Computational Logistics for Container Terminal Logistics Hubs Based on Computational Lens and Computing Principles

Li, Bin; Song, Guanggang

doi:10.1109/access.2020.3033849

Cited by 9 publications

(8 citation statements)

References 29 publications

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“…As a result, the CTHS has been the typical application to demonstrate the ideology and philosophy of computational logistics. e computational logistics tries to overcome the CTL-TOC by the combination of computational thinking, theory of computation, computational lens, and great principles of computing, which are abbreviated to 4CTTLP [59]. It is distinct from the classical research methods for CTHS.…”

Section: Container-terminal-oriented Computational Logisticsmentioning

confidence: 99%

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

2021

Discrete Dynamics in Nature and Society

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The synergy of computational logistics and deep learning provides a new methodology and solution to the operational decisions of container terminal handling systems (CTHS) at the strategic, tactical, and executive levels. Above all, the container terminal logistics tactical operational complexity is discussed by computational logistics, and the liner handling volume (LHV) has important influences on a series of terminal scheduling decision problems. Subsequently, a feature-extraction-based lightweight convolutional and recurrent neural network adaptive computing model (FEB-LCR-ACM) is presented initially to predict the LHV by the fusion of multiple deep learning algorithms and mechanisms, especially for the specific feature extraction package of tsfresh. Consequently, the container-terminal-oriented logistics service scheduling decision support design paradigm is put forward tentatively by FEB-LCR-ACM. Finally, a typical large-scale container terminal of China is chosen to implement, execute, and evaluate the FEB-LCR-ACM based on the terminal running log around the indicator of LHV. In the case of severe vibration of LHV between 2 twenty-foot equivalent units (TEUs) and 4215 TEUs, while forecasting the LHV of 300 liners by the log of five years, the forecasting error within 100 TEUs almost accounts for 80%. When predicting the operation of 350 ships by the log of six years, the forecasting deviation within 100 TEUs reaches up to nearly 90%. The abovementioned two deep learning experimental performances with FEB-LCR-ACM are so far ahead of the forecasting results by the classical machine learning algorithm that is similar to Gaussian support vector machine. Consequently, the FEB-LCR-ACM achieves sufficiently good performance for the LHV prediction with a lightweight deep learning architecture based on the typical small datasets, and then it is supposed to overcome the operational nonlinearity, dynamics, coupling, and complexity of CTHS partially.

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Section: Container-terminal-oriented Computational Logisticsmentioning

confidence: 99%

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

2021

Discrete Dynamics in Nature and Society

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“…On the one hand, it is the synthesis and abstraction of container terminal handling process and physical behaviors. On the other hand, the CTO-LGC is the most direct reflection of the computability of CTHS [ 16 ], and it establishes the foundation of evaluating logistics generalized computational complexity at container terminals [ 29 ].…”

Section: Logistics Generalized Computing Automation and Intelligence For Container Terminalsmentioning

confidence: 99%

“…From the above definition, it is concluded that the computational logistics is significantly different from the traditional methods for CLS that cover mathematic programming, system simulation, intelligent optimization, and simulation-based optimization [13][14][15]. e theoretical origins of computational logistics mainly include computational thinking, computational lens, theory of computation, and great principles of computing [16], which are collectively referred to as 4CTTLP.…”

Section: Computational Logistics and Practical Philosophymentioning

confidence: 99%

Computational Logistics for Container Terminal Handling Systems with Deep Learning

2021

Computational Intelligence and Neuroscience

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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.

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“…Engine Farms Microarchitecture. By computational logistics, the operation of CTHS is highly similar to the running of computer systems whether from the perspective of the system components or in terms of the insights into the operating principles, even at the theoretical level of computation [29,33]. In consequence, the container terminal oriented logistics generalized computing engine farms microarchitecture (CTO-LGC-EFM) makes a crucial effect on the design, implementation, planning, scheduling, execution, upgrading, and reconstruction of CTHS.…”

Section: Container Terminal Logistics Generalized Computingmentioning

confidence: 99%

“…The CTO-LGC is the cornerstone of abstraction and automation for CTHS within the conceptual framework of computational logistics, which is clearly defined according to the essence and connotation of computation [ 31 ]. Furthermore, the problem-oriented computability and generalized computational complexity of CTO-LGC both have also been discussed adequately and defined clearly in our previous studies [ 32 , 33 ], which provide the important theoretical foundations for CTO-LGC from the perspective of the classical theory of computation.…”

Section: Deep Learning For Container Terminal Handling Systems By the Combination Of Computational Logisticsmentioning

confidence: 99%

An Attention Mechanism Oriented Hybrid CNN‐RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction

2021

Computational Intelligence and Neuroscience

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The booming computational thinking and deep learning make it possible to construct agile, efficient, and robust deep learning-driven decision-making support engine for the operation of container terminal handling systems (CTHSs). Within the conceptual framework of computational logistics, an attention mechanism oriented hybrid convolutional neural network and recurrent neural network deep learning architecture (AMO-HCR-DLA) is proposed technically to predict the container terminal liner handling conditions that mainly include liner handling time (LHT) and total working time of quay crane farm (TWT-QCF) for a calling liner. Consequently, the container terminal oriented logistics generalized computation (CTO-LGC) automation and intelligence are established tentatively by AMO-HCR-DLA. A typical regional container terminal hub of China is selected to design, implement, execute, and evaluate the AMO-HCR-DLA with the actual production data. In the case of severe vibration of LHT and TWT-QCF, while forecasting the handling conditions of 210 ships based on the CTO-LGC running log of four years, the forecasting error of LHT within one hour is more than 97% and that of TWT-QCF within six hours accounts for 89.405%. When predicting the operating conditions of 300 liners by the log of five years, the forecasting deviation of LHT within one hour is more than striking 99% and that of TWT-QCF within six hours reaches up to 94.010% as well. All are far superior to the predicting outcomes by the classical algorithms of machine learning and deep learning. Hence, the AMO-HCR-DLA shows excellent performance for the prediction of CTHS with the low and stable computational consuming. It also demonstrates the feasibility, credibility, and realizability of the computing architecture and design paradigm of AMO-HCR-DLA preliminarily.

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Computational Logistics for Container Terminal Logistics Hubs Based on Computational Lens and Computing Principles

Cited by 9 publications

References 29 publications

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

Computational Logistics for Container Terminal Handling Systems with Deep Learning

An Attention Mechanism Oriented Hybrid CNN‐RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction

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