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The global seaports are of pivotal importance for the world economy. Since 1990, global container traffic has grown by an average of 10% annually. Equally, the steady growth of ship sizes poses major logistical and technical problems worldwide. Given these facts, shipping and maritime logistics would largely benefit from Big Data as well as the emerging digital technologies. Apart from the many positive effects of digitization in maritime logistics with respect to efficiency, safety and energy saving, there are, however, also risks (e.g. data abuse, cybercrime). Based on a systematic literature review, this article provides an overview of the current state of digitization in maritime logistics, discusses existing problem areas, and shows potential for improvement. The results show that it is essential to capture the development potential in order to be able to benefit from the advantages. However, research is still in its initial stages, and there is a lack of theoretical and empirical work as well as explanatory approaches to appropriate recommendations for action and restructuring.

Abstract Marine container terminals play a significant role for international trade networks and global market. To cope with the rapid and steady growth of the seaborne trade market, marine container terminal operators must address the operational challenges with appropriate analytical methods to meet the needs of the market. The berth allocation and scheduling problem is one of the important decisions faced by operators during operations planning. The optimization of a berth schedule is strongly associated with the allocation of spatial and temporal resources. An optimal and robust berth schedule remarkably improves the productivity and competitiveness of a seaport. A significant number of berth allocation and scheduling studies have been conducted over the last years. Thus, there is an existing need for a comprehensive and critical literature survey to analyze the state-of-the-art research progress, developing tendencies, current shortcomings, and potential future research directions. Therefore, this study thoroughly selected scientific manuscripts dedicated to the berth allocation and scheduling problem. The identified studies were categorized based on spatial attributes, including discrete, continuous, and hybrid berth allocation and scheduling problems. A detailed review was performed for the identified study categories. A representative mathematical formulation for each category was presented along with a detailed summary of various considerations and characteristics of every study. A specific emphasis was given to the solution methods adopted. The current research shortcomings and important research needs were outlined based on the review of the state-of-the-art. This study was conducted with the expectation of assisting the scientific community and relevant stakeholders with berth allocation and scheduling. Graphical Abstract Graphical Abstract

This paper presents a decision support system for the core problem of berth allocation decision in a container terminal. The allocation of berths to the calling vessels is complex with the fact that different service level requirements are required for different vessels. Terminal managers demand for effective decision support systems that would aid them with the allocation problem considering service priorities. Consequently, this study provides a DSS, built by a dynamic discrete-event simulation model embedded with an optimization tool that determines the priority controls for the berth allocation to the calling vessels. To show the practical application of the DSS, a comprehensive case study from a Turkish container terminal considering the current state and future expansion plans that also provides an indication of the usability aspect of the program on other ports around the world has been conducted. Further experiments are conducted based on data from the Port of Rotterdam. The DSS presented in this study may help port authorities in determining more efficient allocation decisions within a container terminal.

We propose a novel semiopen queuing network (SOQN) model for the interterminal transportation (ITT) problem where multiple container terminals use a common fleet of vehicles (automated lift vehicles, automated guided vehicles, multitrailers, and barges) to transport containers between terminals. To solve the overall queuing network, our solution approach uses a network decomposition method where the original SOQN is decomposed to a closed and an open queuing network (with bulk-service capacity). To our knowledge, this is the first work that considers bulk service in SOQNs. We develop theoretical upper and lower bounds on the throughput time estimates of our model, and provide an extension for the case when service times at the terminal handling stations depend on the number of containers being loaded/unloaded. We numerically validate our model using simulated data where we find that our model results in errors of less than 5% for vehicle utilization. We also show that our model results in better estimates for the ITT problem when compared to existing approaches in the literature. Finally, we apply our model to real-world data from the Port of Rotterdam and show that it can be used to analyze throughput time trade-offs with alternate dwell point policies, different vehicle types, and variable vehicle capacities. The online appendix is available at https://doi.org/10.1287/trsc.2016.0726 .

Due to increasing container traffic and mega-ships, many seaports face challenges of huge amounts of truck arrivals and congestion problem at terminal gates, which affect port efficiency and generate serious air pollution. To solve this congestion problem, we propose a solution of managing truck arrivals with time windows based on the truck-vessel service relationship, specifically trucks delivering containers for the same vessel share one common time window. Time windows can be optimized with different strategies. In this paper, we first propose a framework for installing this solution in a terminal system, and second develop an optimization model for scaling time windows with three alternative strategies: namely fixed ending-point strategy (FEP), variable end-point strategy and greedy algorithm strategy. Third, to compare the strategies in terms of effectiveness, numerical experiments are conducted based on real data. The result shows that (1) good planning coordination is essential for the proposed method; and (2) FEP is found to be a better strategy than the other two.

PurposeThe paper undertakes a bibliometric analysis and assessment of journal publications in the field of container terminal operations research (CTOR), in an attempt to identify high-impact papers (HIPs) published in Science Citation Index/Social Science Citation Index (SCI/SSCI) journals of CTOR subject category from 1973 to 2020.Design/methodology/approachA structured approach for identifying the HIPs is developed based on the utilization of bibliometric and network analyses.FindingsThe CTOR papers are assessed in terms of publication outputs, distribution of outputs in SCI/SSCI journals, authorship, institutions and countries, as well as citation life cycles of papers with the highest total citations since their publication until the year 2020. The results show that between 1989 and 2015, there were 82 HIPs in the field of CTOR, which have been cited at least 200 times, with more than 50% of these citations allocated in the second part of paper citation life cycle according to the database of Google Scholar.Practical implicationsThe practical implication of the aforementioned reviewing and assessing journal publications of CTOR is that it offers the ability to reveal the tone of its development through addressing main characteristics of the relevant HIPs as determined by the highly cited papers in this field of research.Originality/valueThis paper offers a unique analysis and assessment in the field of CTOR by identifying the relevant HIPs and their associated scientific actors (authors, institutions and countries), thus facilitating the future research effort in the field of CTOR.

This paper treats a berth allocation problem (BAP) in dedicated container terminals where feeder ships and container vessels are jointly served. When assigning quay space and a service time to each calling ship particular focus is put on the container exchange between feeder ships and mother vessels, so that the weighted number of containers delivered by feeder missing their intended mother vessel (and vice versa) does not exceed a given upper bound. The resulting BAP is formalized, complexity proofs are provided, and suited optimization procedures are presented and tested.

Ports are essential nodes in global trade, linking maritime and land transport. As maritime logistics increasingly drive global supply chains, optimizing port operations has become vital for enhancing economic efficiency and environmental sustainability. This study presents a Mixed Integer Linear Programming (MILP) model to address inefficiencies in berth allocation and stevedoring processes at the Port of Leixões, Portugal. By integrating real operational data, the model reduces vessel waiting times by 47.56% (from 8.1 to 4.2 h) and operational delays by 37.39% (from 11.5 to 7.2 h). These optimizations also result in a 41.85% reduction in greenhouse gas emissions per ship, aligning with global emissions regulations and promoting sustainable port management. The model’s innovations include real-time data integration and a holistic resource allocation approach to mitigate congestion and inefficiencies. Key findings demonstrate the model’s potential to streamline operations and minimize environmental impacts. These advancements align economic efficiency with environmental sustainability, addressing global emissions regulations. However, the study acknowledges limitations, such as excluding unpredictable factors like weather conditions and equipment failures. Future research should explore dynamic variables, such as weather conditions and mechanical failures, and expand the model’s applicability to other seaports worldwide.

Productivity is an extremely important factor in the competitiveness of a container terminal. Productivity itself is largely influenced by the handling mechanisation, the main task of which is to serve the quay cranes on one side and the yard cranes on the other. One of the most effective types of mechanisation in this segment is shuttle carriers, which are used primarily in the larger terminals. The objective of this study was to determine if they can also be used effectively in smaller container terminals with different yard layouts. Therefore, this study shows the impact of the selected layout and handling mechanisation on terminal productivity. The focus is on the berth productivity, as it has the greatest impact on the handling of the vessel in the port. To this end, a discrete-event simulation modelling approach was used in a container terminal of small capacity. The performed simulations included all operations between berth and yard, focusing on the correct allocation of shuttle carriers to ensure optimal results on the sea side of the terminal. The result showed that the use of shuttle carriers brings a substantial performance effect to the container terminal and also has a different effect considering different terminal layouts in terminals with an annual throughput not exceeding 1 million TEU.

This paper considers the problem of scheduling quay cranes which are used at sea port container terminals to load and unload containers. This problem is studied intensively in a recent stream of research but still lacks a correct treatment of crane interference constraints. We present a revised optimization model for the scheduling of quay cranes and propose a heuristic solution procedure. At its core a Branch-and-Bound algorithm is applied for searching a subset of above average quality schedules. The heuristic takes advantage from efficient criteria for branching and bounding the search with respect to the impact of crane interference. Although the used techniques are quite standard, the new heuristic produces much better solutions in considerably shorter run times than all algorithms known from the literature.