Container Terminals Research Articles

Machine Learning and Simulation for Efficiency and Sustainability in Container Terminals

This paper examines the impact of reducing ship turnaround time on the performance of container terminals, with a focus on leveraging artificial intelligence (AI) to enhance operational efficiency. It presents a novel framework combining machine learning algorithms with discrete-event simulation to predict ship turnaround times using historical data. The proposed approach is empirically validated with data from the Algiers Port Container Terminal, achieving an exceptionally high predictive precision of 0.9991. Simulating terminal operations with both real and predicted data offers valuable insights into improving performance. The results demonstrate that minimizing empty trips and reducing the waiting times for handling equipment significantly enhance turnaround time. Additionally, optimizing terminal operations reduces carbon emissions, aligning with sustainable development objectives in port logistics. This study proposes a novel integration of machine learning and simulation, demonstrating its effectiveness in optimizing ship turnaround times and reducing carbon emissions. By integrating machine learning and discrete-event simulation, this research offers new perspectives on port logistics, contributing to the advancement of sustainable and efficient terminal operations.

AGV Scheduling and Energy Consumption Optimization in Automated Container Terminals Based on Variable Neighborhood Search Algorithm

Automated Guided Vehicles (AGVs) for automated container terminals are mainly used for horizontal transportation at the forefront of the terminal. They shoulder the responsibility of container transportation between the quay cranes and yard cranes. Optimizing their scheduling can not only improve operational efficiency, but also help reduce energy consumption and promote green development of the port. This article first constructs a mathematical model with the goal of minimizing the total energy consumption of AGVs, considering the impact of different states of AGVs on energy consumption during operation. Secondly, by using the variable neighborhood search algorithm, the AGV allocation for container operation tasks is optimized, and the operation sequence is adjusted to reduce energy consumption. The algorithm introduces five types of operators and a random operator usage order to expand the search range and avoid local optima. Finally, the influence of the number and speed of AGVs on the total energy consumption is discussed, and the optimization performance of the variable neighborhood search algorithm and genetic algorithm is compared through computational experiments. The research results show that the model and variable neighborhood search algorithm proposed in this paper have a significant effect on reducing the total energy consumption of AGVs and show good stability and practical application potential.

Electric tugboat scheduling with charging considerations at container terminals

In response to the evolving landscape of container terminals, characterized by the growing prevalence of electrified ports and the ensuing challenges in electric tugboat operations, this research addresses the intricate tugboat scheduling problem with a particular focus on integrating charging operations. The overall port efficiency is aimed to be enhanced, congestion alleviated and service quality elevated. Realistic constraints such as berthing time windows, limited heterogeneous tugboat availability and battery capacity limits are considered. To address these challenges, a mixed-integer linear programming model is proposed, and two algorithms are developed: a matheuristic method and an adaptive large neighbourhood search (ALNS) algorithm. The matheuristic method is the primary approach in the authors' solution, as it reduces the number of decision variables by fixing the task execution sequence, which decreases problem complexity and allows for the efficient generation of feasible solutions. While the heuristic method forms the foundation of the optimization, recent research has demonstrated that ALNS is effective in solving complex scheduling problems. Therefore, a tailored ALNS algorithm is developed specifically for this problem. The ALNS framework's adaptive mechanism, which dynamically adjusts the selection of operators based on their performance during the search process, ensures a more robust exploration of the solution space. Computational experiments on randomly generated instances reveal that, in terms of algorithm and model performance, the matheuristic method outperforms ALNS, which, in turn, outperforms CPLEX ® . However, for large-scale instances where the matheuristic fails to find a feasible solution, ALNS remains effective and continues to yield strong results, even in highly complex scenarios.

Scheduling truck arrivals for efficient container flow management in port logistics

Abstract The management of truck arrivals at container terminals is crucial for efficient port operations. Congestions developing both outside and inside the gates can cause logistical problems, while also having a significant impact on the environment and the surroundings of the port. Therefore, optimizing truck queues outside the gates of the port, as well as routing of trucks inside the terminals can lead to an improvement in the overall efficiency of the port processes. This paper presents a mixed-integer linear programming formulation to determine these optimal truck routes and schedules. The model considers a port with an external parking lot, multiple gates, internal roadways, and docks. A rolling horizon heuristic is also developed for the solution of instances where the model is otherwise intractable. The developed methods are evaluated on instances simulated based on real-world data.

Works Rescheduling in Container Terminals

Container transportation plays a vital role in freight movement, with container yards serving as critical hubs for operations. This paper focuses on the rescheduling of handling equipment in response to unexpected crane failures, which disrupt planned tasks. To address this challenge, a heuristic algorithm is proposed, incorporating the Longest Processing Time and List Scheduling procedures. Two rescheduling strategies are evaluated through numerical experiments. The results indicate that the second strategy outperforms the first in most cases by improving efficiency, albeit with more changes to the initial schedule. The numerical examples demonstrate the effectiveness of the algorithm, achieving feasible rescheduling solutions within less than one second for all tested cases. These findings highlight the algorithm’s efficiency and practicality in real-world container yard operations.

Bi-Objective Optimization for Joint Time-Invariant Allocation of Berths and Quay Cranes

With the increasingly busy transportation of cargo at container terminals (CTs), the requirements for terminal throughput and operational efficiency are constantly increasing. The operational efficiency and cost of CTs are closely related to the seamless docking of terminal facilities, especially the joint operation between berths and quay cranes (QCs). Therefore, a joint allocation problem of berths and QCs (BACASP) is presented in this paper and formalized as a mathematical model to minimize terminal operation costs and shipowner dissatisfaction. Given that BACASP is an NP-hard problem, an improved multi-objective cuckoo search (IMOCS) algorithm is proposed to solve this problem, in which an elite-guided tangent flight strategy is presented to speed up the convergence for making up the lack of random search direction of the traditional cuckoo search algorithm; and an information-enhanced abandonment strategy is put forward to increase the possibility of escaping from local optima. Numerical experimental results show the effectiveness of the proposed algorithm.

Modeling of the distribution of cargo flows of combined shipments in the hinterland area of the seaport

This study focuses on assessing the potential for optimizing the utilization of the hinterland transport network when organizing the dispatch of combined cargo shipments from a seaport. The complexity of this task stems from the need to implement innovative methods to enlarge bulk shipments to facilitate their inland transportation using containerization. This approach promotes the development of new transportation routes and necessitates the evaluation of the transport characteristics of the shipping process. Modeling the transport network requires a comprehensive evaluation of factors, as well as economic and operational indicators that characterize the movement of cargo between origin points (sea container terminals) and destination points (consignee warehouses). This paper reviews existing models for assessing transport network utilization and provides a brief overview of each. In evaluating ongoing processes, it is emphasized that each specific origin-destination pair (sea container terminal to consignee warehouse) can be represented not only in terms of volume but also as a function of time. The possibility of developing a dynamic model based on various methods of unitizing cargo and evaluating the network's transport capabilities is also highlighted. The study concludes that, when addressing the problem of distributing flows across the transport network, considering different methods of organizing cargo units, and accounting for constraints on route capacity, time, or transportation costs, a projection method can be applied to identify the optimal transportation solution from a range of feasible options.

Delay-tolerated vehicle positioning and scheduling problem at terminal aprons: a target-oriented robust optimisation approach

The terminal apron, a critical component of container terminals, handles the majority of container flow but faces challenges due to limited maneuvering space and high traffic volume, necessitating improved vehicle positioning and scheduling. Operational uncertainties, such as unpredictable container transit times, compound these challenges by potentially causing delays in arrivals at the terminal apron. These types of delays are particularly problematic for container loading operations with tight schedules, making it difficult to estimate vessel turnaround time. This study addresses the integrated vehicle positioning and scheduling problem at terminal aprons while considering vehicle transit time uncertainties. The aim is to increase the resilience of the terminal apron system against delays, thereby enhancing the efficiency and robustness of loading operations under uncertain conditions. To achieve this, a target-oriented robust optimisation (RO) model is proposed, developing an uncertainty set to characterise transit time variability. A solution approach includes model reformulation, a customised bisection method, and the logic-based Benders decomposition method to efficiently solve the proposed models. Numerical experiments using data from Tianjin Port demonstrate that the target-oriented RO is suitable for managing terminal apron operations in uncertain environments, especially true during peak operating times or in scenarios characterised by significant variability.

Assessing container terminals’ efficiency from the sustainable development perspective: The BWM-GRA-SBM model

Assessing container terminals’ efficiency from the sustainable development perspective: The BWM-GRA-SBM model

Comparison of Carbon Emission Estimation Results with Case Studies of Container Terminals

Comparison of Carbon Emission Estimation Results with Case Studies of Container Terminals

Changes of marine environments due to human activities in coastal waters of Korea.

Changes of marine environments due to human activities in coastal waters of Korea.

Quay crane assignment in container terminals using a genetic algorithm

Quay crane assignment in container terminals using a genetic algorithm

Factors Influencing on Job Satisfaction among Executives in Container Terminals in the Port of Colombo, specifically at Jaya Container Terminal

Container terminals are crucial to global trade, handling over 95% of goods transported via sea, with Sri Lanka's Colombo Port offering a strategic role as a key maritime hub. Despite significant infrastructure advancements, the effectiveness of these investments is contingent upon the satisfaction and performance of human resources. This study investigates the factors influencing job satisfaction, focusing on hygiene factors such as salary and co-worker relationships and motivation factors like recognition and responsibility. A cross-sectional survey of 110 executives revealed that salary (r = 0.818) and co-worker relationships (r = 0.709) were the strongest predictors of satisfaction. To enhance satisfaction and organizational performance, recommendations include revising salary structures, fostering interpersonal relationships, and providing career development opportunities. The findings emphasize the dual importance of hygiene factors such as competitive salary structures and fostering interpersonal relationships and motivation factor like meaningful work and acknowledgment to enhance job satisfaction and organizational performance. By addressing these dimensions, organizations can mitigate dissatisfaction, improve retention, and optimize productivity. The study contributes to the broader understanding of workforce dynamics in high-pressure industries, providing actionable recommendations for aligning organizational goals with employee well-being. These insights are particularly valuable for the maritime sector, where operational efficiency and competitiveness are closely tied to human resource management.

Collaborative optimization of truck scheduling in container terminals using graph theory and DDQN

The container terminal is a key node in global trade and logistics, where trucks connect quay cranes, storage yards, and vessels. Optimizing truck scheduling is crucial for enhancing port efficiency by addressing issues such as low truck utilization, excessive quay crane waiting times, and extended equipment completion times. This paper develops a container terminal simulation model based on graph theory, with the objective of minimizing the maximum completion time of terminal equipment. A collaborative scheduling algorithm for truck fleets, based on Deep Double Q-Networks (DDQN), is proposed. The algorithm designs five heuristic rules as the action space and refines state features and reward functions to optimize scheduling effectively. Experimental results indicate that this algorithm consistently identifies optimal scheduling strategies, outperforming both the five heuristic rules and the Deep Q-Network (DQN) algorithm. It significantly reduces quay crane waiting times and equipment completion times, improves truck utilization, and enhances overall container terminal efficiency.

Deep learning-based estimation of truck Turn Around Time at container port

ABSTRACT In South Korea, most import and export cargo are mainly transported by sea. With so much cargo being transported through the ports, congestion from trucks is particularly severe at city ports, which are located close to cities. Accordingly, port terminals require systematic and efficient terminal operation, since congestion leads to problems such as air pollution. This study aims to estimate the Turn Around Time (TAT) of trucks at container terminals. Accurate truck TAT estimations have the potential to mitigate congestion, since truck drivers can avoid congested times. The dataset was constructed by combining truck DTG (Digital Tacho Graph) data collected in Korea in 2021, shipping in port data collected from major ports in Korea such as Busan New Port and Incheon Port, and weather data. Several artificial intelligence models were used to estimate the TAT, and their performance evaluation indicators were compared. From the comparison results, the artificial intelligence model with the best estimation performance was identified. As a result of estimating TAT through artificial intelligence models, Busan New Port had the highest TAT estimation accuracy. Through this study, we found that it is possible to estimate TAT using DTG data, which is installed in most Korean trucks.

Evaluating container terminal competitiveness in the Hamburg – Le Havre range

PurposeThe present research aims to develop a Terminal Competitiveness Index (TCI) applied to the container terminals located in the Hamburg – Le Havre range, which is an area characterised for its intense container activity. The main components of the TPCI are productivity, foreland connectivity and infrastructure.Design/methodology/approachTo construct the index, the Benefit-of-the-Doubt and the Common Set of Weights methods in Data Envelopment Analysis are used to obtain a common weighting scheme for the evaluation of container terminals.FindingsResults show that connectivity and terminal efficiency are the most important factors for terminal competitiveness. The TCI has identified that APM Terminals Maavslakte II (Rotterdam), ECT Delta (Rotterdam) and MPET (Antwerp) turned out with the highest competitiveness score.Originality/valueContainer terminals play a key role in today’s marketplace since they are the main infrastructure responsible for loading and unloading the containers full of intermediate and final goods. Therefore, the competitiveness of such terminals is crucial for shipping lines and importing and exporting companies, influencing their cost and schedule reliability. However, there is scarce literature studying the competitiveness of container terminals, since the focus to date has been on ports as units of analysis. The terminal-approach used allows the analysis of the competitiveness of terminals belonging to different ports, but also between those located in the same port.

Digital twins: digitalization of automated container terminals in seaports

Digital twins: digitalization of automated container terminals in seaports

Dynamic charging strategies for battery-powered IGV based on multi-agent simulation in automated container terminals

Dynamic charging strategies for battery-powered IGV based on multi-agent simulation in automated container terminals

Challenges Faced by Garment Exporters of Tirupur in the Digitalization of ICDs and CFS

Tirupur, widely recognized as the "Knitwear Capital of India," plays a pivotal role in India’s garment export industry, contributing significantly to the country's foreign trade revenue. The city’s export sector heavily depends on efficient logistics and supply chain management, where Inland Container Depots (ICDs) and Container Freight Stations (CFSs) serve as critical nodes in facilitating international trade. The digitalization of these logistics hubs offers a transformative opportunity to enhance trade efficiency, streamline operations, reduce transaction costs, and improve real-time cargo tracking. By integrating advanced technologies such as automated documentation, digital customs clearance, and blockchain-based trade platforms, exporters can achieve greater transparency, security, and compliance with global trade regulations. Despite these advantages, garment exporters in Tirupur encounter several barriers to adopting digital solutions. Key challenges include technological infrastructure limitations, such as inadequate access to high-speed internet and the lack of sophisticated IT systems; regulatory complexities, stemming from frequently changing compliance requirements and the need for integration with national and international trade platforms; financial constraints, as small and medium-sized enterprises (SMEs) struggle with the high initial costs of digital implementation and limited access to financial support; operational inefficiencies, arising from resistance to change, lack of skilled personnel, and dependence on manual processes; and cybersecurity risks, which pose threats to data integrity, privacy, and fraud prevention. This study provides an in-depth analysis of these challenges and explores possible solutions to facilitate the seamless digital transformation of Tirupur’s export ecosystem. It examines strategies such as strengthening digital infrastructure through public-private partnerships, offering financial incentives to exporters, simplifying regulatory procedures for easier compliance, enhancing workforce training in digital logistics, and implementing robust cybersecurity frameworks. By addressing these issues, the study aims to offer actionable insights for policymakers, industry stakeholders, and exporters, ultimately fostering a more efficient, competitive, and technology-driven export environment in Tirupur.

Scheduling AGVs in ports with battery charging and swapping

Efficient scheduling of automated guided vehicles (AGVs) in automated container terminals (ACTs) is crucial to their operations management under the initiative of green port and smart port development. Facing numerous containers, a port operator needs to assign container loading/unloading tasks to AGVs, sequence the tasks for each AGV, and arranges the battery charging or swapping activities for the AGVs in a high-efficient manner, which affects the operational performance of the port and further impacts its annual throughput. This study proposes a mathematical model for scheduling AGVs under the battery charging-swapping mixed mode. The model should be solved in a short time, otherwise the proposed methodology cannot apply in realistic port environment. Therefore, an efficient column generation based solution method is designed for solving the models; a novel label setting algorithm is also proposed and embedded in the above method for solving the pricing problems in a fast way. Based on two representative ACTs, i.e., the first ACT in China and the largest ACT in the world, numerical experiments are conducted to derive some managerial insights for ACTs’ operations management on AGVs under different battery management modes.