Unais Sait , Marco Frego , Antonella De Angeli , Angelika Peer
{"title":"基于多输出支持向量回归器的自动化车间规划任务发生分布预测","authors":"Unais Sait , Marco Frego , Antonella De Angeli , Angelika Peer","doi":"10.1016/j.procir.2025.01.032","DOIUrl":null,"url":null,"abstract":"<div><div>The digitalization of shop floors has led to a significant shift towards automated planning and scheduling to improve resource management and production efficiency. This paper presents a comparative study of the use of machine learning approaches for predicting the distribution of task occurrence in activity-based shop floors. This study leverages real data extracted from Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES), and historical data of shopfloor-level processes. Furthermore, three regression-based models, namely a fe-nearest Neighbor Regressor (KNR), Random Forest Regressor (RFR), and Multi-output Support Vector Regressor (M-SVR) are evaluated on the extracted data. The study identifies M-SVR as the best-performing model when hyperparameters were optimised through model optimisation via grid search and 5-fold cross-validation. The comparative analysis includes evaluation metrics, providing insight into effective task prediction in shop floor environments. This paper highlights the importance of data-driven methods for the prediction of manufacturing processes and the digitalization of shop floors.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"132 ","pages":"Pages 191-196"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of task occurrence distribution for automated shop floor planning using multi-output support vector regressor\",\"authors\":\"Unais Sait , Marco Frego , Antonella De Angeli , Angelika Peer\",\"doi\":\"10.1016/j.procir.2025.01.032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The digitalization of shop floors has led to a significant shift towards automated planning and scheduling to improve resource management and production efficiency. This paper presents a comparative study of the use of machine learning approaches for predicting the distribution of task occurrence in activity-based shop floors. This study leverages real data extracted from Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES), and historical data of shopfloor-level processes. Furthermore, three regression-based models, namely a fe-nearest Neighbor Regressor (KNR), Random Forest Regressor (RFR), and Multi-output Support Vector Regressor (M-SVR) are evaluated on the extracted data. The study identifies M-SVR as the best-performing model when hyperparameters were optimised through model optimisation via grid search and 5-fold cross-validation. The comparative analysis includes evaluation metrics, providing insight into effective task prediction in shop floor environments. This paper highlights the importance of data-driven methods for the prediction of manufacturing processes and the digitalization of shop floors.</div></div>\",\"PeriodicalId\":20535,\"journal\":{\"name\":\"Procedia CIRP\",\"volume\":\"132 \",\"pages\":\"Pages 191-196\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Procedia CIRP\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2212827125000320\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Procedia CIRP","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212827125000320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of task occurrence distribution for automated shop floor planning using multi-output support vector regressor
The digitalization of shop floors has led to a significant shift towards automated planning and scheduling to improve resource management and production efficiency. This paper presents a comparative study of the use of machine learning approaches for predicting the distribution of task occurrence in activity-based shop floors. This study leverages real data extracted from Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES), and historical data of shopfloor-level processes. Furthermore, three regression-based models, namely a fe-nearest Neighbor Regressor (KNR), Random Forest Regressor (RFR), and Multi-output Support Vector Regressor (M-SVR) are evaluated on the extracted data. The study identifies M-SVR as the best-performing model when hyperparameters were optimised through model optimisation via grid search and 5-fold cross-validation. The comparative analysis includes evaluation metrics, providing insight into effective task prediction in shop floor environments. This paper highlights the importance of data-driven methods for the prediction of manufacturing processes and the digitalization of shop floors.