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The increasing vessel size and automation level have shifted the productivity bottleneck of automated container terminals from the terminal side to the yard side. Operating an automated container terminal (ACT) yard with a big number of automated guided vehicles (AGV) is challenging due to the complexity and dynamics of the system, severely affecting the operational efficiency and energy use efficiency. In this paper, a hybrid multi-AGV scheduling algorithm is proposed to minimise the energy consumption and the total makespan of AGVs in an ACT yard. This framework first models the AGV scheduling process as a Markov decision process (MDP). Furthermore, a novel scheduling algorithm called MDAS is proposed based on multi-agent deep deterministic policy gradient (MADDPG) to facilitate online real-time scheduling decision-making. Finally, simulation experiments show that the proposed method can effectively enhance the operational efficiency and energy use performance of AGVs in ACT yards of various scales by comparing with benchmarking methods. [ABSTRACT FROM AUTHOR]

The deployment of the Industrial Internet of Things (IIoT) in smart container terminals provides a foundation for sensing and recording all operational processes. However, little effort has been devoted to integrating the massive data regarding interoperability challenges, thus limiting the value of data in advancing the intelligent evolution of ports. In this research, we propose a graph-based approach to organise operational records semantically, thereby facilitating data-driven decision-making in container terminals. We first construct a knowledge graph for operational processes in container terminals, employing a tailored procedure for the automatic conversion of operational records into triples. By utilising the graph information, we propose a novel method that integrates reinforcement learning (RL) with a mathematical solver for optimising scheduling problems. The quay crane scheduling problem (QCSP) is illustrated as an example to elaborate on the technical details. Based on a dataset from a real-world container terminal, numerical studies demonstrate the superiority of the proposed framework in terms of information retrieval efficiency and solution quality compared with the traditional data organisation approach. [ABSTRACT FROM AUTHOR]

The container pre-marshalling problem (CPMP) aims to minimise the number of reshuffling moves, ultimately achieving an optimised stacking arrangement in each bay based on the priority of containers during the non-loading phase. Given the sequential decision nature, we formulated the CPMP as a Markov decision process (MDP) model to account for the specific state and action of the reshuffling process. To address the challenge that the relocated container may trigger a chain effect on the subsequent reshuffling moves, this paper develops an improved policy-based Monte Carlo tree search (P-MCTS) to solve the CPMP, where eight composite reshuffling rules and modified upper confidence bounds are employed in the selection phases, and a well-designed heuristic algorithm is utilised in the simulation phases. Meanwhile, considering the effectiveness of reinforcement learning methods for solving the MDP model, an improved Q-learning is proposed as the compared method. Numerical results show that the P-MCTS outperforms all compared methods in scenarios where all containers have different priorities and scenarios where containers can share the same priority. [ABSTRACT FROM AUTHOR]

Digitalization is constantly altering company paradigms and expanding cross-border supply chain prospects. Maritime transportation plays an increasingly essential part in the global supply network. Since maritime shipping services need to exchange a huge number of papers and paperwork across numerous companies, the usage of a unified platform for inter-organisational communication and information sharing is required. To develop an integrative, adaptive, and intelligent container booking system, a multi-agent architecture is designed in this article. The proposed architecture will aid the maritime industry in establishing real-time information interchange between autonomous agents, shippers, freight forwarders, and shipping lines. The process outlined in this paper reveals how the agents communicate with one another to resolve underlying inconsistencies. With the multi-agent framework, the article also presents a container slot optimisation problem considering market segmentation, different booking periods, heterogeneous containers and port congestion scenarios. Using this model the managers can find the booking limit for each type of containers and accordingly they can accept or reject the incoming booking requests. Furthermore, a simulated case study is also provided to validate the model. [ABSTRACT FROM AUTHOR]

An inventory stacking decision assigns positions to items which are stacked vertically, such as containers in container terminals and steel plates in steel plants. The performance of stacking decisions is greatly affected by the arrival and departure information on items. We study an inventory stacking problem with partial information based on industrial observation in a steel plant. On the inbound side, we investigate three levels of information on future arriving items and their effect on performance. On the outbound side, we study the impact of the retrieval sequence, which is often random. We develop models incorporating different availabilities of information and determine stacking strategies. The study shows that the stacking strategy and stacking performance depend highly on information quality and space utilisation. Especially, when the space utilisation is high, low-quality information deteriorates the performance and such information should be ignored. This contradicts the general belief that more information should bring better performance. The study further proposes a time window allocation approach to reduce the uncertainty in retrieval, and it is effective in improving stacking performance. [ABSTRACT FROM AUTHOR]

The aim of this research is to minimize the number of container-rehandling operations in both the yard and on the ship in order to solve the problem of coordinated optimization of ship loading and yard container retrieval and enhance the loading efficiency of automated container terminals,. An optimization model that integrates the optimized decision-making of both the container retrieval order from the yard and the ship's space allocation is developed, and an improved cuckoo algorithm is employed to solve the optimization problem. This paper examines the influence of container retrieval order from the yard on rehandling within the yard, as well as the effect of the ship's space allocation on subsequent rehandling operations at the unloading port. Experimental results demonstrate that the solution derived from the comprehensive optimization model based on the improved cuckoo algorithm considerably reduces the overall number of overturned containers. This confirms that the proposed optimization model effectively enhances loading efficiency and reduces container rehandling both in the yard and on the ship. The analysis of experimental results indicates that the model and algorithm proposed in this paper have certain applicability. [ABSTRACT FROM AUTHOR]

To improve the operational efficiency of automated container terminals and the coordination between multiple operations, this article studies the integrated scheduling optimization problem of automated guided vehicles (AGVs) and yard cranes. The impact of charging constraints on AGV task allocation and scheduling is considered. With the goal of minimizing the maximum completion time of all tasks, a mixed integer programming model is proposed. A solution method based on a dynamic programming algorithm is designed, where a heuristic algorithm is used to assign tasks to the yard cranes, and the dynamic programming method is used to assign tasks to the AGVs based on the task assignment results of the yard cranes. Finally, the validity of the model and algorithm is tested by numerical experiments. Furthermore, the influence of the quantity of AGVs on the terminal operational efficiency and the impact of AGV charging strategies on AGV scheduling are analysed. [ABSTRACT FROM AUTHOR]

The white paper on China's Arctic Policy, which proposed the joint construction of the Polar Silk Road (PSR), was officially published in January 2018. As a short and economically feasible sea route, the PSR will inevitably affect the carrier's market choice behaviour, thereby affecting the foreland network structure and foreland pattern of China's coastal container ports (CCCP). Grasping the evolution trend of CCCP foreland under the PSR will help predict the development trend of the port and shipping market in advance and enable measures to be taken to adapt to the changing market environment. This paper constructs the port foreland network evolution (PFNE) model and presents a complex network delineation method of port foreland to simulate the effects of PSR on CCCP foreland evolution in different scenarios. Results show that the PSR's addition to the CCCP foreland network will improve shipping connectivity, increase the connection between long-distance ports, reshape the clustering groups, promote the orderliness of the network and help the development of small and medium-sized ports. China's global maritime transport pattern will change, which is mainly reflected in the enhanced shipping links between CCCP and Asia, Europe and Africa, while the importance of the Americas for CCCP weaken. PSR has a more obvious role in promoting the establishment of maritime links between China's northern ports and the world ports. In the discussion, we propose the development policy of CCCP under the PSR. [ABSTRACT FROM AUTHOR]

With the development of the Shanghai International Shipping Center, the diversity of vehicle types on the highways and arterial roads near Yangshan port is continually increasing. Within such a container port corridor, large container trucks are primarily utilized for mainline transportation. Their larger size and significant inertia would increase psychological pressure on sedan drivers, and elevate their behavior risk. To investigate the effects of container trucks on drivers' visual characteristics and driving behavior as well as to predict driving risk, firstly, this research conducted field tests in four scenarios surrounding the port. Visual characteristics and behavior data of sedan drivers were collected. Secondly, a "Visual‐behavior" chain model was established. The relationship between drivers' visual characteristics, driving behavior, and driving risk was illustrated from the perspective of time‐series behavior patterns. Thirdly, three driving risk prediction models were built with Autoregressive Integrated Moving Average (ARIMA), Long Short‐Term Memory (LSTM), and ARIMA‐LSTM. The results indicate that the ARIMA‐LSTM model shows the most effective prediction performance. This research provides a field‐data comparative analysis of the driving risks influenced by a high proportion of container trucks. The findings contribute to understanding the unique mixed traffic visual environment around large‐scale container ports. [ABSTRACT FROM AUTHOR]

U‐shaped automated container terminals (ACTs) represent a strategic design in port infrastructure that facilitates simultaneous loading and unloading operations. This paper addresses the challenges of scheduling multiple types of equipment, such as dual trolley quay cranes (DTQCs), automated guided vehicles (AGVs), double cantilever rail cranes (DCRCs), and external trucks (ETs) in U‐shaped ACTs. This paper proposes a mixed integer linear programming model for optimizing the multiple equipment scheduling, aiming to minimize container completion time and AGV waiting time simultaneously. This paper customizes a hybrid genetic‐cuckoo optimization algorithm (HGCOA) with double‐point crossover and Lévy flight Cuckoo search strategies. Extensive numerical results show that the proposed HGCOA outperforms the benchmark genetic algorithms in terms of solution quality and computational time while significantly improving efficiency without substantial sacrifices in solution quality compared with the exact solution method. Overall, this study presents a promising solution for enhancing coordination and operation efficiency in U‐shaped ACTs. [ABSTRACT FROM AUTHOR]

The building scheme design of the maintenance center plays a crucial role in the maintenance and safe operation of dock process equipment. Intelligent container terminals are currently at the forefront of terminal automation development. However, traditional maintenance center buildings are no longer able to meet the process equipment maintenance needs of intelligent container terminals. Based on the new construction concept of the intelligent container terminal project in Section C of Tianjin Port, the key design issues of the functional positioning, spatial layout, green intelligence, "zero carbon" design and aesthetic enhancement of the maintenance center building are summarized, and a maintenance center building scheme that meets the needs of intelligent container terminals is designed. [ABSTRACT FROM AUTHOR]

For charging problem in AGV scheduling in automated container terminals, this paper constructed a mixed integer optimization model considering the shallow charging and shallow discharging charging strategy. The model aimed to minimize the final completion time of the AGV. Under the constraints of considering the change of AGV battery power and the difference in power consumption in different states of the AGV, the model used the AGV idle time and the end time of a work cycle to make up power, reducing the number of AGV charging times, and thus reducing the total completion time. The model was solved by Wolf-PHC reinforcement learning, and the results were compared with GAMS solver, Q-learning algorithm and genetic algorithm (GA) respectively to verify the effectiveness of the model and superiority of the algorithm. The example analysis shows that AGV utilization efficiency is higher under the shallow charging and shallow discharging charging strategy, and the combination of Wolf-PHC and GA is better for the model solution. [ABSTRACT FROM AUTHOR]

This study analyzes the potential impact of Brexit on port congestion and supply chain cost using the example of the largest European Ro-Ro port of Dover. A microscopic simulation model is constructed to replicate traffic conditions and analyse different scenarios. The results show that the current port infrastructure will have difficulties facing the challenges that Brexit could have brought. Checkpoints for border controls are found to be the primary bottleneck. Three types of countermeasures are discussed and found that none of the single measures is sufficient. However, the combination of two additional border checking booths and 10% traffic reduction is the most realistic and effective solution to contain the congestion. The case study shows the company's supply chain costs would increase by around 7% to 20% in different scenarios. The largest increase comes from annual shipping cost, which accounts for 94% of the total supply chain costs. Alternative transportation options are discussed. It is found that the Port of Dover remains the preferred Ro-Ro port, but the Lo-Lo container port of Southampton will become a better solution for cargoes with modest unit value. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a major impact on the container shipping and port industry in terms of its reach and scale. The research analyzes the impact of COVID-19 on hub port choice and shipping connectivity using the case of major container ports in Southeast Asia. This study enriches the definition of connectivity and proposes a new research approach that differentiates the order of port-call for mainline services. The research approach is applied to the case of major container ports in Southeast Asia for the Asia-Europe trade route. The study further analyzes hubbing strategies of major shipping lines and alliances with the application of data mining on global satellite ship data. Findings indicate shipping connectivity to have grown in Singapore particularly at the expense of Port Klang during the COVID-19 pandemic as evidenced by changes in the shipping networks of The Alliance and Ocean Alliance. The research offers important policy and managerial implications concerning the post-COVID competition landscape covering the aspects of joint venture terminals, shipping alliance networks, and container port development. [ABSTRACT FROM AUTHOR]

The Northeast Asia container port system has experienced a significant increase in ship calls and container throughput, resulting in a detrimental impact on the environment surrounding the port area. This is primarily caused by emissions from the hotelling phase, which is closely associated with cargo operations at the berths. This study aims to investigate ship emissions during the hotelling phase and evaluate the impact of cargo operation productivity on environmental performance. To achieve this goal, the study used a bottom-up approach to calculate the ship's emissions at the berth based on various factors such as the number of ship calls, the power of the auxiliary engines, the load factor, the emissions factor, and in-port hotelling duration of the top 20 major container ports in the region from 2017 to 2021. The outcomes revealed that the major container ports in the Northeast Asian region, including Shanghai, Ningbo, Shenzhen, Hongkong, and Busan, are emitting an enormous volume of pollutants, primarily due to the high density of ships and continuous loading and unloading operations, causing severe air pollution. The study found that improving loading/unloading productivity is an effective measure to improve the environmental quality of the port area. Furthermore, the study also revealed that handling larger ships could reduce emissions per TEU. • The study highlights significant emissions during the hotelling phase at Northeast Asian container ports, attributing them to high ship density and continuous cargo operations, leading to severe air pollution in port areas. • Improving cargo operation productivity emerges as a crucial strategy to enhance environmental quality at ports, suggesting a direct link between operational efficiency and reduced emissions. • Research identifies handling larger ships as a potential method to decrease emissions per TEU, indicating scalability benefits for environmental sustainability in port operations. [ABSTRACT FROM AUTHOR]

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As the digital paradigm stimulates changes in various areas, seaports, which are fundamental to logistics and the global supply chain, are also undergoing a digital revolution, evolving into smart ports. Smart gates are essential components in this transformation, playing a vital role in increasing port efficiency. In the context of smart gates, the aim of this study is to understand how process management can serve as a catalyst for digital transformation, promoting efficiency in traffic flow and logistics. To achieve this objective, the design science research (DSR) methodology was followed, which allowed for the integration of information from several sources of requirement, encompassing both theoretical and practical aspects. The practical component took place at one of Portugal's largest container terminals, which allowed for the integration of information from various sources. As a result, this study presents the conceptual definition of a smart gate in terms of processes, main technologies, and key performance indicators that will support the monitoring and improvement of future operations. The results provide theoretical and practical contributions: on a practical level, they present a real application of the transformation towards a smart gate, serving as a model for other ports in their digitalization; on a theoretical level, they enrich the literature with a methodology for digitalizing maritime road gates, showing how the use of process management approaches, such as the BPMN, can increase operational efficiency in container terminals. [ABSTRACT FROM AUTHOR]

This paper introduces and solves a real-world problem that arises at container ports, namely the double stack railcar allocation problem. The key challenge in the double stack railcar allocation problem is to balance the number of containers loaded and the railcar usage, while respecting the loading constraints for double stack trains. To capture the key characteristics of the double stack railcar allocation problem, an integer linear optimization model is presented. The model is found to be computationally prohibitive for a state-of-the-art off-the-shelf solver (CPLEX). Hence, a tailored solution method is proposed that manages to drastically reduce the computation times, sometimes by more than 99%. In a detailed case study, insights into the double stack railcar allocation problem are revealed and discussed. [ABSTRACT FROM AUTHOR]

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]

This study aims to analyze the connectivity of non-containerized ports, particularly in the sub-commodity of vegetable/animal oils and fats, within the operational area of PT Pelindo Multi Terminal. Using Social Network Analysis (SNA) and Geographic Information System (GIS) techniques, this study builds a network based on nodes (ports) and assesses inter-node connections with metrics such as degree centrality, betweenness centrality, and hub index. Gephi application was used for the calculation of network metrics, while QGIS visualized the shipping routes. The research revealed that high traffic ports such as Belawan, Dumai, and Tanjung Priok play a crucial role in the network. The findings support recommendations for infrastructure development and implementation of an integrated Terminal Operations System (TOS) to improve logistics efficiency. [ABSTRACT FROM AUTHOR]

This study identifies and analyzes the relative importance of the multi-faceted factors that drive the decision to automate container terminals and the realized benefits, thus establishing how accurately terminal operators predicted the benefits of automation. The empirical analysis relies on a survey-based approach and the input of senior representatives of terminal operating entities in charge of the fully and semi-automated container terminals. The analysis of the findings, using statistical tools (i.e., descriptive statistics, Pearson correlations, ANOVA, Kruskal-Wallis), reveals that most of the benefits assumed by an individual terminal operator materialized once the automated terminal was in operation. It also concludes that expectations often exceed benefits and vice versa. A stepwise regression analysis enables the search of causal relationships between drivers and realized benefits with key characteristics of automated container terminals, such as their organizational features, technical dimensions, and the maritime and urban markets they serve. This article is a revised and expanded version of a paper Container Terminal Automation: Assessment of Drivers and Benefits presented at the International Association of Maritime Economists Annual Conference, Busan, Korea, 2022. [ABSTRACT FROM AUTHOR]

Port operators and liner shipping companies attempt to gain cost and competitive advantages by providing services in complementary maritime-related areas. In line with this purpose, companies undertake a task known as Global Terminal Operator (GTO). Substantially, there are more than 21 GTOs of various sizes involved in container terminal operations worldwide, handling 80% of the total containerized cargo traffic. In recent years, there have been drastic changes in the GTO market with numerous mergers, acquisitions, and concessions. However, academic and sectoral studies have fallen short of keeping up with these changes. To fill the gap in research, a dynamic portfolio analysis has been carried out within the scope of this novel study for the first time for the GTOs with the help of BCG Matrix analyses. Findings reveal that GTOs followed different development paths with respect to their distinctive characteristics. Major operators maintained their market dominance, unlike the minors who followed an unsteady path of development. This paper contributes to the academic literature by conducting a comprehensive market analysis for the GTO sector and revealing the dynamic development courses as well as strategic competitive positions of the operators from a managerial perspective toward shaping future strategic plans of the industry stakeholders. [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]

Background: Container terminals (CTs) have constantly administered truck appointment systems (TASs) to effectively accomplish the planning and scheduling of drayage operations. However, since the operations in the gate and yard area of a CT are stochastic, there is a need to incorporate uncertainty during the development and execution of appointment schedules. Further, the situation is complicated by disruptions in the arrival of external trucks (ETs) during transport, which results in congestion at the port due to unbalanced arrivals. In the wake of Industry 4.0, simulation can be used to test and investigate the present CT configurations for possible improvements. Methods: This paper presents a simulation optimization (SO) and simulation-based optimization (SBO) iteration framework which adopts a dual transactions approach to minimize the gate operation costs and establish the relationship between productivity and service time while considering congestion in the yard area. It integrates the use of both the developed discrete event simulation (DES) and a mixed integer programming (MIP) model from the literature to iteratively generate an improved schedule. The key performance indicators considered include the truck turnaround time (TTT) and the average time the trucks spend at each yard block (YB). The proposed approach was verified using input parameters from the literature. Results: The findings from the SO experiments indicate that, at most, two gates were required to be opened at each time window (TW), yielding an average minimum operating cost of USD 335.31. Meanwhile, results from the SBO iteration experiment indicate an inverse relationship between productivity factor (PF) values and yard crane (YC) service time. Conclusions: Overall, the findings provided an informed understanding of the need for dynamic scheduling of available resources in the yard to cut down on the gate operating costs. Further, the presented two methodologies can be incorporated with Industry 4.0 technologies to design digital twins for use in conventional CT by planners at an operational level as a decision-support tool. [ABSTRACT FROM AUTHOR]

Global shipment volumes have been increasing due to changes in the business environment of e‐commerce and manufacturing. Consequently, container vessels carry more cargo for international trade, increasing uncertainties in terminal management. Terminal operators manage terminals by establishing a proactive schedule that responds to disruptions such as vessel delays, and the introduction of buffer time is a representative proactive strategy. In this study, by analyzing historical delay data with machine learning, we propose data‐driven buffer times to consider the heterogeneous arrival uncertainty of vessels. Thus, we proactive scheduling with data‐driven buffer times according to the desired robustness levels. This is a novel study on berth scheduling that applies data mining approaches to improve operations research techniques. Numerical experiments were conducted on the berth scheduling with time‐invariant quay crane assignment using real‐life data to validate the effectiveness of the proposed method. These experimental results revealed that applying the data‐driven buffer time could effectively reduce the cost incurred at the terminal by balancing baseline and recovery costs. In addition, our proposed methodology ensured the quality of the solution compared with a stochastic method and reduced the computational burden of a stochastic problem by using the data‐driven buffer times obtained prior to the solution construction. Therefore, the proposed method can be introduced into terminal operations to overcome the deficiencies of traditional approaches in terms of academic perspective. [ABSTRACT FROM AUTHOR]

Container weighing is crucial to the safety of the shipping system and has garnered significant attention in the maritime industry. This research develops a container weighing optimization model and validates several propositions derived from this model. Then, a case study is conducted on ports along the Yangtze River, and the sensitivity analysis of the model is provided. We report the following findings. First, the model can be solved efficiently for large-scale optimization problems. Second, as the number of weighing machines increases, the container weighing mode changes—from selectively weighing containers at their origin ports, then weighing containers at their transshipment ports or destination ports, to all of the containers weighed at their origin ports. Third, in order to improve the safety benefits of weighing containers, port authorities can increase the weighing capacity of weighing machines. The research provides theoretical guidance for shipping system managers to design container weighing plans that enhance maritime safety. [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]

Implementation of Verified Containers Carried on Ships (Verified Gross Mass of Container) -- VGM at Belawan Container Terminal (Research Team Supriadi and Christy Pongkessu). In an effort to improve shipping safety and minimize the risk of damage or accidents at sea due to load imbalance and overloading on ships, in May 2014 the International Maritime Organization - The Maritime Safety Committee (IMOMSC) has decided to approve changes to The International Convention for the Safety of Life at Sea (SOLAS) 1974 Chapter VI Article 2 concerning the requirements for verifying the gross weight of containers (Verified Gross Mass of Container/ VGM). Verified Gross Mass (VGM) is validated container weight information using scales that have been certified and accounted for by a verifier. This study aims to determine the implementation and impact of the implementation of Verified Gross Mass (VGM) at Belawan Port. This research was conducted at Belawan Port located in Medan City, North Sumatra, Indonesia for 6 (six) months, namely from May to October 2023. The type of research used is qualitative research. Qualitative research methods are research methods based on the philosophy of postpositivism, data collection techniques with triangulation (combined), inductive/qualitative data analysis. Based on the results of the study, it was concluded that Belawan Port has implemented the Verified Gross Mass (VGM) procedure in accordance with SOLAS (Safety of Life at Sea) regulations. This process involves verifying the gross weight of containers before loading onto ships using appropriate weighing equipment. The main impact of implementing Verified Gross Mass (VGM) is increasing the safety of container ship navigation. By ensuring the correct gross weight of containers, ports can prevent ship accidents caused by gross weight inaccuracies. [ABSTRACT FROM AUTHOR]

Influenced by the dynamics of supply and demand, the demand for maritime transport has been increasing annually, putting significant pressure on container ports. To alleviate this pressure, a new mixed-integer programming model for the integrated scheduling of tugboats, berths, and quay cranes has been established. This model considers the uncertainties in vessel arrival times, vessel berthing preferences, time-varying quay crane availability, and the constraint that quay cranes cannot cross each other. The objective is to minimize the total costs including fuel consumption during port stays, delays and waiting times for berthing and departure, berthing deviation costs, tugboat assistance costs, and quay crane handling costs. To obtain high-quality solutions, an adaptive large neighborhood search (ALNS) algorithm was employed to solve the model. The algorithm incorporated five destruction operators and five repair operators that were specifically designed to enhance the solution accuracy and efficiency for the integrated scheduling problem. Several case studies of varying scales, based on a port in China, were used to validate the effectiveness of the proposed model and algorithm. The experimental results demonstrate the model's validity and show that the ALNS algorithm designed for the integrated scheduling problem outperformed CPLEX and other algorithms in terms of the accuracy and efficiency. Finally, a sensitivity analysis of the key parameters provides recommendations for the integrated scheduling of tugboats, berths, and quay cranes, offering valuable insights for port operations. [ABSTRACT FROM AUTHOR]

Purpose: Organizational technologies can be classified according to the roles they play as either commodity or strategic. Commodity technologies support common operations, while strategic technologies address perceived threats to competitiveness, often identified by strategic foresight. These must go through an adoption process before playing an effective role in strategy execution. The adoption process includes known activities, ranging from sourcing (itself from in-house development to turn-key acquisition) to operational integration. This paper aims to reveal strategic technology adoption risks that arise during strategy execution. Design/methodology/approach: A gradually developed causal loop diagram model, supported by general literature, introduces three general classes of technology adoption risks: mismatched requirements, supplier dependence and unmanaged life cycles. Findings: Rather than managed, these risks are incurred or avoided depending on decisions made during the adoption process. Research limitations/implications: Despite the scarce literature coverage for the approach, examples revealing the presence of adoption risks are nevertheless available in the well-documented history of enterprise resource planning (ERP). Practical implications: Although ERP is presented as a general-purpose strategic technology, the unique business features of maritime container terminals pose serious challenges to its adoption, which provides additional support to the discussion and reinforces the conclusions. Originality/value: The approach to identifying risks in strategic technology adoption departs from the current risk paradigm in two significant ways. First, it emphasizes policy decision-making rather than external events. Second, it views risks as systemic rather than occurring independently. [ABSTRACT FROM AUTHOR]

Abstract In container terminal, the container trucks should go by a “quay crane - container trucks -import and export block” routing, and the optimization of this routing has become the focus of this paper. This study divided the problem of routing optimization into two levels: one is a routing optimization model of single container truck which takes the lowest total cost as the target, without considering the distribution of overall transportation task; the other is a routing optimization model concerning the allocation of no-load trucks, aiming at the lowest cost of transportation task. We designed an improved Particle Swarm Optimization (PSO) algorithm to solve the two-level optimization model, and the influence of emission policy on the two-level optimization model and the influence of the number of container trucks on the transportation distance of level II in optimization model are analyzed. The results show that the increase of carbon tax will lead to the decrease of emissions in level I, but has no effect on the emissions in level II. The increase in the number of container trucks will lead to a reduction of the no-load transportation distance in level II.

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]

The primary objective of this research was to implement a more efficient handling strategy in the container terminal storage yard at the Baltic Hub Container Terminal (BHCT). Following an analysis of the current logistics system associated with starboard-side mooring, an attempt was made to shift the mooring to the port side. Two routing strategies corresponding to the analysed mooring operations were tested using a developed model. Simulations were conducted to enable a comparison of various handling strategies that were suitable for the mooring arrangements and equipment under investigation. This analysis was based on reliable information obtained from a real process. Evaluative criteria for examining the impact of changing the handling strategy of internal movement vehicles (IMVs) in the terminal storage yard in terms of process efficiency included the total time and distance of container transportation and the truck utilisation level. A new route for IMVs was developed to accommodate the changed mooring operation. This adjustment aimed to enhance the discharge process and to reduce the distance travelled, resulting in a reduction in fuel consumption. The shorter travel distance also positively impacted productivity, and contributed to a reduction in operating costs. The changes to the mooring arrangement directly resulted in a 10% increase in the quay crane gross productivity, measured in moves per hour (mph). Given that many ports do not pay attention to how ships are moored, the case study and analysis presented here can help staff quickly improve their handling strategy and productivity, thus increasing the port’s competitiveness.

Automatic driving technology provides an economical horizontal transportation automation solution for automated container terminals and the automation upgrade of existing terminals, which is highly adaptable to land conditions and terminal layout forms, and is a part of the construction of smart ports. Based on the current testing situation of Mawan Xinghai Terminal to transform an automated container terminal with driverless container trucks, this paper studies the key technical problems to be solved for the application of driverless container trucks based on automatic driving technology under current technological conditions, and puts forward the key factors for the general layout of the automated container port area under the mode of driverless container trucks, which is of great significance to promote the large-scale application of driverless container trucks in automated container terminals, and can provide reference for the construction of automated container terminals in China. [ABSTRACT FROM AUTHOR]

European Union cargo and container ports are under pressure to reduce GHG emissions and achieve carbon neutrality by 2050, as mandated by the European Commission. The pace of progress varies among ports. This study examined the characteristics influencing GHG reduction measures in European cargo and container ports and their implications for sustainable investing. The methods used in this study, such as linear regression models to analyze predictive variables, can be applied in sustainable investing to assess which factors most strongly predict a company's environmental, social, and governance performance. Using linear regression models to analyze data from the 33 busiest European ports, we identified five predictive variables: port size, cargo mix, surrounding population density, access to the sea, and the economic wealth of the host country. Our findings revealed that the port size significantly correlates with the adoption of measures to reduce scope 1, 2, and 3 emissions. This study underscores the importance of contextual and operational factors in evaluating sustainability efforts across sectors. The results contribute to drawing parallels with the field of sustainable investing within finance. This offers valuable insights for sustainable investing, emphasizing the importance of considering various contextual and operational factors when evaluating the sustainability efforts of entities in different sectors. [ABSTRACT FROM AUTHOR]

Marine container terminal (MCT) plays a key role in the marine intelligent transportation system and international logistics system. However, the efficiency of resource scheduling significantly influences the operation performance of MCT. To solve the practical resource scheduling problem (RSP) in MCT efficiently, this paper has contributions to both the problem model and the algorithm design. Firstly, in the problem model, different from most of the existing studies that only consider scheduling part of the resources in MCT, we propose a unified mathematical model for formulating an integrated RSP. The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective. Secondly, in the algorithm design, a pre-selection-based ant colony system (PACS) approach is proposed based on graphic structure solution representation and a pre-selection strategy. On the one hand, as the RSP can be formulated as the shortest path problem on the directed complete graph, the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP, which effectively avoids the generation of infeasible solutions. On the other hand, the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution. To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model, a set of test cases with different sizes is conducted. Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm, which can significantly outperform other state-of-the-art algorithms. [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]

Similar to world trade, container transportation and the tendency to transport various cargoes with containers is increasing in Türkiye. Moreover, in a world where production centers will be shifting with the Covid-19 pandemic, it can be said that Türkiye's production will increase and over and above expectations, container traffic in its ports will expand. In this context, it is anticipated that the urban of Kocaeli will need more container transportation between container ports and logistics centers. Development processes need to be well planned so that this increase in container transportation does not disrupt the traffic of cities, ports and logistics centers. This planning should be carried out by reconciling the current situation, the demands of the logistic stakeholders and the future vision. This study aims to reveal whether Kocaeli container ports, logistics centers and the urban need alternative transportation methods for economic and sustainable development and to offer solution suggestions by analyzing the opinions of all logistics stakeholders. The data collected in the research were analyzed using the Kruskal Wallis H test in the IBM SPSS 22.0 program, and the hypotheses were questioned. Reconsidering alternative opportunities in transportation for the development of ports and logistics stakeholders of Kocaeli urban in a competitive structure, increasing the road and railway transportation capacities between the ports and Köseköy Logistics center will be appropriate, a new/alternative transportation method will have a positive impact on the efficiency of the ports/logistics businesses, inland waterway/canal to the logistics center. It has been concluded that integration with transportation may be beneficial in the future and may have a moderate impact on business volume and productivity. [ABSTRACT FROM AUTHOR]

The growth of container throughput and the impact of COVID-19 have made container ports increasingly congested, leading to uncertain cargo transit times and high freight rates. Shipping parties want to know in advance the congestion of each port in the next phase to adjust their plans, but there are few studies involving congestion prediction, and this study hopes to make some additions. Three indicators are extracted from Automatic Identification System (AIS) data to represent the port congestion status for the sake of generalizability. And those indicators are used to predict port congestion status and further to predict container ships' time in port using the eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) algorithms. The established models show that those indicators can improve prediction accuracy of port time compared to not considering port status. The findings also show that port congestion status contributes significantly to determining port time and makes it fluctuate by up to nearly 50 hours. The prediction results could help shipping companies to change their transportation schedules early to bypass ports with long waiting times or to arrange ships to enter the port earlier for delivering cargos on schedule. [ABSTRACT FROM AUTHOR]

Cooperation in maritime transport is a research area that has attracted the interest of academics and practitioners. Most models published in the literature on marine container terminal cooperation focus on the planning level and consider TEUs handled at each terminal as the decision variable. At the tactical/operational level, the vessel provides a more realistic selection as the decision variable for cooperation. The objective of this paper is to analyze the impact of the choice of both decision variables for terminal intra-port cooperation. To do so, we present two main mathematical models for marine container terminal intra-port cooperation: volume-based and vessel-based models. For each model, two different cooperation schemes and seven different concession fee policies among terminals are considered. Based on the individual terminals' before and after cooperation profits, the models are used to quantitatively assess and compare the proposed cooperation schemes and concession fee policies as well as to estimate indicators of terminals' willingness to cooperate under each model. Results from numerical experiments using simulated data for three terminals (with high, medium, and low utilization) indicate significant differences in profit sharing and willingness to cooperate under the proposed cooperation schemes and concession fee policies between the volume- and vessel-based models. [ABSTRACT FROM AUTHOR]

This paper addresses the question whether energy consumption variables and the disaggregation of output matter in the context of efficiency analysis of container terminals. While it is obvious that the energy consumption of refrigerated cargo is higher than the energy consumption of non-refrigerated cargo, this work investigates whether those differences show in an overall efficiency analysis of terminals. This would point to a potentially important input for efficiency measures, to be considered in future productivity and efficiency analysis of terminals. Starting with a discussion on theoretical concepts and variable selection for measuring the energy dimension of terminal efficiency, this is the first paper that applies data envelopment analysis (DEA) comparing results with and without energy consumption, as well as differentiating productive outputs (dry and reefer container handling). The results reveal how the output disaggregation leads to substantially different efficiency scores and are a first step to show the relevance of output disaggregation and the inclusion of the energy variables as inputs in container terminal efficiency studies. [ABSTRACT FROM AUTHOR]

Purpose: This present study uses political, economic, social, technological, legal and environmental (PESTLE) analysis and the strategic management theory to examine how external factors, namely the coronavirus (COVID-19) pandemic, the industrial revolution (IR) 4.0 technologies, the fuel price crisis and Sultanate of Oman Logistics Strategy (SOLS) 2040, affect the performance of container terminals in Oman. Design/methodology/approach: A hybrid decision-making method that combined the analytical hierarchy process technique and Bayesian network model was used to achieve the objective of the present study. Findings: The COVID-19 pandemic (54.60%) most significantly affected the performance of container terminals in Oman, followed by IR 4.0 technologies (19.66%), SOLS (17.00%) and fuel price crisis (8.74%). Container terminals in Oman were also found to perform "moderately," considering the uncertainty of external factors. Research limitations/implications: This study enriches existing literature by using PESTLE analysis to assess the impact of the external business environment on firm performance in the context of the maritime industry as well as highlights how challenging external environmental factors affect the performance of container terminals in Oman. Originality/value: This study contributes to developing novel study models and determining the performance level of container terminals in Oman considering integrated uncertainties and external factors such as the COVID-19 pandemic, IR 4.0 technologies, the SOLS 2040 and the fuel price crisis. [ABSTRACT FROM AUTHOR]

This survey was aimed to assess the impact of container colour in selection of oviposition site by gravid females of Aedes aegypti and Aedes albopictus in some urban and industrial areas of Dibrugarh district. The study was carried out in an urban area known to harbour a large population of Aedes albopictus and in an industrial area with more or less small factories with a large population of Aedes aegypti using ovitraps with different colour. Results showed an increased oviposition tendency for black coloured ovitraps by Aedes aegypti and Aedes albopictus but both the species equally prefers red ovitraps for their multiplication with less oviposition in lighter coloured ovitraps such as brown and white. Means (SE) in order of efficacy for Aedes aegypti were black 145.83(17.87)>red 105.5(24.47)> blue 83.5(14.03)> green 68.17(8.66)> brown 25(8.98)> white 15(5.1) and for Aedes albopictus were black 147.5 ((19.03)> red 140.66(17.39)> green 107.83(10.76)> blue 73.5(6.48)> brown 27.67(7.07) white15.33 (3.93). Black ovitraps outperformed competing coloured with respect to choose from both Aedes aegypti and Aedes albopictus seeking artificial oviposition sites. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the daily berth planning problem for Xiamen Hai-Tian Container Terminal (XHCT) at the Port of Xiamen, China, and propose the development and implementation of a berth planning system. The aim of the berth planning problem is to optimize daily berth plans by considering various decisions, including berth allocation, quay crane assignment, fairway traffic control, and berthing safety requirements. Among these decisions, the berthing safety requirement is a novel but practical problem in berth allocation that concerns the resource allocation related to berthing safety and interrelation with other decisions. A mathematical programming-driven methodological framework is designed with a 0-1 integer linear programming model for problem formulation and a highly efficient decomposition heuristic algorithm for solving the problem. This framework establishes the core for the berth planning system. The adoption of the berth planning system contributes to the increase of container throughput and berth capacity by transforming the planning process of XHCT. Moreover, the mathematical programming-driven daily berth planning informs further intelligent operations development in the Port of Xiamen and other container ports. History: This paper was refereed. Funding: This research was supported by the National Natural Science Foundation of China [Grants 72394360, 72394362, 72025103, 71831008, 72361137001, 72071173, and 72371221]. [ABSTRACT FROM AUTHOR]

The Bohai Rim region constitutes the third prominent "growth pole" in China's economic landscape, wherein the Bohai Rim multi-port system, encompassing Tianjin Port, Dalian Port, and Qingdao Port, engages in intense competition to establish itself as the foremost shipping hub in northern China. This study compares the ternary diagram method and employs the comprehensive concentration index (CCI), Lerner index (LI), and spatial shift-share analysis (SSSA) methods to delve into the intricacies of concentration, inequality, and evolving competitive dynamics within the Bohai Rim multi-port system over the four decades spanning from 1981 to 2023. The aim is to analyze the evolutionary trajectory and underlying dynamic mechanisms of this multipartite port system. The analysis delineates the development trajectory of the system into three stages: the dominant stage of Tianjin Port from 1981 to 1990, the efficiency competition stage from 1991 to 1996, and the ascendancy of Qingdao Port from 1997 to 2023. The results indicate that: (i) the Bohai Rim multi-port system exhibits a relatively low level of concentration, ensuring balanced growth within a non-monopolistic competitive environment; (ii) the internal competitiveness of the Bohai Rim multi-port system has gradually shifted from Tianjin Port to Qingdao Port, with Dalian Port experiencing steady development in its container transport capabilities. (iii) Dalian Port has witnessed a decline in container throughput since 2015, indicating a weakening competitive posture. These revelations suggest that Qingdao Port is a viable candidate for development into the northern China shipping center, leveraging its increasing competitiveness and strategic location. The method applied in this study may also prove beneficial for similar multi-port systems elsewhere. [ABSTRACT FROM AUTHOR]

Accurate recognition and prediction of the multi-level handling complexity in automated container terminals (referred to as "automated terminals") is a prerequisite for improving the effectiveness of scheduling and realizing intelligent operation and maintenance. According to the operating characteristics of the automated terminal equipment, the operating network is constructed of automated terminals that describe the characteristics of operating complexity. We use K-medoids and a light gradient boosting machine (LightGBM) to construct a K-LightGBM model that recognizes multi-level handling complexity. The key lies in the reasonable construction of prediction models. A hyper-heuristic autoregressive integrated moving average (ARIMA) model is proposed to address the problem that the ARIMA is ineffective in predicting nonlinear data. We combine ARIMA and the LightGBM model to establish an ARIMA-LightGBM model to predict multi-level handling and residuals. To improve accuracy, we propose the two residual prediction strategies of direct prediction and limited residual boundary prediction based on the residuals generated by ARIMA. We propose a hyper-heuristic algorithm based on a gradient descent-trust region (GD-TR) to compute the weights of predicted values under the two strategies, which improves the global search capability by GD and TR. The particle swarm optimization algorithm, simulated annealing algorithm, and ant colony optimization algorithm are low-level heuristics. Simulation results show that the proposed model possesses the lowest root mean square error on all characteristics compared to ARIMA, E-ARIMA, and ARIMA-LSTM. Therefore, the proposed model is very effective in improving the accuracy of predicting the multi-level handling complexity at automated terminals. [ABSTRACT FROM AUTHOR]