authors\":\"Shriprasad Chorghe, Rishi Kumar, Makarand S. Kulkarni, Vibhor Pandhare, Bhupesh Kumar Lad\",\"doi\":\"10.1007/s10845-024-02484-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In the current scenario, smart scheduling has become an essential requirement to generate dynamic schedules, prescribe, and adjust scheduling plans in response to dynamic events such as machine failures, unpredictable demand, customer order cancellations, worker unavailability, and mass customization. Such scheduling techniques must also take advantage of intelligence continuously being built for next-generation manufacturing systems. This study presents a systematic literature review on smart scheduling, analysing 123 identified literature from 2010 to May 2024 using the PRISMA technique. The analysis includes scientometric and content analysis to identify paradigm shifts in development (concepts, methodologies, practices) along with their maturity levels, and provides recommendations for the next generation of smart scheduling. This study is significant for advancing knowledge and addressing current and future needs/requirements in smart scheduling. This would serve as a reference in understanding the maturity status of various developments, assist researchers and practitioners in identifying research gaps, and direct future advancements in the smart scheduling domain.</p>\",\"PeriodicalId\":16193,\"journal\":{\"name\":\"Journal of Intelligent Manufacturing\",\"volume\":\"59 1\",\"pages\":\"\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Intelligent Manufacturing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s10845-024-02484-2\",\"RegionNum\":2,\"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":"Journal of Intelligent Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10845-024-02484-2","RegionNum":2,"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}
Smart scheduling for next generation manufacturing systems: a systematic literature review
In the current scenario, smart scheduling has become an essential requirement to generate dynamic schedules, prescribe, and adjust scheduling plans in response to dynamic events such as machine failures, unpredictable demand, customer order cancellations, worker unavailability, and mass customization. Such scheduling techniques must also take advantage of intelligence continuously being built for next-generation manufacturing systems. This study presents a systematic literature review on smart scheduling, analysing 123 identified literature from 2010 to May 2024 using the PRISMA technique. The analysis includes scientometric and content analysis to identify paradigm shifts in development (concepts, methodologies, practices) along with their maturity levels, and provides recommendations for the next generation of smart scheduling. This study is significant for advancing knowledge and addressing current and future needs/requirements in smart scheduling. This would serve as a reference in understanding the maturity status of various developments, assist researchers and practitioners in identifying research gaps, and direct future advancements in the smart scheduling domain.
期刊介绍:
The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.