{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T06:18:31Z","timestamp":1663395511794},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,14]]},"abstract":"<jats:p>To stay competitive, modern market scenarios are forcing a radical shift in the manufacturing concept, focusing companies\u2019 attention on customer satisfaction through increased product customization and quick response strategies. Significant progress has been made in the field of Industry 4.0 technologies, but there is still an open gap in the literature regarding methodologies for efficiently managing a manufacturing system\u2019s available productive resources. Spearman et al. proposed the CONtrolled Work-In-Progress (CONWIP) production logic, which allows controlling Work-In-Progress (WIP) in a production system while monitoring throughput. However, in order to face with the increased variability that enters into the production system, an affordable performances estimation tool is still required. Taking advantages of the recent innovation in the field of machine learning, this paper contributes to the development of a tool for estimating the performance of a production line using a deep learning neural network. The results demonstrated that the proposed estimation tool outperforms the current best-known mathematical model when estimating the throughput of a CONWIP Flow-Shop production line with a given processing time distribution and WIP value.<\/jats:p>","DOI":"10.3233\/faia220269","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:08:14Z","timestamp":1663319294000},"source":"Crossref","is-referenced-by-count":0,"title":["A Deep Learning Approach for the Performance Estimation of a Stochastic CONWIP Flow-Shop System"],"prefix":"10.3233","author":[{"given":"Silvestro","family":"Vespoli","sequence":"first","affiliation":[{"name":"Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale (DICMAPI), University of Naples Federico II, Napoli (NA) 80125, Italy"}]},{"given":"Emma","family":"Salatiello","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale (DICMAPI), University of Naples Federico II, Napoli (NA) 80125, Italy"}]},{"given":"Andrea","family":"Grassi","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale (DICMAPI), University of Naples Federico II, Napoli (NA) 80125, Italy"}]},{"given":"Guido","family":"Guizzi","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale (DICMAPI), University of Naples Federico II, Napoli (NA) 80125, Italy"}]},{"given":"Liberatina Carmela","family":"Santillo","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale (DICMAPI), University of Naples Federico II, Napoli (NA) 80125, Italy"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","New Trends in Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220269","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:08:15Z","timestamp":1663319295000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220269"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220269","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,14]]}}}