当前位置：
X-MOL 学术
›
Comput. Intell. Neurosci.
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Computational Logistics for Container Terminal Handling Systems with Deep Learning
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2021-04-26 , DOI: 10.1155/2021/5529914 Bin Li 1 , Yuqing He 2
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2021-04-26 , DOI: 10.1155/2021/5529914 Bin Li 1 , Yuqing He 2
Affiliation
Container terminals are playing an increasingly important role in the global logistics network; however, the programming, planning, scheduling, and decision of the container terminal handling system (CTHS) all are provided with a high degree of nonlinearity, coupling, and complexity. Given that, a combination of computational logistics and deep learning, which is just about container terminal-oriented neural-physical fusion computation (CTO-NPFC), 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-CCA) for LBT prediction is presented to practice CTO-NPFC. 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-CCA is designed, implemented, executed, and evaluated with TensorFlow 2.3 and the specific feature extraction package of tsfresh. The DLM-CCA 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-NPFC preliminarily.
更新日期：2021-04-26
京公网安备 11010802027423号