{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T07:49:10Z","timestamp":1751010550460,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031368073"},{"type":"electronic","value":"9783031368080"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-36808-0_33","type":"book-chapter","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T23:03:04Z","timestamp":1688079784000},"page":"462-470","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Forecasting in\u00a0Shipments: Comparison of\u00a0Machine Learning Regression Algorithms on\u00a0Industrial Applications for\u00a0Supply Chain"],"prefix":"10.1007","author":[{"given":"Nunzio","family":"Carissimo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raffaele","family":"D\u2019Ambrosio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Milena","family":"Guzzo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sabino","family":"Labarile","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carmela","family":"Scalone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Awad, M., Khanna, R.: Support vector regression. In: Efficient Learning Machines. Apress, Berkeley (2015). https:\/\/doi.org\/10.1007\/978-1-4302-5990-9_4","key":"33_CR1","DOI":"10.1007\/978-1-4302-5990-9_4"},{"key":"33_CR2","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1007\/s00170-010-2987-6","volume":"54","author":"R Bhattacharya","year":"2011","unstructured":"Bhattacharya, R., Bandyopadhyay, S.: A review of the causes of bullwhip effect in a supply chain. Int. J. Adv. Manuf. Technol. 54, 1245\u20131261 (2011)","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"33_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"33_CR4","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1016\/j.ejor.2006.12.004","volume":"1843","author":"R Carbonneau","year":"2008","unstructured":"Carbonneau, R., Laframboise, K., Vahidov, R.: Application of machine learning techniques for supply chain demand forecasting. Eur. J. Oper. Res. 1843, 1140\u20131154 (2008)","journal-title":"Eur. J. Oper. Res."},{"key":"33_CR5","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/978-1-4419-9326-7_5","volume-title":"Ensemble Machine Learning","author":"A Cutler","year":"2012","unstructured":"Cutler, A., Cutler, D.R., Stevens, J.R.: Random forests. In: Zhang, C., Ma, Y.Q. (eds.) Ensemble Machine Learning, pp. 157\u2013175. Springer, New York (2012). https:\/\/doi.org\/10.1007\/978-1-4419-9326-7_5"},{"issue":"1\u20132","key":"33_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/S0925-2312(03)00373-4","volume":"55","author":"AV David S\u00e1nchez","year":"2003","unstructured":"David S\u00e1nchez, A.V.: Advanced support vector machines and kernel methods. Neurocomputing 55(1\u20132), 5\u201320 (2003)","journal-title":"Neurocomputing"},{"issue":"2","key":"33_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10444-020-09779-x","volume":"46","author":"N Guglielmi","year":"2020","unstructured":"Guglielmi, N., Scalone, C.: An efficient method for non-negative low-rank completion. Adv. Comput. Math. 46(2), 1\u201325 (2020). https:\/\/doi.org\/10.1007\/s10444-020-09779-x","journal-title":"Adv. Comput. Math."},{"doi-asserted-by":"crossref","unstructured":"Fransoo, J.C., Wouters, M.J.F.: Measuring the bullwhip effect in the supply chain. Supply Chain Manag. 5(2), 78\u201389 (200)","key":"33_CR8","DOI":"10.1108\/13598540010319993"},{"doi-asserted-by":"publisher","unstructured":"Keung, K.L., Lee, C.K.M., Yiu, Y.H.: A machine learning predictive model for shipment delay and demand forecasting for warehouses and sales data. In: 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (2021). https:\/\/doi.org\/10.1109\/IEEM50564.2021.9672946","key":"33_CR9","DOI":"10.1109\/IEEM50564.2021.9672946"},{"doi-asserted-by":"publisher","unstructured":"Kavitha, S., Varuna, S., Ramya, R.: A comparative analysis on linear regression and support vector regression. In: 2016 Online International Conference on Green Engineering and Technologies (IC-GET) (2016). https:\/\/doi.org\/10.1109\/GET.2016.7916627","key":"33_CR10","DOI":"10.1109\/GET.2016.7916627"},{"issue":"1","key":"33_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00345-2","volume":"7","author":"S Islam","year":"2020","unstructured":"Islam, S., Amin, S.H.: Prediction of probable backorder scenarios in the supply chain using distributed random forest and gradient boosting machine learning techniques. J. Big Data 7(1), 1\u201322 (2020). https:\/\/doi.org\/10.1186\/s40537-020-00345-2","journal-title":"J. Big Data"},{"unstructured":"Lee, H.L., Padmanabhan, V., Whang, S.: The bullwhip effect in supply chains. Sloan Manag. Rev. 38(3), 93\u2013102 (1997)","key":"33_CR12"},{"doi-asserted-by":"crossref","unstructured":"Lin, Q., Zhao, Q., Lev, B.: Cold chain transportation decision in the vaccine supply chain. Eur. J. Oper. Res. 283(1), 182\u2013195 (2020)","key":"33_CR13","DOI":"10.1016\/j.ejor.2019.11.005"},{"key":"33_CR14","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.biosystemseng.2018.04.016","volume":"171","author":"S Mercier","year":"2018","unstructured":"Mercier, S., Uysal, I.: Neural network models for predicting perishable food temperatures along the supply chain. Biosys. Eng. 171, 91\u2013100 (2018)","journal-title":"Biosys. Eng."},{"key":"33_CR15","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine Learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"33_CR16","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1080\/22797254.2017.1299557","volume":"50","author":"E Raczko","year":"2017","unstructured":"Raczko, E., Zagajewski, B.: Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images. Eur. J. Remote Sens. 50(1), 144\u2013154 (2017)","journal-title":"Eur. J. Remote Sens."},{"issue":"3","key":"33_CR17","doi-asserted-by":"publisher","first-page":"51","DOI":"10.3390\/axioms7030051","volume":"7","author":"C Scalone","year":"2018","unstructured":"Scalone, C., Guglielmi, N.: A gradient system for low rank matrix completion. Axioms 7(3), 51 (2018)","journal-title":"Axioms"},{"issue":"3","key":"33_CR18","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1108\/JAMR-01-2015-0002","volume":"12","author":"MM Siddh","year":"2015","unstructured":"Siddh, M.M., Soni, G., Jain, R.: Perishable food supply chain quality (PFSCQ): a structured review and implications for future research. J. Adv. Manag. Res. 12(3), 292\u2013313 (2015)","journal-title":"J. Adv. Manag. Res."},{"doi-asserted-by":"publisher","unstructured":"Stadtler H.: Supply chain management - an overview. In: Stadtler, H., Kilger, C. (eds) Supply Chain Management and Advanced Planning, pp. 9\u201336. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-74512-9_2","key":"33_CR19","DOI":"10.1007\/978-3-540-74512-9_2"},{"key":"33_CR20","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1007\/978-3-030-28377-3_27","volume-title":"Computational Collective Intelligence","author":"N Vairagade","year":"2019","unstructured":"Vairagade, N., Logofatu, D., Leon, F., Muharemi, F.: Demand forecasting using random forest and artificial neural network for supply chain management. In: Nguyen, N.T., Chbeir, R., Exposito, E., Aniort\u00e9, P., Trawi\u0144ski, B. (eds.) ICCCI 2019. LNCS (LNAI), vol. 11683, pp. 328\u2013339. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28377-3_27"},{"key":"33_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9326-7","volume-title":"Ensemble Machine Learning: Methods and Applications","author":"C Zhang","year":"2012","unstructured":"Zhang, C., Ma, Y.: Ensemble Machine Learning: Methods and Applications. Springer, New York (2012). https:\/\/doi.org\/10.1007\/978-1-4419-9326-7"},{"issue":"4","key":"33_CR22","first-page":"1937","volume":"14","author":"M Zohdi","year":"2022","unstructured":"Zohdi, M., Rafiee, M., Kayvanfar, V., Salamiraad, A.: Demand forecasting based machine learning algorithms on customer information: an applied approach. Int. J. Inf. Technol. 14(4), 1937\u20131947 (2022)","journal-title":"Int. J. Inf. Technol."}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36808-0_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T23:12:04Z","timestamp":1688080324000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36808-0_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031368073","9783031368080"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36808-0_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Custom based on Cyberchair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"283","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PHD Showcase Papers: 6(for main conf) \/ For ICCSA 2023 Workshops 876 subm sent, 350 full papers and 29 short papers accepted, additional PHD Showcase Papers: 2","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}