{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T10:04:27Z","timestamp":1769940267236,"version":"3.49.0"},"reference-count":39,"publisher":"Informa UK Limited","issue":"5","funder":[{"DOI":"10.13039\/100000010","name":"Ford Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000010","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Production Research"],"published-print":{"date-parts":[[2024,3,3]]},"DOI":"10.1080\/00207543.2023.2199438","type":"journal-article","created":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T13:00:21Z","timestamp":1681822821000},"page":"1699-1714","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":7,"title":["Hierarchical RNN-based framework for throughput prediction in automotive production systems"],"prefix":"10.1080","volume":"62","author":[{"given":"Mengfei","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Furness","sequence":"additional","affiliation":[{"name":"Global Data Insight &amp; Analytics, Ford Motor Company, Dearborn, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajesh","family":"Gupta","sequence":"additional","affiliation":[{"name":"Global Data Insight &amp; Analytics, Ford Motor Company, Dearborn, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saumuy","family":"Puchala","sequence":"additional","affiliation":[{"name":"Manufacturing Technology Development, Ford Motor Company, Dearborn, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8433-6326","authenticated-orcid":false,"given":"Weihong (Grace)","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"301","published-online":{"date-parts":[[2023,4,18]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4047855"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.15282\/jmes.7.2014.23.0121"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.07.037"},{"key":"e_1_3_3_5_1","doi-asserted-by":"crossref","unstructured":"Cho Kyunghyun Bart Van Merri\u00ebnboer Caglar Gulcehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk and Yoshua Bengio. 2014. \u201cLearning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation.\u201d arXiv preprint arXiv:1406. 1078.","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1506.02216"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0219720005001004"},{"key":"e_1_3_3_8_1","doi-asserted-by":"crossref","unstructured":"Grabczewski Krzysztof and Norbert Jankowski. 2005. \u201cFeature Selection with Decision Tree Criterion.\u201d In Fifth International Conference on Hybrid Intelligent Systems (HIS'05) .\u00a0Rio de Janeiro Brazil.","DOI":"10.1109\/ICHIS.2005.43"},{"key":"e_1_3_3_9_1","unstructured":"Hall Mark A. 2000. \u201cCorrelation-Based Feature Selection of Discrete and Numeric Class Machine Learning.\u201d In Proceedings of the Seventeenth International Conference on Machine Learning .\u00a0Stanford CA USA."},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.1080\/002075499191319"},{"key":"e_1_3_3_11_1","unstructured":"Johannesson Anton and Perham Shams. 2018. \u201cData-Driven and Variant-Based Throughput and Bottleneck Prediction Using Ensembled Machine Learning Algorithms.\u201d Master's thesis."},{"key":"e_1_3_3_12_1","doi-asserted-by":"crossref","unstructured":"Kinoshita Keisuke Thilo von Neumann Marc Delcroix Tomohiro Nakatani and Reinhold Haeb-Umbach. 2020. \u201cMulti-Path RNN for Hierarchical Modeling of Long Sequential Data and Its Application to Speaker Stream Separation.\u201d arXiv preprint arXiv:2006.13579 .","DOI":"10.21437\/Interspeech.2020-2388"},{"key":"e_1_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2018.1443230"},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2021.07.016"},{"key":"e_1_3_3_15_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207540701829752"},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207540701881860"},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4003786"},{"key":"e_1_3_3_18_1","volume-title":"Introduction to Statistical Quality Control","author":"Montgomery Douglas C.","year":"2020","unstructured":"Montgomery, Douglas C. 2020. Introduction to Statistical Quality Control. Hoboken, NJ, USA: John Wiley & Sons."},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/wics.199"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2013.12.007"},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-04717-1_9"},{"key":"e_1_3_3_22_1","unstructured":"Pandian Annamalai and Ahad Ali. 2013. \u201cAutomotive Assembly Line Production Loss Prediction Based on ARMA-ANN Model.\u201d In Proceedings of IIE Annual Conference 2571. Institute of Industrial and Systems Engineers (IISE).\u00a0Caribe Hilton San Juan Puerto Rico."},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.159"},{"key":"e_1_3_3_24_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207540050117477"},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-016-1423-9"},{"key":"e_1_3_3_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66926-7_43"},{"key":"e_1_3_3_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/WSC.2002.1166360"},{"key":"e_1_3_3_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-85874-2_74"},{"key":"e_1_3_3_29_1","doi-asserted-by":"publisher","DOI":"10.3926\/jiem.1464"},{"key":"e_1_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.01.026"},{"key":"e_1_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2021.07.021"},{"key":"e_1_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1080\/23311916.2016.1239516"},{"key":"e_1_3_3_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2019.07.004"},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106851"},{"key":"e_1_3_3_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2020.02.011"},{"key":"e_1_3_3_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2018.04.024"},{"key":"e_1_3_3_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2018.01.006"},{"key":"e_1_3_3_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105683"},{"key":"e_1_3_3_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11771-022-4906-z"},{"key":"e_1_3_3_40_1","doi-asserted-by":"crossref","unstructured":"Zhao Z. R. Anand and M. Wang. 2019. \u201cMaximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform.\u201d In 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA) .\u00a0San Diego CA USA","DOI":"10.1109\/DSAA.2019.00059"}],"container-title":["International Journal of Production Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/00207543.2023.2199438","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T12:36:41Z","timestamp":1707223001000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/00207543.2023.2199438"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,18]]},"references-count":39,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,3,3]]}},"alternative-id":["10.1080\/00207543.2023.2199438"],"URL":"https:\/\/doi.org\/10.1080\/00207543.2023.2199438","relation":{},"ISSN":["0020-7543","1366-588X"],"issn-type":[{"value":"0020-7543","type":"print"},{"value":"1366-588X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,18]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tprs20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tprs20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2022-07-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-03-26","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-04-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}