Container Throughput Research Articles

Collaborative Optimization of Yard Crane Deployment and Inbound Truck Arrivals with Vessel-Dependent Time Windows

Due to mega-ships, increasing container throughput, and nonuniform truck arrivals, many container terminals face challenges of unbalanced workloads of yard equipment, shortage of equipment resources in peak hours, and congestion problem. To solve such issues, we propose a mixed-integer bilevel programming model to optimize the vessel-dependent time windows for inbound trucks and yard crane deployment simultaneously. In the proposed bilevel model, the upper level aims to minimize the total truck waiting time at the container terminal gate and yard, while the lower level is formulated to minimize the total workload overflow to next shift in the whole container yard. The optimal yard crane deployment obtained in the lower level will transfer to the upper level problem to determine the waiting time of trucks in the yard and then affect the truck arrivals pattern. To solve the model, a hybrid algorithm—called hybrid genetic algorithm, based on collective decision optimization—is put forward by combining the genetic algorithm and the collective decision optimization algorithm. Numerical experiments are conducted to validate the proposed approach is effective to simultaneously flatten truck arrivals and improve the efficiency of yard cranes. The proposed approach can significantly reduce container terminals’ truck waiting time.

Development of a Machine Learning-Based Framework for Predicting Vessel Size Based on Container Capacity

Ports are important hubs in logistics and supply chain systems, where the majority of the available data is still not being fully exploited. Container throughput is the amount of work done by the TEU and the ability to handle containers at a minimal cost. This capacity of container throughput is the most important part of the scale of services, which is a crucial factor in selecting port terminals. At the port container terminal, it is necessary to allocate an appropriate number of available quay cranes to the berth before container ships arrive at the port container terminal. Predicting the size of a ship is especially important for calculating the number of quay cranes that should be allocated to ships that will eventually dock at the port terminal. Machine learning techniques are flexible tools for utilizing and unlocking the value of the data. In this paper, we used neighborhood component analysis as a tool for feature selection and state-of-the-art machine learning algorithms for multiclass classification. The paper proposes a novel two-stage approach for estimating and predicting vessel size based on container capacity. Our proposed approach revealed seven unique features of port data, which are the essential parameters for the identification of the vessel size. We obtained the highest average classification accuracy of 97.6% with the linear support vector machine classifier. This study paves a new direction for research in port logistics incorporating machine learning.

Club convergence and allometry in Chinese mainland coastal container ports

Research on container port development is of great theoretical and practical significance because of the port's crucial role in the local economic system and regional transportation system. Understanding the development dynamics of container ports is important when formulating related operational and management strategies in the future. Hence, this article investigates the club convergence of 25 coastal ports in mainland China spanning from 2006 to 2020 in terms of container throughput, which signals the development of a port to some extent. The study finds three clubs in China's coastal container port system, with various transitional behaviors indicating that the whole system has shown a tendency toward a slight centralization since 2013 and that small and medium-sized ports are regressing. On this basis, analysis on the container port system is conducted by employing an allometric model. The result shows that allometric relations indeed exist not only among three factors linked with port development, namely, port container throughput, port city's import-export volume, and port maritime connectivity, but also among the three port clubs. Moreover, the allometric relations slightly differ and vary over time for the three port clubs, thus implying that large ports may have different growth mechanisms from small and medium-sized ports. Based on the findings mentioned above, several general policy implications are proposed, which may impact port production, construction, and operations management in mainland China.

Comparison of Different Approaches of Machine Learning Methods with Conventional Approaches on Container Throughput Forecasting

Container transportation is an important mode of international trade logistics in the world today, and its changes will seriously affect the development of the international market. For example, the COVID-19 pandemic has added a huge drag to global container logistics. Therefore, the accurate forecasting of container throughput can make a significant contribution to stakeholders who want to develop more accurate operational strategies and reduce costs. However, the current research on port container throughput forecasting mainly focuses on proposing more innovative forecasting methods on a single time series, but lacks the comparison of the performance of different basic models in the same time series and different time series. This study uses nine methods to forecast the historical throughput of the world’s top 20 container ports and compares the results within and between methods. The main findings of this study are as follows. First, GRU is a method that can produce more accurate results (0.54–2.27 MAPE and 7.62–112.48 RMSE) with higher probability (85% for MAPE and 75% for RMSE) when constructing container throughput forecasting models. Secondly, NM can be used for rapid and simple container throughput estimation when computing equipment and services are not available. Thirdly, the average accuracy of machine learning forecasting methods is higher than that of traditional methods, but the accuracy of individual machine learning forecasting methods may not be higher than that of the best conventional traditional methods.

Hierarchical container throughput forecasting: The value of coherent forecasts in the management of ports operations

A port's overall container throughput (CT) data can be arranged into diverse hierarchical dimensions (cross-sectional or temporal). Wherein the forecasts are generated using the “horses-for-courses” approach to achieve maximum accuracy at the base level. Generally, these base forecasts do not add up across temporal or cross-sectional hierarchies and give rise to the issue of forecast “incoherence”. Accordingly, the present study extends the idea of utilising optimal forecast reconciliation techniques in CT forecasting. However, the frameworks of these techniques do not allow for their interaction across different dimensions of hierarchy present in CT data. Hence, the paper addresses this problem by proposing a cross-temporal forecast reconciliation framework.Further, monthly CT data from the Port of Los Angeles is used for empirical analysis. It is observed that the proposed framework, with exponential smoothing as the base forecasting method, can provide coherent forecasts at all levels and hierarchical dimensions while simultaneously improving the accuracy of the forecasts. Further, the study also devises a mechanism to evaluate the efficacy of the proposed framework in providing decision support for various port operations (the requirement for terminal ground slots and gantry cranes). The results indicate that improvements could be made in port operations decisions using the proposed framework.

Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic

The COVID-19 pandemic had a significant impact on container transportation. Accurate forecasting of container throughput is critical for policymakers and port authorities, especially in the context of the anomalous events of the COVID-19 pandemic. In this paper, we firstly proposed hybrid models for univariate time series forecasting to enhance prediction accuracy while eliminating the nonlinearity and multivariate limitations. Next, we compared the forecasting accuracy of different models with various training dataset extensions and forecasting horizons. Finally, we analysed the impact of the COVID-19 pandemic on container throughput forecasting and container transportation. An empirical analysis of container throughputs in the Yangtze River Delta region was performed for illustration and verification purposes. Error metrics analysis suggests that SARIMA-LSTM2 and SARIMA-SVR2 (configuration 2) have the best performance compared to other models and they can better predict the container traffic in the context of anomalous events such as the COVID-19 pandemic. The results also reveal that, with an increase in the training dataset extensions, the accuracy of the models is improved, particularly in comparison with standard statistical models (i.e. SARIMA model). An accurate prediction can help strategic management and policymakers to better respond to the negative impact of the COVID-19 pandemic.

Impact of local port disruption on global container trade: An example of stressing testing Chinese ports using a D-vine copula-based quantile regression

In the shipping market, ports are increasingly interconnected in the global container network. A port disruption in one place can lead to a chain effect on subsequent ports. Nowadays, China's container port throughput accounts for around 30% of the global port throughput, manifesting its essential role in the global container trade system. Therefore, any disruption in Chinese ports could have a cascading effect on the world container trade. This study explores the potential impact on the container main trading lanes given a stress scenario for a number of Chinese ports. We are particularly interested in the degree of risk spillover to the different trading lanes. The stress scenario used in this study is a severe drop in the container throughput in major Chinese ports. This drop will be reflected in quantiles of the respective throughput distributions that are close to zero. D-vine copula-based quantile regression is used to measure the throughput impact of stressed Chinese ports as covariates on other response container trading routes. Copula is a powerful tool to measure the tail dependence structure between throughputs of major Chinese ports and major container trading routes. Results show that port disruptions in China severely impact the major container trading routes. The Asia to Sub Saharan Africa route is extremely vulnerable to downside risk given any port disruption event in China. We have also compared the results with the historical trade and port call data. The results provide significant implications for container shipping risk management.

Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development

Accurate forecasts of containerised freight volumes are unquestionably important for port terminal operators to organise port operations and develop business plans. They are also relevant for port authorities, regulators, and governmental agencies dealing with transportation. In a time when deep learning is in the limelight, owing to a consistent strip of success stories, it is natural to apply it to the tasks of forecasting container throughput. Given the number of options, practitioners can benefit from the lessons learned in applying deep learning models to the problem. Coherently, in this work, we devise a number of multivariate predictive models based on deep learning, analysing and assessing their performance to identify the architecture and set of hyperparameters that prove to be better suited to the task, also comparing the quality of the forecasts with seasonal autoregressive integrated moving average models. Furthermore, an innovative representation of seasonality is given by means of an embedding layer that produces a mapping in a latent space, with the parameters of such mapping being tuned using the quality of the predictions. Finally, we present some managerial implications, also putting into evidence the research limitations and future opportunities.

Analyzing the international connectivity of the major container ports in Northeast Asia

PurposeThis study aims to identify the characteristics of the maritime shipping network in Northeast Asia as well as compare the level of port connectivity among these container ports in the region. In addition, this study analyses the change in role and position of 20 ports in the region by clustering these ports based on connectivity index and container throughput and route index.Design/methodology/approachThis study employs Social Network Analysis (SNA) to delineate the international connectivity of major container ports in Northeast Asia. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify each port's connectivity index and container throughput index, and the resulting indexes are employed as the basis to cluster 20 major ports by fuzzy C-mean (FCM).FindingsThe results revealed that Northeast Asia is a highly connected maritime shipping network with the domination of Shanghai, Shenzhen, Hong Kong and Busan. Furthermore, both container throughput and connectivity in almost all container ports in the region have decreased significantly due to the coronavirus disease 2019 (COVID-19) pandemic. The rapid growth of Shenzhen and Ningbo has allowed them to join Cluster 1 with Shanghai while maintaining high connectivity, yet decreasing container throughput has pushed Busan down to Cluster 2.Originality/valueThe originality of this study is to combine indexes of SNA into connectivity index reflecting characteristics of the maritime shipping network in Northeast Asia and categorize 20 major ports by FCM.

KONTEYNER TERMİNALİ YERLEŞİM PLANININ METOT ETÜDÜ İLE İNCELENMESİ

Container terminals are complex facilities where many operations take place simultaneously. In order to carry out these operations efficiently, terminal layout plans should be designed according to the physical and characteristic features of the terminal. Layout plans can be reconstructed in cases where the existing terminal is expanded or in cases where it is required to increase the productivity of the terminal to manage the increasing container throughput. For strategic decisions and long-term planning, knowing how the layout design revisions would affect the terminal's operation and productivity is critical for terminal managers. One of the most effective methods for increasing productivity in enterprises is the work study. In this direction, to improve terminal productivity, the analysis of the terminal is carried out by using the method study, which is one of the work study techniques. Alternative layouts have been proposed for the terminal layout after a detailed analysis and critical evaluation of terminal movements.

Comparison of hybrid and non-hybrid models in short-term predictions on time series in the R development environment

Because many time series usually contain both linear and nonlinear components, a single linear or nonlinear model may be insufficient for modeling and predicting time series. Therefore, estimation results are tried to be improved by using collaborative models in time series short-term prediction processes. In this study, the performances of both stand-alone models and models whose different combinations can be used in a hybrid environment are compared. The mean absolute percentage error (MAPE) metric values obtained from two different categories were evaluated. In addition, the estimation performances of three different approaches such as equi-weighted (EW), variable-weighted (VW) and cross-validation-weighted (CVW) for hybrid operation were also compared.
 The findings on the container throughput forecast of the Airpassengers dataset reveal that the hybrid model's forecasts outperform the non-combined model.

Recent Patterns of Competition and Co-operation among Major Container Ports in Asia: An Implication for Busan

Seaports are crucial for regional development as they offer an employment to adjacent societies through industrialisation and value-added services. Since Asia has become a manufacturing hub for the world economy with a large volume of exports and imports, this paper investigates the trading pattern of container line services for three major ports in Asia: that is, Shanghai, Singapore and Busan. Ports compete and co-operate with neighbouring ports in the region to generate the greater throughputs and subsequently creat economic values. Utilising the liner services data from AXS Marine’s Alphaliner database, this study proposes an inter-dependency ratio of container ports in a way to understand the degree of co-opetition among the ports from a foreland perspective. The findings suggest that, the Singaporean port generates around 40% of its volume without depending on Shanghai and Busan, while the Busan port relies about 90% on Shanghai and Singapore to generate its volume. The Port of Busan is desperately needed to diversify the liner services/routes portfolio and expand its self-dependency to become a transhipment hub located in the Far East. This paper concludes by identifying several challenges and opportunities for the Port of Busan as a specified example. Big data in maritime industry could provide a plateform for decision support systems to port operators, managers and policy-makers so as to uncover the market’s gaps and needs, thus increasing port market shares and retaining the greater container throughputs.

Economic Efficiency of the Korean Container Terminals: A Stochastic Cost Frontier Approach

Purpose - Recent issues such as vessel enlargement, strengthening of environmental regulations, and port smartization are expected to increase costs and intensify competition in the port industry. In the new normal era, when external growth has reached its limit, the efficient operation of ports is becoming indispensable for achieving sustainable growth. This study aims to identify the determinants of inefficiency by examining the cost structure and efficiency of container terminals in Korea and furthermore propose the political implications to derive the maximization of efficiency.
 Design/methodology - This study estimates the cost function of container terminal operators and identifies the efficiency of container terminals using stochastic cost frontier (SCF) in the first stage. In the second step, the SCF results are compared with the data envelopment analysis (DEA). Last, this paper proposes efficiency determinants on container terminal operation to establish appropriate strategies. Out of the 29 container terminal operators in South Korea, 13 operators participated in the survey. The translog cost function was estimated utilizing a total of 116 observations collected over the 2007-2017 period.
 Findings - Empirical analysis shows that economies of scale exist in Korea’s container ports, which provides a rationale for the government’s policy to establish the global terminal operator by integrating small terminal operators to enhance competitiveness. In addition, as a result of the determinants analysis, container throughput, weight of direct employment costs, and labour cost share have positive effects on improving cost efficiency, while inefficiency increases as the length of quay increases. More specifically, cost efficiency improves as the proportion of direct employment costs to outsourcing service costs increases.
 Originality/value - This study contributes to analyzing the inefficiency factors of container terminals through efficiency analysis with respect to a cost function. In addition, this study proposes the practical and political implications, such as establishing a long-term manpower pool, the application of the hybrid liner terminal system, and the construction of a statistical data system, to improve the cost inefficiency of terminal operators.

Ship emissions in hotelling phase and loading/unloading in Southeast Asia ports

The rapid increase in ship calls and container throughput in Southeast Asia's container port system severely affects the regional environment. This study investigates ship emissions in the hotelling phase, relating mainly to loading and unloading activities at berths. Ship emissions at berths were calculated using the bottom-up approach based on the number of ship calls, power of auxiliary engines, load factor, emissions factor, and in-port hotelling duration of the top ten ports in the region from 2016 to 2020. With a high density of ships and continuity in loading/unloading operations, the Southeast Asia container port system, with several large container ports such as Singapore, Port Kelang, and Tanjung Pelepas, produces enormous emissions, causing air pollution. Further, increased volume of containers and ship calls leads to high pollution in the Southeast Asia container port system, whereas enhanced loading/unloading productivity of ports reduces emissions per TEU.

Seaport throughput forecasting and post COVID-19 recovery policy by using effective decision‐making strategy: A case study of Vietnam ports

This study deals with the dynamic interactions between seaports and decision-making strategy for seaport operations by utilizing four-dimensional fractional Lotka-Volterra competition model under frequently disrupted by time-delay factor. Nonlinear analysis methods, including equilibrium analysis, stability evaluation, and time series investigation, are intensely explored to describe the cooperation and competition dynamics in maritime logistics. The dynamical analysis indicates that the port competition system shows a complex and highly nonlinear behaviour, notably illustrating unstable equilibria and even chaotic phenomena. Besides, nonlinear dynamical interactions in seaport management have been analysed by exploiting fractional calculus (FC) and system dynamics theory. Novel multi-criteria decision-making strategies realized by the neural network prediction controller (NNC) and adaptive fractional-order super-twisting sliding mode control (AFOSTSM) have been presented for dealing with throughput dynamics under parametric perturbations and external disturbances. Particularly, the active control algorithms are implemented to ensure the recovery strategy for throughput growth of Vietnam ports in the post-coronavirus (COVID-19) pandemic era. The case study has confirmed the efficacy of the proposed strategy by using system dynamics and control theory. The simulation results show that the average growth rates of container throughput can be ensured up to 7.46% by exploiting resilience management scheme. The presented method can be also utilized for providing managerial insights and solutions on efficient port operations. In addition, the control strategies with neural network forecasting can help managers obtain timely and cost-effective decision-making policy for port sustainability against unprecedented impacts on global supply chains related to COVID-19 pandemic.

Dynamics of the Asian shipping network in adjacent ports: Comparative case studies of Shanghai-Ningbo and Hong Kong-Shenzhen

The evolution of adjacent container ports enhances the Asian port system. This study analyzes the dynamics of the Asian shipping network in adjacent ports, a case study of Shanghai-Ningbo and Hong Kong-Shenzhen ports. The competition between the adjacent port groups, assessed by offset growth rate is analyzed using the Shift-Share model. The dynamics of port centrality and connection are then analyzed using the complex network index. Additionally, the dynamics of the external links of the ports in the network are analyzed using an interlocking network model. The results highlight that the container throughput of Shanghai and Ningbo ports increases simultaneously, while the container throughput of Hong Kong and Shenzhen presents the trade-off phenomenon. The two adjacent port groups generate geographically dispersed networks of container flow in the Asian region. International route patterns and port coverages of Shanghai and Ningbo have a high degree of similarity, while that of Hong Kong and Shenzhen are different. Interestingly, Shanghai-Ningbo demonstrate a dramatic growth pattern, whereas Hong Kong-Shenzhen exhibit a shifting pattern. The findings of this study can be used by the sector to understand the evolution process of the port system and provide useful guidance for adjacent port groups to improve their efficiency.

An environmental management system in seaports: evidence from Malaysia

ABSTRACT Seaports face significant environmental challenges in their role as hubs for logistics activities, particularly since ships are significant contributors to energy consumption, global warming and air and marine pollution. The environmental pressures facing ports has led to the emergence of the green port concept. Port Klang is the largest port in Malaysia, characterised not only by significant container throughput, but also by significant congestion issues, emissions and pollution. These characteristics led to the selection of Port Klang, the nation’s major seaport, as a case study for this analysis of the variables most relevant in the effective implementation of the green port concept and subsequent research into how this green seaport concept influences a seaport’s future competitiveness. To achieve these aims, the Analytic Hierarchy Process (AHP) has been employed. The findings of the analysis suggest that environmental quality, the use of energy and resources, waste handling and habitat quality and greenery are the critical influential factors required for establishing green port hub status and that then port competitiveness is most significantly affected by green operations, sustainable equipment, resources management and cost efficiency at seaports.

A New Container Throughput Forecasting Paradigm under COVID-19

COVID-19 has imposed tremendously complex impacts on the container throughput of ports, which poses big challenges for traditional forecasting methods. This paper proposes a novel decomposition–ensemble forecasting method to forecast container throughput under the impact of major events. Combining this with change-point analysis and empirical mode decomposition (EMD), this paper uses the decomposition–ensemble methodology to build a throughput forecasting model. Firstly, EMD is used to decompose the sample data of port container throughput into multiple components. Secondly, fluctuation scale analysis is carried out to accurately capture the characteristics of the components. Subsequently, we tailor the forecasting model for every component based on the mode analysis. Finally, the forecasting results of all the components are combined into one aggregated output. To validate the proposed method, we apply it to a forecast of the container throughput of Shanghai port. The results show that the proposed forecasting model significantly outperforms its rivals, including EMD-SVR, SVR, and ARIMA.

Measuring the impact of an exogenous factor: An exponential smoothing model of the response of shipping to COVID-19

Following the outbreak of the COVID-19 pandemic, various lockdown strategies restrained global economic growth bringing a significant decline in maritime transportation. However, the previous studies have not adequately recognized the specific impacts of COVID-19 on maritime transportation. In this study, a series of analyses of the Baltic Dry Index (BDI), the China Coastal Bulk Freight Index (CCBFI) and of container throughputs with and without the impact of COVID-19 were carried out to assess changing trends in dry bulk and container transportation. The results show that global dry bulk transportation was largely affected by lockdown policies in the second month during COVID-19, and BDI presented a year-on-year decrease of approximately 35.5% from 2019 to 2020. The CCBFI showed an upward trend in the second month during COVID-19, one month ahead of the BDI. The container throughputs at Shanghai Port, the Ports of Hong Kong, the Ports of Singapore and the Ports of Los Angeles from 2019 to 2020 presented the largest year-on-year drops of approximately 19.6%, 7.1%, 10.6% and 30.9%, respectively. In addition, the authors developed exponential smoothing models of BDI, CCBFI, and container transportation, and calculated the percentage prediction error between the observed and predicted values to examine the impact of exogenous effects on the shipping industry due to the outbreak of COVID-19. The results are consistent with the conclusions obtained from the comparison of BDI, CCBFI, and container transportation during the same period in 2020 and 2019. Finally, on the basis of the findings, smart shipping and special support policies are proposed to reduce the negative impacts of COVID-19.

Monte Carlo Tree Search algorithm for the receiving containers intelligently problem among container shipping terminals

With the development of economic globalization, shipping exchanges between countries are rapidly increasing and the container throughput of shipping ports has increased quickly, and the operation of most seaport terminals in China has reached a bottleneck which puts forward new requirements for the efficiency of container shipping terminal operations. Therefore, locating for export containers intelligently is of great significance for the development of terminals. This paper focuses on receiving containers intelligently, and establishes the export container locating model based on the principle of actual shipping terminal operations. The Monte Carlo Tree Search algorithm for export container locating problem was proposed and constructed. After examples, the algorithm can effectively solve the problem that meet the constraints, which further proves the practicability of the algorithm and the correctness of the model. The research shows that the method of locating for export container based on Monte Carlo Tree Search algorithm can effectively solve the problem and maintain the green, energy-saving and sustainable development of the shipping terminal. The ideas and methods have certain academic value and reference significance for other NP-Hard problems.