Container Terminal Handling Research Articles

Automated rail-water intermodal transport container terminal handling equipment cooperative scheduling based on bidirectional hybrid flow-shop scheduling problem

The importance of rail-water intermodal transport is becoming more and more prominent in recent years. As a key node of rail-water intermodal transportation, the operation efficiency of automated container terminal directly affects the overall transportation efficiency. However, there are few studies on the collaborative scheduling of automated container terminal equipment in rail-water intermodal transportation. Therefore, this paper focuses on the ‘Automated Quay Crane (AQC)-Automated Straddle Carrier (ASC)-Automated Rail Mounted Gantry Crane (ARMG)’ operation system and addresses the operational efficiency requirements of the Automated Rail-Water Intermodal Transport Container Terminal (ARWITCT), Based on the bidirectional Hybrid Flow-shop Scheduling Problem (HFSP), a scheduling optimization model for the ARWITCT is established with the objective of minimizing the maximum completion time. To solve this complex scheduling problem, Particle Swarm Optimization based on Adaptive Tent Chaos Mapping (PSO-ATCM) is designed. In this algorithm, a parameter adaptive adjustment strategy and a Tent Chaos Mapping Optimization Strategy are introduced to help PSO jump out of local optimum. Problem-feature-based encoding and decoding methods are designed for the problem. An ‘insertion-translation’ mechanism is used to judge and resolve the conflicts between import and export containers during the decoding process. The effect of proposed scheduling model and the performance of the proposed algorithm are validated via comprehensive comparison experiments. To validate the effectiveness of proposed scheduling model, we compare the unidirectional HFSP model with the bidirectional HFSP model. The proposed PSO-ATCM is compared with standard Genetic Algorithm, Particle Swarm Optimization algorithm and Adaptive Genetic Algorithm. Computational results demonstrate that the proposed model is effective and PSO-ATCM performs better than other three compared algorithms.

Analysis of Container Terminal Handling System Based on Petri Net and ExtendSim

The container terminal handling system plays an important role in marine transportation, and improving its efficiency has become a big challenge. Therefore, this paper proposes an analytical method that combines a Petri net with simulation tools. Firstly, the container terminal handling system is abstracted into a Petri net system according to the internal logic of the handling process. Next, eigenvalues of the correlation matrix are calculated to analyse the effectiveness of the Petri net system. Then, the Petri net system is simulated using the Extend-Sim software. The result suggests that, after optimising, the handling capacity of the berth is clearly improved. Using the Petri net and simulation tools together to analyse the container terminal system is the innovation and the most important aspect of this paper. Because the combination of a Petri net and simulation can not only ensure the reliability of the model but also optimise the container terminal handling system more intuitively.

Container Terminal Berth and Yard Collaborative Allocation for Import and Export Synchronous Operations by Computational Logistics

Container terminal operators have practical and clear requirements on improving operational efficiency and service level to address running challenges. The resource allocation and collaborative scheduling of quayside berth and storage yard are vital issues in the optimization of container terminal handling systems (CTHSs). The allocation of berth in quayside and blocks on storage yard directly affects the running efficiency of CTHS. Consequently, we focus on the berth and yard collaborative allocation for import and export synchronous operations (BYCA-IESOs) and propose the BYCA-IESO research model based on synchronous or asynchronous integrated scheduling strategy by computational logistics. The BYCA-IESO model is a mixed integer quadratic programming model to minimize multiple time consumption of calling liners and the total horizontal transferring distances of yard trailers at container terminals. Furthermore, we design other two allocation policies for comparison on the BYCA-IESO, which are both multistage scheduling strategy profiles. CPLEX 20.1.0 is applied to implement and solve the model and tactics of BYCA-IESO. Accordingly, the feasibility, validity, and relative merits of the models and strategies are obtained and verified by analyzing and comparing multiple numerical experimental results.

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

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.

Computational Logistics for Container Terminal Handling Systems with Deep Learning.

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.

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

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.

Dynamic pricing research for container terminal handling charge

ABSTRACT The purpose of this study is to provide a new pricing strategy for the container terminal handling charge (THC) of terminals. A dynamic pricing model is established by using the Back Propagation Neural Network (BPNN) algorithm and the Time-Driven Activity-Based Costing method. This pricing strategy can dynamically amend the price of the container THC based on handling demand, recent charge standards per container, and handling time for a particular customer. To some extent, this dynamic pricing strategy can provide a valuable reference for terminals in pricing decisions. This case study implemented the dynamic pricing model at the Shanghai ShengDong International Container Terminal, one of the largest container terminals in China. The results show that this pricing model dynamically adjusts the container THC depending on customers’ handling conditions. Besides, this dynamic pricing model is more precise than the traditional contractual pricing used at the terminal. Compared with the BPNN algorithm, the optimized BPNN algorithm has faster convergence speed and less learning error. Moreover, this pricing method is universally applicable to the container THC pricing problem of most terminals.

Design of Container Terminal Handling System Based on Index Forecast and Economic Evaluation

Huang, X.-L.; Dai, X.-M.; Luo, Y.-Z., and Wang, Y.-W., 2019. Design of container terminal handling system based on index forecast and economic evaluation. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 377–384. Coconut Creek (Florida), ISSN 0749-0208.The construction of a container terminal is a large and long-period investment, and its planning and design involve many factors such as technology, economy and social environment. At the same time, the handling system of a container terminal is a complex discrete dynamic system. Therefore, the design of a container terminal is a complex process requiring repeated modification from proposing alternative schemes to evaluating and optimizing schemes. Modelling and simulation technology can realize the evaluation of various schemes and the determination of the optimal scheme without real operation. Therefore, in order to design the handling system of a container terminal systematically and scientifically to reduce cost and risk, this paper first proposes a comprehensive forecast model to forecast the throughput of a container terminal, and puts forward two handling technology schemes. And then, configuration model of key machinery resources is constructed, and detailed schemes are obtained through simulation experiments with the actual data of container terminal A. Finally, economic evaluation is carried out to examine the financial feasibility and economic rationality of the schemes.

Simulation-based Research on the Allocation of Low-carbon Container Terminal Handling Equipment

At present considerable attention is being given to climate change and global warming. In an increasing globalized world, international transportation produces considerable CO2, which is an essential cause for global warming. As important nodes in international logistics, seaports discharge massive CO2, especially for the handling equipment and vessels. In order to reduce the port carbon emissions influenced by handling facilities, a microscopic handling simulation model is built in this paper for a container terminal with four types of quay cranes and two types of vehicles by using Arena software. Firstly, by changing the type of quay crane and the number of vehicles, the carbon emissions from each facilities are analyzed. Secondly, the carbon emissions of system with five common combinations of quay crane and vehicles are contrasted, and two recommended combinations are provided.

Incorporating container location dispersion into evaluating GCR performance at a transhipment terminal

ABSTRACTThe degree of container dispersion at a transhipment terminal is measured by an index termed container location dispersion, which represents the range, both horizontal and vertical, of all containers for ship loading that are scattered in the yard. First, this paper identifies the impacts of container location dispersion on loading performance with insufficient or sufficient equipment deployment in real-life settings at a terminal. Then, we investigate those scenarios with different levels of interference. This research aims to explore the relationship between the container location dispersion and Gross Crane Rate (GCR) stability at a transhipment terminal and proposes a discrete-event driven simulation model for this purpose. All data in our experiments are extracted from the terminal operation system at a real-life container terminal. It is concluded from a series of experiments that the container location dispersion well captures the overall performance of container terminal handling and can be used for yard template optimization and management. Implications of this investigation are discussed in the context of the yard template design and improvement in the overall performance of a container transhipment terminal.

MULTI AGV SCHEDULING PROBLEM IN AUTOMATED CONTAINER TERMINAL

In this paper we propose a dynamic multi automated guided vehicle (AGV) scheduling method based on the scheduling properties of automated container terminal handling systems. In multi-AGV scheduling, the composition of AGV handling time and the precedence order of certain tasks are major constraints. Taking these into consideration, we design a genetic algorithm (GA) for a dynamic multi-AGV scheduling model to minimize completion time and standard deviation of handling time of quay cranes (QC), and validated the proposed model through numerical experiment. We expect this model to be significant for multi-agent scheduling of discrete production systems.

Design and Evaluation of Multi-storey Lifting Frame Bridge Based Automated Container Terminal Handling System

This paper aims to propose an innovated design of automated container terminal (ACT) handling system, which can help the port to enhance the handling performance of the entire terminal. Different from conventional ACTs that use trucks or automated guided vehicles (AGVs) to transport containers in the terminal, the new handling system takes advantages of multi-storey lifting frame bridge (MLFB), elevators, automated lifting vehicles (ALVs), frame trolleys (FTs) and reach stackers(RSs). The MLFB-based ACT handling system is composed of several subsystems, which have respective lifting frame bridges. Furthermore, an explorative study has been applied to analyze the transportation efficiency within the handling system using a queuing theory model. The results represent significant improvements over that of conventional handling systems.

Optimization for two-stage double-cycle operations in container terminals

The loading of ships while they are unloaded (double cycling) can significantly improve the efficiency of container terminal handling operations. This paper formulated a mixed integer programming model for two-stage double-cycle operation. The quay crane operating time is reduced by optimizing the loading and unloading sequence for the hatches and stacks in each ship-bay. Meanwhile, optimizing the stowage plan of outbound containers in container yards reduces yard crane operating time. A bi-level genetic algorithm was proposed to solve this model, and a lower bound was derived to evaluate the scheduling performance. The numerical experiments indicated that the model and algorithm presented in this paper are effective compared with the lower bound, the currently used method and the method raised in previous studies.

Modeling and Simulation of Automated Container Terminal Operation

The growths of containerization and transporting goods in containers have created many problems for ports, especially with the development of operation equipments produced by port machine manufacturer. A simulation study was proposed to a new container terminal handling technology, which is comprised of twin 40-foot quay cranes, a low bridge allocation system and twin 40-foot rail mounted gantry crane (RMG). The loading and unloading process was analysised and container terminal operation conditions are defined under this operation conditions. Based on eventdriven and virtual reality technology, the handling technology simulation model was established. The operating efficiency of key equipments is calculated during the simulation. Finally, validation is presented through a case study. The method enriched the contents of container terminal handling technology simulation and laid the foundation for further study of automated container terminal.

Critical mass for the development of a new container port in Vlissingen

The economic importance of accommodating container transport and the increasing demand for container-handling capacity are major triggers for conventional ports to develop container terminals. The port of Vlissingen, located between Rotterdam and Antwerp, is aspiring to gain a position in deep-sea container handling. A relevant factor that greatly determines the potential of the port of Vlissingen is the critical mass of the container terminal handling operations to be developed by the port authority. In developing a full deep-sea terminal there is consensus about a critical mass of approximately one million TEU with possibilities to extend to at least two million TEU. In its role as a satellite port for Antwerp, a critical mass of 350 000 TEU could be sufficient. In attracting sufficiently large container flows to Vlissingen, the competitive strength of the port plays an important role. Our contribution in this article lies in the analysis of the optimal size of container port development. The competitive position of the proposed container port of Vlissingen is analysed by using economic research and forecasting methods, backcasting analysis, and analysis of the competitive position vis-a-vis Antwerp and Rotterdam.

An assessment of port reform in Argentina: outcomes and challenges ahead

During the 1990s Argentina engaged in a process of restructuring and deregulation of its ports. The main objective of the reform was to increase efficiency and service quality and to reduce the size and role of the public sector in ports. Overall, it was expected to allow the transport sector to adjust its capacity to demand changes and to facilitate international trade while contributing to the reduction of the fiscal burden of this sector. This paper shows that the reform caused significant efficiency gains, allowing a 50% drop in container terminal handling price within five years in the most important ports. The paper also identifies outstanding issues that could impact the long-run sustainability of the gains achieved. Among these issues, emphasis is given to recent horizontal and vertical mergers in the Port of Buenos Aires and their consequences in terms of actual competition and access regulation.