Alessandro Rizzuto, David Govi, F. Schipani, Alessandro Lazzeri
{"title":"基于机器和深度学习算法的钻井工厂交货期估计:一个案例研究","authors":"Alessandro Rizzuto, David Govi, F. Schipani, Alessandro Lazzeri","doi":"10.5220/0010655000003062","DOIUrl":null,"url":null,"abstract":": This project is presented as a real case-study based on machine learning and deep learning algorithms which are compared for a clearer understanding of which procedure is more suitable to industrial drilling.The predic-tions are obtained by using algorithms with a pre-processed dataset which was made available by the industry. The losses of each algorithm together with the SHAP values are reported, in order to understand which features most influenced the final prediction.","PeriodicalId":380008,"journal":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lead Time Estimation of a Drilling Factory with Machine and Deep Learning Algorithms: A Case Study\",\"authors\":\"Alessandro Rizzuto, David Govi, F. Schipani, Alessandro Lazzeri\",\"doi\":\"10.5220/0010655000003062\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": This project is presented as a real case-study based on machine learning and deep learning algorithms which are compared for a clearer understanding of which procedure is more suitable to industrial drilling.The predic-tions are obtained by using algorithms with a pre-processed dataset which was made available by the industry. The losses of each algorithm together with the SHAP values are reported, in order to understand which features most influenced the final prediction.\",\"PeriodicalId\":380008,\"journal\":{\"name\":\"International Conference on Innovative Intelligent Industrial Production and Logistics\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Innovative Intelligent Industrial Production and Logistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0010655000003062\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Innovative Intelligent Industrial Production and Logistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0010655000003062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Lead Time Estimation of a Drilling Factory with Machine and Deep Learning Algorithms: A Case Study
: This project is presented as a real case-study based on machine learning and deep learning algorithms which are compared for a clearer understanding of which procedure is more suitable to industrial drilling.The predic-tions are obtained by using algorithms with a pre-processed dataset which was made available by the industry. The losses of each algorithm together with the SHAP values are reported, in order to understand which features most influenced the final prediction.
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