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Container terminals are essential for handling the growing container traffic resulting from global trade. Operation managers must either increase the number of handling components or improve the efficiency of existing equipment to meet the increasing demand. This paper employs the response surface methodology to obtain the optimal number of yard trucks (YTs) and rubber tyred gantry (RTG) cranes. First, a two-level full factorial design was employed to fit first-order regression models and assess the effects of the variables. Then, the second-order models were constructed with a central composite design to further explore the influence of key parameters on the response variables. The Derringer-Suich method was applied to identify the optimal levels of performance criteria such as operation time and cost. According to the implementation results, the average operation time was reduced from 303.25 minutes to 286.25 minutes, reflecting a 5.6 % improvement in operational efficiency. Furthermore, the total operating costs decreased by $315.72, from $2,331.59 to $2,015.87. This 13.5 % improvement highlights the operational cost advantages achieved by optimizing resources. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, severe demurrage in shipping and container terminals has led to a global logistics crisis and supply chain disruptions. Although the situation is gradually stabilizing, discussions persist on strategies to reduce volatility in container freight transportation. Demurrage imposes unnecessary costs, delays deliveries, and impedes the efficiency of container terminals. This study analyzes the factors influencing demurrage from the perspective of logistics companies, specifically, based on the characteristics of logistics companies and the attributes of the cargo owners they serve. Additionally, it explores the impact of demurrage levels on firm performance and investigates how this relationship may be moderated by container terminal characteristics. The empirical findings indicate that advancements in information systems within logistics companies, along with improved collaboration and contract compliance among cargo owners, can significantly reduce demurrage. This reduction in demurrage can enhance firm performance. Furthermore, credibility management and digitization of shipping documents also play a crucial role in minimizing demurrage. Building on previous research and empirical insights, this study emphasizes the importance of sustained long-term investment and policy support from the government for the digital transformation of logistics processes, complementing the efforts of both logistics companies and cargo owners. [ABSTRACT FROM AUTHOR]

Large vessels that call at European ports will have to pay for their CO2 emissions from transporting cargo that enters or goes from a European port since January 2024. The costs will increase with increasing global trade. This results in a higher pollution level, including greenhouse gas (GHG) emissions like CO2. Based on the gravity model embedded in a global supply chain, we developed a model to evaluate maritime transport pollution in case a new, sufficiently large container port becomes operational. Additionally, we consider how lousy railway connections to European customers increase transportation costs and pollution. The approach to the well-connected sequences of gravity models in the intercontinental maritime chains evaluates the differences in quantities of cargo between ports when a new port is opened, and the waiting time does not change. We also highlight that poor rail connections can reduce this positive effect. We showed how it is possible to estimate the optimal capacity of a new port with a multi-level gravity model and how this would affect the pollution around the port and on the routes from the port to the final consumers. [ABSTRACT FROM AUTHOR]

In line with the development of green port terminals and the necessity of environmentally friendly efficient use of handling and transportation equipment, criteria such as emission reduction, energy minimization, and efficiency in a container terminal are prominent issues, which in recent years, have been the main concern of container terminal managers in developing terminal operating systems. Therefore, in this study, a bi-objective mixed integer mathematical programing model of integrated scheduling for ships' loading and unloading operation sequencing in container terminals is proposed, which considers the minimization of containers' flow time, trucks' emission, and energy consumption as well as sharing trucks among quay cranes and decreasing their empty trips. In this model trucks' technical specifications and polluting levels are input parameters for estimating emission and energy consumption of operation, which consequently leads to two uniform parallel machine scheduling models. To derive the exact solution of the model, the epsilon-constraint method is applied. Nevertheless, due to the problem's NP-hardness attribute, two variants of non-dominated sorting genetic algorithm and multiobjective particle swarm optimization metaheuristic algorithms are proposed to identify the approximate Pareto front of solutions. In large-size problems, these algorithms outperform the epsilon-constraint method by finding acceptable results in a reasonable time. This model, which could be embedded in terminal operating software, not only yields a practical optimum operational sequence but also measures the amount of energy and emission from trucks during loading and unloading operations. The results, which utilized the data collected from Shahid Rajaee port in southern Iran, indicate that this practical model as compared to the current procedure in the terminal, results in a significant improvement in energy and emission reduction and terminal flow time. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, severe demurrage in shipping and container terminals has led to a global logistics crisis and supply chain disruptions. Although the situation is gradually stabilizing, discussions persist on strategies to reduce volatility in container freight transportation. Demurrage imposes unnecessary costs, delays deliveries, and impedes the efficiency of container terminals. This study analyzes the factors influencing demurrage from the perspective of logistics companies, specifically, based on the characteristics of logistics companies and the attributes of the cargo owners they serve. Additionally, it explores the impact of demurrage levels on firm performance and investigates how this relationship may be moderated by container terminal characteristics. The empirical findings indicate that advancements in information systems within logistics companies, along with improved collaboration and contract compliance among cargo owners, can significantly reduce demurrage. This reduction in demurrage can enhance firm performance. Furthermore, credibility management and digitization of shipping documents also play a crucial role in minimizing demurrage. Building on previous research and empirical insights, this study emphasizes the importance of sustained long-term investment and policy support from the government for the digital transformation of logistics processes, complementing the efforts of both logistics companies and cargo owners.

Container terminals are among the most critical parts of transportation systems. Reducing the ship turnaround time increases terminal efficiency and enhances global trade volumes. Such time reductions can be achieved by improving the operational efficiency of major resources at container terminals. Quay crane operating strategies are crucial for assessing the performance of container terminals. Simulation models are effective and reliable methods for interpreting and improving complex systems. This study proposes simulation models that include container loading and unloading tasks in marine container terminals implementing single-cycling and double-cycling strategies. Double-cycling is a quay crane operating strategy that attempts to improve container handling efficiency. The system was modeled using ARENA simulation software. The implementation results of the single-cycling and double-cycling strategies were compared in terms of the performance criteria, such as the utilization rates of the quay crane and yard trucks, ship turnaround time, and operating cost.

Abstract The extension of existing container terminals or the creation of new ones introduces new logistical challenges, including topographic issues and increased distances between the quays and storage yards located several kilometers away from the quay (dry port). These challenges are complex to evaluate analytically and directly impact the acceleration, deceleration, and average speed of a truck which in turn affect the productivity and synchronization of the overall terminal logistics. This paper proposes a transportation simulation model that incorporates detailed descriptions of the topographical and geometrical restrictions. Our simulation model evaluates various scenarios for container transportation logistics, including varying road design terminals and truck fleet size to enhance productivity. A case study from a potential container terminal on Canada’s St. Lawrence River is used to demonstrate the simulation model. Several scenarios with different designs are tested and the simulation provides numerical results for supporting decision makers.

The extension of existing container terminals or the creation of new ones introduces new logistical challenges, including topographic issues and increased distances between the quays and storage yards located several kilometers away from the quay (dry port). These challenges are complex to evaluate analytically and directly impact the acceleration, deceleration, and average speed of a truck which in turn affect the productivity and synchronization of the overall terminal logistics. This paper proposes a transportation simulation model that incorporates detailed descriptions of the topographical and geometrical restrictions. Our simulation model evaluates various scenarios for container transportation logistics, including varying road design terminals and truck fleet size to enhance productivity. A case study from a potential container terminal on Canada's St. Lawrence River is used to demonstrate the simulation model. Several scenarios with different designs are tested and the simulation provides numerical results for supporting decision makers. [ABSTRACT FROM AUTHOR]

In this study, the static and dynamic performance efficiencies of container terminals are analyzed and compared for the main container terminals in China and Korea. The static performance efficiency is calculated using the Super-SBM model based on slack variables at the micro-level. An analysis on the dynamic performance efficiency is conducted with the Malmquist index method. The factors of scale and technology of container terminals are mainly taken into account to explore the performance efficient improvement path of container ports. We obtained the following conclusions: (1) The container terminals in Korea show a similar performance efficiency level to the terminals in China, and their performance efficiency is an overall upward trend over the past five years. (2) The main reason for inefficiency in the container terminals in China and Korea is predominantly scale inefficiency. (3) Boosting the automation degree does not have a completely positive impact on the efficiency of the terminal. (4) In 2019–2023, the technical progress index of all container terminals in China and Korea showed a decreasing trend, leading to performance inefficiency of the container terminals. [ABSTRACT FROM AUTHOR]

Background: We focus on the inland rail forwarding of import containers from a marine terminal. Specifically, we present a discrete-event simulation study related to container-loading operations by train, evaluating different train departure policies within a predetermined schedule based on the capacity of the rail network. The aim is threefold: manage more containers shipped by train, improve terminal operational efficiency, and increase the rail share. Methods: The proposed procedures take full advantage of the digitization and visualization of data currently present in the terminal with the aim of improving the performance indices of interest, thereby increasing the terminal's operational efficiency. We evaluate the medium- and long-term impact of alternative strategies on container dwell times and the possible increase in the number of containers shipped by train. Results: The computational tests are performed with data from a terminal in the port of Genoa (Italy). The results show the relationships between train departure management policies and train departure distributions. The number of departing trains, cancelled trains, and trains departing with load percentages below 70% is studied. Average loads per train and estimated delays are also analyzed. Conclusions: It is noted that the results, which can be obtained with data from any terminal, are of great importance for optimizing operational management, offering practical solutions to improve efficiency and reduce container downtime. [ABSTRACT FROM AUTHOR]

Increasing freight volumes and challenging environments in seaports and container terminals worldwide require streamlined and reliable operations. Digital twins are seen as important drivers of the digitalization in seaports by providing a basis for higher transparency, control and data-driven decision making. In this context, however, the concept is rarely studied, and implementation issues are not comprehensively discussed. The paper presents an exploratory study of digital twins in seaports based on a literature review and case studies. The analysis reveals a standardization deficit for digital twin implementations, an inflationary and improper use of the term digital twin, and fields of research that need to be explored further. The application of optimization methods and the integration of simulation-based optimization in the field of seaports and container terminals is examined, due to its relevance for digital twins. Important lessons learned can be taken from the most advanced implementations, integrating simulations and emulations with optimization methods. An in-depth examination of multiple case studies and discussions with global port leaders yields valuable perspectives on the varied levels of digital twin implementations being applied today, including insights into the most advanced implementations currently being used in ports and container terminals. As a result of the analyses conducted, various research directions and a research agenda are presented. [ABSTRACT FROM AUTHOR]

This paper introduces a novel strategy aimed at enhancing productivity and minimizing non-productive movements within container terminals, specifically focusing on container yards. It advocates for the implementation of a digital twin-based methodology to streamline the operations of stacking cranes (SCs) responsible for container handling. The proposed approach entails the creation of a virtual container yard that mirrors the physical yard within a digital twin system, facilitating real-time observation and validation. In addition, this article demonstrates the effectiveness of using a digital twin to reduce unproductive movements and improve productivity through simulation. It defines various operational strategies and takes into account different yard contexts, providing a comprehensive understanding of optimisation possibilities. By exploiting the capabilities of the digital twin, managers and operators are provided with crucial information on operational dynamics, enabling them to identify areas for improvement. This visualisation helps decision-makers to make informed choices about their stacking strategies, thereby improving the efficiency of overall container terminal operations. Overall, this paper present a digital twin solution in container terminal operations, offering a powerful tool for optimising productivity and minimising inefficiencies. [ABSTRACT FROM AUTHOR]

In recent years, despite a decline in international trade and disruptions in the supply chain caused by COVID-19, the main container terminals in Latin America and the Caribbean (LAC) have increased their container volumes. This growth has necessitated significant adaptations by seaports and their authorities to meet new demands. Consequently, there has been a focused analysis on the performance, efficiency, and competitiveness, particularly their most relevant logistical aspects. In this paper, a multi-objective hybrid approach was employed. The Principal Component Analysis (PCA) technique was combined with the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) to rank LAC container terminals and identify operational criteria affecting efficiency. The analysis considered all input variables (berth/quay length, quay draught, yard area, number of quay cranes (portainer), number of yard cranes (trastainer), reachstacker, multicranes, daily montainer movement capacity, number of station reefer container type, number of terminals, and distance to the Panama Canal) and output variable (port performance expressed in TEUs from 2014 to 2023). The results revealed noteworthy findings for several terminals, particularly Colón, Santos, or Cartagena, which stands out as the main container port in LAC not only in annual TEUs throughput, but also in resource utilization. [ABSTRACT FROM AUTHOR]

Container terminals are among the most critical parts of transportation systems. Reducing the ship turnaround time increases terminal efficiency and enhances global trade volumes. Such time reductions can be achieved by improving the operational efficiency of major resources at container terminals. Quay crane operating strategies are crucial for assessing the performance of container terminals. Simulation models are effective and reliable methods for interpreting and improving complex systems. This study proposes simulation models that include container loading and unloading tasks in marine container terminals implementing single-cycling and double-cycling strategies. Double-cycling is a quay crane operating strategy that attempts to improve container handling efficiency. The system was modeled using ARENA simulation software. The implementation results of the single-cycling and double-cycling strategies were compared in terms of the performance criteria, such as the utilization rates of the quay crane and yard trucks, ship turnaround time, and operating cost. [ABSTRACT FROM AUTHOR]

In a container terminal, the length of time that containers remain in the yard, known as Container Dwell Time (CDT), is considered one of the significant operational indicators due to its direct correlation with terminal productivity and efficiency. However, due to complex processing procedure and the involvement of various logistics stakeholders, CDT is subject to high uncertainty, making it more difficult for the terminal to manage. To address this issue, this paper presents a comprehensive framework to identify the Key Factors (KFs) influencing prolongation of CDT for import containers. In order to elucidate abnormal cases from dataset which contains yard loading information, the Process Mining (PM) method is utilized. Subsequently, XAI has been utilized to identify the KFs of import CDT. To reflect reality as closely as possible, we collected event data from a container terminal in Busan, Korea. Based on experiments, the KFs thus identified were: 1) Temperature, 2) Weight of container, 3) Voyage number of container 4) Block, 5) Shipping company, and 6) Month of discharging. To conclude, we formulated domain knowledge-based interpretations of the six most influential KFs.

The years 2021 and 2022 showed that maritime logistics are prone to interruptions. Ports especially turned out to be bottlenecks with long queues of waiting vessels. This leads to the question of whether this can be (at least partly) mitigated by means of better and more flexible terminal operations. Digital Twins have been in use in production and logistics to increase flexibility in operations and to support operational decision-making based on real-time information. However, the true potential of Digital Twins to enhance terminal operations still needs to be further investigated. A Delphi study is conducted to explore the operational pain points, the best practices to counter them, and how these best practices can be supported by Digital Twins. A questionnaire with 16 propositions is developed, and a panel of 17 experts is asked for their degrees of confirmation for each. The results indicate that today's terminal operations are far from ideal, and leave space for optimisation. The experts see great potential in analysing the past working shift data to identify the reasons for poor terminal performance. Moreover, they agree on the proposed best practices and support the use of emulation for detailed ad hoc simulation studies to improve operational decision-making. [ABSTRACT FROM AUTHOR]

The configuration of the equipment is essential when designing a container terminal. The quay crane (QC) is one of the most important equipment in the container terminal because it decides the capacity of the quayside operation and berth service. A macroscopic simulation model of the quayside operation and berth service in the container terminal is proposed in this study. An integrated berth allocation and QC scheduling strategy is utilised in the simulation. The long-term simulation experiments considering the annual seasonal variation are conducted. And an appropriate configuration of the QCs is selected according to the analysis for key indicators of berth service and quayside operation. The comprehensive analysis indicates the selected configuration is great when the throughput is 2.5 million TEUs and it also can meet the quayside operation when the throughput increases by 20%. [ABSTRACT FROM AUTHOR]

In order to address the dynamic changes in vessel preferences for berth lines caused by the deployment of shore-based power equipment in major ports and the collaborative scheduling problem of berthing and towing assistance, this paper quantifies the environmental costs of pollutants from the main engines of tugs and auxiliary engines of container ships using an environmental tax. Additionally, considering the economic costs such as vessel delay and shore power cable connection, a two-layer mixed-integer linear programming model is constructed using the task sequence mapping method. This model integrates the allocation of continuous berths at container terminals with coordinated towing scheduling for shore power selection. A solution approach is designed by combining the commercial solver (CPLEX) and the immune particle swarm optimization algorithm (IAPSO). The proposed scheme is validated using the example of the Nansha Phase IV Terminal at the Port of Guangzhou. The results show that compared to the traditional first-come-first-served and adjacent scheduling schemes, the collaborative scheduling scheme proposed in this paper reduces the total cost by 21.73%. By effectively utilizing berth resources and shore power equipment while densely arranging collaborative tasks and appropriately increasing the number of tugs, the port can convert the economic cost of leasing a small number of tugs (increased by 10.63%) into environmental benefits (decreased by 33.88%). This approach provides a reference for addressing nearshore pollution emissions in ports. [ABSTRACT FROM AUTHOR]

Container terminals (CTs) play a crucial role within the global supply chain in the context of the global container transportation business. The primary obligation associated with these terminals involves ensuring the punctual execution of primary vessel operations, while strictly adhering to the Estimated Time of Departure (ETD) for vessels. To accomplish this goal, it is crucial to develop rigorous and exact operating strategies. Nonetheless, the accurate prediction of operation times in CT presents a significant challenge due to the concurrent involvement of various Container Handling Equipment (CHE) and the occurrence of unforeseen situations. To address this issue, this study proposes a novel approach called Predictive Discrete Event Simulation (PDES) that utilizes data collected from CT to predict the operation times. The PDES is an advanced approach that builds upon the widely used Discrete Event Simulation (DES) technique in the field of simulations. It is specifically designed to provide precise predictions of the operation times in CTs, where multiple events take place concurrently. The PDES in this paper aims to overcome the shortcomings of current simulation-based approaches for prediction in CT. These approaches often rely on predefined task sequences and assumed time for job handling time of CHE in their scenarios, which can result in reduced accuracy when predicting operation times. Through the resolution of these issues, the proposed PDES exhibits the capacity to improve predictive performance. To enhance the predictive performance of operation times in CTs through PDES, two approaches are introduced. The first approach entails the application of Support Vector Machine (SVM) algorithms for the purpose of predicting operation times. This approach is further augmented by integrating it with DES to improve the accuracy of predictive performance. The second approach involves predicting by simulating real-world operational scenarios in CTs using algorithms for CHE assignment. The predictive performance of the proposed PDES is assessed through the utilization of data gathered from Busan Port Terminal (BPT) in South Korea, demonstrating superior performance compared to alternative prediction approaches.

Each container terminal has attempted to improve its performance through effective strategic and tactical management. Therefore, performance measurement is needed due to the long waiting time of the service process within container terminal operations. We have utilized the system dynamics methodology to assess container terminal performance. Container port enterprises have been influenced by governance, environmental and social components, therefore, the management of those components has significant impacts on the service port's performance. Therefore, the objective of the research is to model the dynamic performance of the container terminal with consideration of environmental, social, and governance components. Furthermore, this dynamic model has presented practical value for increasing the performance of the container terminal operation under capacity constraints. The case study focused on the Indonesia Port Corporation of Jakarta, Indonesia. As a result, the research has identified the influence of container terminal decision variables, such as emissions, employee satisfaction, and good corporate governance, on container terminals, implying their significance in reducing berthing time as the main indicator of container terminal performance. The novelty of this research is to provide a system dynamic model which combines tactical and strategic levels for container terminal operation. [ABSTRACT FROM AUTHOR]

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A container terminal plays a significant role in global supply chain. Coordinated scheduling is one of the most important issues for sustainable development of container terminals. This research provides an in-depth survey of the coordinated scheduling for container terminals in order to identify existing research streams for future investigations. Researches on the coordinated scheduling of container terminals which are published between 2010 and 2021 are reviewed and classified. Related coordinated scheduling models and solution methods are analyzed. Research gaps and future directions are also lighted up. This will help new researchers quickly understanding the distribution of researches on these fields. The findings are important for both research and practice perspectives. [ABSTRACT FROM AUTHOR]

This paper addresses the highly topical issue of the digitalisation of the port community through the development of advanced port communication systems (PCSs). The efficient and sustainable operation of container terminals (CTs) plays a key role in global trade and in ensuring the sustainable functioning of transport chains. While larger container terminals utilise PCSs, smaller container terminals often face challenges when implementing similar systems due to resource constraints and unique operational characteristics. This research analyses the impact of PCSs on the operational processes and information exchange between the smaller CT in the Port of Koper and the local port community. The aim of this study is to identify the possibilities of upgrading PCSs with modern information technologies, such as artificial intelligence (AI), blockchains, internet-of-things (IoT), etc., that will lead to more efficient execution of operational processes, reduced congestion and a lower carbon footprint for the port community as a whole. The paper provides guidelines for the possible coordinated development of PCSs in the Northern Adriatic to ensure the most harmonised operation of stakeholders in different ports or CTs. [ABSTRACT FROM AUTHOR]

We present an analysis of the impact of large container ship call sizes on the operational efficiency of container terminals as well as on hinterland modes of transport. We use standard container terminal simulation software, that can represent a realistic terminal operation. We include dynamic crane productivity, arrival time uncertainty, and deepsea vessel prioritisation in our model.Our experiments investigate the impact of introducing larger call sizes of container ships, increasing the uncertainty in arrival times of deepsea ships, and the impact of the phenomenon of split calls that are employed by the largest container ships in certain regions in the world. We differentiate between the impact on terminal operations, land-based hinterland traffic, and hinterland traffic over water, through barges and feeder vessels.Our results show that introducing large ships with large container call sizes comes with benefits and drawbacks. Benefits can be found in planning advantages for both the deepsea ships and barges and feeders handled in the terminal. Drawbacks occur as a result of operational peaks in subsystems such as the quay cranes, the stack and the land side gates. Higher uncertainty deteriorates the performance of the terminal, but the impact on the hinterland modes of transport is greater than on the large deepsea vessels. The impact of the split call is that it brings all the disadvantages of large vessels, but none of the benefits.Our analysis shows the operational impact of large call sizes on terminal operations and hinterland transport operations, based on container terminal simulations with a higher degree of realism than previous work. In addition, we offer, for the first time, insights in the impact of split calls of container ships at container terminals.

Background: We focus on the inland rail forwarding of import containers from a marine terminal. Specifically, we present a discrete-event simulation study related to container-loading operations by train, evaluating different train departure policies within a predetermined schedule based on the capacity of the rail network. The aim is threefold: manage more containers shipped by train, improve terminal operational efficiency, and increase the rail share. Methods: The proposed procedures take full advantage of the digitization and visualization of data currently present in the terminal with the aim of improving the performance indices of interest, thereby increasing the terminal‘s operational efficiency. We evaluate the medium- and long-term impact of alternative strategies on container dwell times and the possible increase in the number of containers shipped by train. Results: The computational tests are performed with data from a terminal in the port of Genoa (Italy). The results show the relationships between train departure management policies and train departure distributions. The number of departing trains, cancelled trains, and trains departing with load percentages below 70% is studied. Average loads per train and estimated delays are also analyzed. Conclusions: It is noted that the results, which can be obtained with data from any terminal, are of great importance for optimizing operational management, offering practical solutions to improve efficiency and reduce container downtime.

In this study, the static and dynamic performance efficiencies of container terminals are analyzed and compared for the main container terminals in China and Korea. The static performance efficiency is calculated using the Super-SBM model based on slack variables at the micro-level. An analysis on the dynamic performance efficiency is conducted with the Malmquist index method. The factors of scale and technology of container terminals are mainly taken into account to explore the performance efficient improvement path of container ports. We obtained the following conclusions: (1) The container terminals in Korea show a similar performance efficiency level to the terminals in China, and their performance efficiency is an overall upward trend over the past five years. (2) The main reason for inefficiency in the container terminals in China and Korea is predominantly scale inefficiency. (3) Boosting the automation degree does not have a completely positive impact on the efficiency of the terminal. (4) In 2019–2023, the technical progress index of all container terminals in China and Korea showed a decreasing trend, leading to performance inefficiency of the container terminals.

In recent years, despite a decline in international trade and disruptions in the supply chain caused by COVID-19, the main container terminals in Latin America and the Caribbean (LAC) have increased their container volumes. This growth has necessitated significant adaptations by seaports and their authorities to meet new demands. Consequently, there has been a focused analysis on the performance, efficiency, and competitiveness, particularly their most relevant logistical aspects. In this paper, a multi-objective hybrid approach was employed. The Principal Component Analysis (PCA) technique was combined with the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) to rank LAC container terminals and identify operational criteria affecting efficiency. The analysis considered all input variables (berth/quay length, quay draught, yard area, number of quay cranes (portainer), number of yard cranes (trastainer), reachstacker, multicranes, daily montainer movement capacity, number of station reefer container type, number of terminals, and distance to the Panama Canal) and output variable (port performance expressed in TEUs from 2014 to 2023). The results revealed noteworthy findings for several terminals, particularly Colón, Santos, or Cartagena, which stands out as the main container port in LAC not only in annual TEUs throughput, but also in resource utilization.

The design of container terminal information system is directly related to the operational mode and efficiency of container terminals, and modern container terminals rely more on information systems. Based on the project characteristics, owner requirements, and information technology development of the Hainan Yangpu International Container Terminal, the design scope and content of the container terminal information system were systematically studied with a focus on the operation process, system architecture, and main functions of the container terminal management system (TOS) and intelligent gate system, which will provide reference for the design and construction of modern specialized container terminal information systems. [ABSTRACT FROM AUTHOR]

The issue of unloading efficiency for containers is the operational bottleneck for most traditional container terminals. In addressing the intricate challenges of space allocation in container yards during ship unloading, this study focuses on the real-time, dynamic decision-making needs that are currently unmet by existing planning methods. To tackle this, the article introduces a novel model for container space allocation that aims to maximize the "attractiveness" of yard spaces. This model factors in key considerations like the allocation of container handling equipment resources, the rate of container handling equipment traversing the yard, and container handling equipment operations across containers. A unique Monte Carlo tree search (MCTS)-based algorithm is developed to solve this multi-objective problem. The algorithm's efficacy is rigorously tested via numerical experiments, where it outperforms existing approaches like UCT-MCTS, AMAF-MCTS, and manual scheduling plans using practical engineering examples. This research not only provides a more dynamic and efficient method for yard space allocation but also offers empirical evidence to support its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

Short-term electricity load forecasting plays a crucial role in modern container terminal. In this work, we design a short-term forecasting approach aimed at port load under the framework of patch learning. Firstly, singular spectrum analysis is applied to obtain denoised and noise features, respectively; then, a patch learning model based on the long short-term memory network is employed to address such a time-series forecasting problem. LSTM network and BiLSTM are considered as the global models to process denoised and noisy data, respectively, and convolutional neural network is selected as the patch model. Furthermore, an endpoint detection strategy is designed for adaptively identifying the positions of patches. The performance of the proposed model is tested and verified on a real Chinese container terminal load dataset. Experimental results show that the proposed approach, compared with state-of-the-art load forecasting models, has the greatest performance with respect to seven evaluation criteria. [ABSTRACT FROM AUTHOR]

Berth allocation is a critical concern in container terminal port logistics, involving the precise determination of where and when arriving vessels should dock along a quay. With berth space limitations and a continuous surge in container handling demands, ensuring an effective berth allocation is paramount for the smooth and efficient operation of container ports. However, due to the randomness of vessel arrival times and uncertainties surrounding container ship loading capacities, berth allocation problems (BAP) often present discrete and dynamic challenges. This paper addresses these challenges by considering real-world terminal operational factors, formulating relevant assumptions, and establishing a model for dynamic berth allocation and efficient ship berthing scheduling. The primary motivation stems from the parallels observed between the BAP problem and ant foraging path selection, leading to the proposal of a novel Parallel Search Structure Enhanced Ant Colony Algorithm (PACO). A proper set of parameters of the algorithm are selected based upon sensitivity analyses on the convergence and parallelism efficiency of the algorithm. To validate our method, a real-world case-container terminal operation in Shanghai Port was studied. The experimental comparison results show that the PACO algorithm outperforms other commonly used algorithms, making it more effective and efficient for the Discrete Dynamic Berth Allocation Problem (DDBAP). [ABSTRACT FROM AUTHOR]

Container shipping plays a pivotal role in global trade, and understanding the duration that vessels spend in ports is crucial for efficient voyage planning by shipping companies. However, these companies often rely solely on one-way communication for required arrival times provided by terminals. This reliance on fixed schedules can lead to vessels arriving punctually, only to face berths that are still occupied, resulting in unnecessary waiting times. Regrettably, limited attention has been given to these issues from the perspective of shipping companies. This study addresses this gap by focusing on the estimation of dwell times for container vessels at a terminal in the Port of Busan using various machine learning techniques. The estimations were compared against the terminal's operational reference. To compile the dataset, a 41-month history of terminal berth schedules and vessel particulars data were utilized and preprocessed for effective training. Outliers were removed, and dimensions were reduced. Six regression machine learning algorithms, namely adaptive learning, gradient boosting, light gradient boosting, extreme gradient boosting, categorical boosting and random forest, were employed, and their parameters were fine-tuned for optimal performance on the validation dataset. The results indicated that all models exhibited superior performance compared to the terminal's operating reference model. [ABSTRACT FROM AUTHOR]