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Given that, a combination of computational logistics and deep learning, which is just about container terminal\u2010oriented neural\u2010physical fusion computation (CTO\u2010NPFC), is proposed to discuss and explore the pattern recognition and regression analysis of CTHS. Because the liner berthing time (LBT) is the central index of terminal logistics service and carbon efficiency conditions and it is also the important foundation and guidance to task scheduling and resource allocation in CTHS, a deep learning model core computing architecture (DLM\u2010CCA) for LBT prediction is presented to practice CTO\u2010NPFC. Based on the quayside running data for the past five years at a typical container terminal in China, the deep neural networks model of the DLM\u2010CCA is designed, implemented, executed, and evaluated with TensorFlow 2.3 and the specific feature extraction package of tsfresh. The DLM\u2010CCA shows agile, efficient, flexible, and excellent forecasting performances for LBT with the low consuming costs on a common hardware platform. It interprets and demonstrates the feasibility and credibility of the philosophy, paradigm, architecture, and algorithm of CTO\u2010NPFC preliminarily.<\/jats:p>","DOI":"10.1155\/2021\/5529914","type":"journal-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T00:11:17Z","timestamp":1619482277000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Computational Logistics for Container Terminal Handling Systems with Deep Learning"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9322-1723","authenticated-orcid":false,"given":"Bin","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3853-2809","authenticated-orcid":false,"given":"Yuqing","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,4,26]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.omega.2009.10.008"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2019.04.069"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1057\/s41278-019-00131-9"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2014.09.013"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2019.02.013"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/682486"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2020.02.021"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2019.100972"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2019.06.003"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2020.05.017"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2019.104781"},{"key":"e_1_2_9_12_2","doi-asserted-by":"crossref","unstructured":"LiB. 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