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基于深度学习的集装箱码头装卸系统计算物流

Computational Logistics for Container Terminal Handling Systems with Deep Learning.

作者信息

Li Bin, He Yuqing

机构信息

School of Mechanical & Automotive Engineering, Fujian University of Technology, Fuzhou 350118, China.

School of Transportation, Fujian University of Technology, Fuzhou 350118, China.

出版信息

Comput Intell Neurosci. 2021 Apr 26;2021:5529914. doi: 10.1155/2021/5529914. eCollection 2021.

DOI:10.1155/2021/5529914
PMID:33986792
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8093079/
Abstract

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.

摘要

集装箱码头在全球物流网络中发挥着越来越重要的作用；然而，集装箱码头装卸系统（CTHS）的规划、计划、调度和决策都具有高度的非线性、耦合性和复杂性。鉴于此，提出了一种计算物流与深度学习相结合的方法，即面向集装箱码头的神经物理融合计算（CTO-NPFC），以探讨和研究CTHS的模式识别与回归分析。由于班轮靠泊时间（LBT）是码头物流服务和碳效率状况的核心指标，也是CTHS中任务调度和资源分配的重要基础和指导，因此提出了一种用于LBT预测的深度学习模型核心计算架构（DLM-CCA）来实践CTO-NPFC。基于中国某典型集装箱码头过去五年的码头前沿运行数据，使用TensorFlow 2.3和tsfresh的特定特征提取包对DLM-CCA的深度神经网络模型进行了设计、实现、运行和评估。DLM-CCA在普通硬件平台上以较低的消耗成本对LBT显示出敏捷、高效、灵活和出色的预测性能。它初步诠释和证明了CTO-NPFC的理念、范式、架构和算法的可行性和可信度。

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