Search Results (55)

Shipping containers are vital to the transportation industry due to their cost-effectiveness and compatibility with intermodal systems. With the significant increase in container usage since the mid-20th century, manual tracking at port terminals has become inefficient and prone to errors. Recent advancements in Deep Learning for object detection have introduced Computer Vision as a solution for automating this process. However, challenges such as low-quality images, varying font sizes & illumination, and environmental conditions hinder recognition accuracy. This study explores various architectures and proposes a Container Code Localization Network (CCLN), utilizing ResNet and UNet for code identification, and a Container Code Recognition Network (CCRN), which combines Convolutional Neural Networks with Long Short-Term Memory to convert the image text into a machine-readable format. By enhancing existing shipping container localization and recognition datasets with additional images, our models exhibited improved generalization capabilities on other datasets, such as Syntext, for text recognition. Experimental results demonstrate that our system achieves $97.93\%$ accuracy at $64.11$ frames per second under challenging conditions such as varying font sizes, illumination, tilt, and depth, effectively simulating real port terminal environments. The proposed solution promises to enhance workflow efficiency and productivity in container handling processes, making it highly applicable in modern port operations. Full article

The port of Gaženica, managed by the Port Authority of Zadar, is open to public traffic of special economic interest to the Republic of Croatia. Situated outside Zadar’s city centre, with convenient access to the airport and A1 highway, this port presents significant opportunities for Zadar County’s economic growth. While also serving as a cargo and fishing port, as the second-largest passenger port in Croatia, the port of Gaženica prioritises the development of cruise ship traffic. The expansion of intermodal traffic is being facilitated through the development of a multipurpose terminal to accommodate general, roll-on/roll-off, and containerised cargo (full and empty containers). The rising number of passenger ships—particularly cruise ships—along with the increasing passenger, vehicle, and cargo traffic, poses a significant risk of pollution due to dust, noise, greenhouse gases, and other pollutants. Considering these risks, the use of alternative energy sources, decarbonisation of maritime transport, the separation of waste by type, and the proper handling and disposal of ship waste are of utmost importance. The aim of this study is to present and analyse the green transition process of the port of Gaženica through the results that have been achieved or are yet to be achieved through the implementation of green projects by the Port Authority of Zadar. For this purpose, a mixed-methods approach combining project analysis and the qualitative analysis of emissions data is used. It is important to highlight that the method of interviews with relevant representatives of institutions involved in the project was also used to gain insight into financial and infrastructural challenges, the accessibility of certain data, and potential improvements in implementation. The research results indicate that the port of Gaženica has completed four green projects, while another four are currently being implemented, with their completion expected by 2026. The research concludes that it is necessary to strengthen environmental awareness regarding proper waste disposal among all stakeholders in maritime transport, including the local community, businesses, and local authorities. The results demonstrate a need to focus on certification with the aim of strengthening the green transition process through involvement in the EcoPorts and Green Award certification schemes. It is also necessary to actively improve the public availability of data from the base station in the port of Gaženica to inform the public about environmental impacts in real time (24/7) while facilitating data collection for statistical reporting purposes. Full article

Efficient container stacking is a critical factor for the performance of intermodal terminals. This study evaluates how classical, hybrid, and LLM-assisted heuristic stacking strategies perform when terminals operate under incomplete or uncertain schedule information. A simulation model of a 4 × 5 × 3 yard was developed, comparing three strategies: a layer-based rule (LAY), a hybrid heuristic (SVD), and an adaptive heuristic supported by a large language model (ChatGPT-4), rather than a full ML/RL model. Each scenario (0%, 25%, 50%, and 100% schedule visibility) was repeated 10 times with controlled random seeds. Results show that under full schedule information, the LLM-assisted strategy reduced relocations by up to 35% and crane operating time by 28% compared to deterministic methods. However, its performance degraded with partial visibility, sometimes falling behind the hybrid strategy, which remained more stable across scenarios. Standard deviations confirmed that differences between methods were statistically significant. The findings highlight both the potential and the limitations of LLM-assisted heuristics: they can outperform classical approaches in data-rich environments but may overreact to incomplete inputs without explicit data quality assessment. This study should therefore be regarded as a simulation-based proof-of-concept, with further validation on real operational data required to confirm its applicability. Full article

Ports represent the point of intersection between sea and land, as well as a crucial node for the integration of maritime and land transport in the global logistics chain. Consequently, it is crucial to consider an articulated system that includes dry ports, freight interchange and intermodal logistics platforms, since the relationships between the port and the city, as well as those between the different decision-makers involved, are multiple and complex. Maritime transport and port operations have a direct and indirect impact on the surrounding contexts, with significant effects, particularly from an environmental point of view. Therefore, the green transition in logistics, port, and maritime systems is essential for reducing these impacts. In this context, the aspects related to operational practices and terminal design are of great importance. This paper aims to explore sustainable strategies for ports and maritime logistics in order to provide a methodological approach to green transition. The proposed methodology was divided into phases. First, an analysis of international and European legislation was conducted in order to identify the main critical issues. Subsequently, a review of the existing literature and best practices was carried out to identify tested solutions. The third phase included a Stakeholder Engagement Process, centred on the use of a thematic focus group to foster a collaborative approach to the definition of priorities and operational strategies. Part of the proposed methodology was implemented as part of the DEMASTER—Design of Maritime Sustainable Terminals—project, and it can allow for the evaluation of the different options and the identification of more effective and innovative solutions for the green transition. Full article

The spatial separation of port yards and railway hubs, which relies on external truck drayage as a necessary link, hampers the seamless transshipment of sea–rail intermodal containers between ports and railway hubs. This creates challenges in synchronizing yard cranes (YCs) at the port terminal, external trucks (ETs) on the road, and rail-mounted gantry cranes (RMGs) at the railway hub. However, most existing studies focus on equipment scheduling or container transshipment organization under the port–railway integration mode, often overlooking critical time window constraints, such as train schedules and export container delivery deadlines. Therefore, this study investigates the collaborative scheduling of YCs, ETs, and RMGs for synchronized loading and unloading under the port–railway separation mode. A mixed-integer programming (MIP) model is developed to minimize the maximum makespan of all tasks and the empty-load time of ETs, considering practical time window constraints. Given the NP-hard complexity of this problem, an improved genetic algorithm (GA) integrated with a “First Accessible Machinery” rule is designed. Extensive numerical experiments are conducted to validate the correctness of the proposed model and the performance of the solution algorithm. The improved GA demonstrates a 6.08% better solution quality and a 97.94% reduction in computation time compared to Gurobi for small-scale instances. For medium to large-scale instances, it outperforms the adaptive large neighborhood search (ALNS) algorithm by 1.51% in solution quality and reduces computation time by 45.71%. Furthermore, the impacts of objective weights, equipment configuration schemes, port–railway distance, and time window width are analyzed to provide valuable managerial insights for decision-making to improve the overall efficiency of sea–rail intermodal systems. Full article

The sea–rail intermodal container terminal serves as a key transportation hub for green logistics, where efficient resource coordination directly enhances multimodal connectivity and operational synergy. To address limited storage capacity and trans-shipment inefficiencies, this study innovatively proposes a resource-sharing strategy between the seaport and the railway container terminal, focusing on the joint allocation of yard space and internal trucks. For indirect trans-shipment operations between ships, the port, the railway container terminal, and trains, a mixed-integer programming model is formulated with the objective of minimizing the container trans-shipment cost and the weighted turnaround time of ships and trains. This model simultaneously determines yard allocation, container transfers, and truck allocation. A two-layer hybrid heuristic algorithm incorporating adaptive Particle Swarm Optimization and Greedy Rules is designed. Numerical experiments verify the model and algorithm performance, revealing that the proposed method achieves an optimality gap of only 1.82% compared to CPLEX in small-scale instances while outperforming benchmark algorithms in solution quality. And the shared yard strategy enhances ship and train turnaround efficiency by an average of 33.45% over traditional storage form. Sensitivity analysis considering multiple realistic factors further confirms the robustness and generalizability. This study provides a theoretical foundation for sustainable port–railway collaboration development. Full article

The U-shaped automated container terminal (U-ACT) meets the requirements of sea-rail intermodal transportation with its unique layout. However, this layout also presents challenges, such as complex container transshipment planning and challenging equipment scheduling, which limit further improvements in overall efficiency. This paper focuses on the integrated scheduling of horizontal transportation and container-handling equipment for container transshipment at U-ACT. To minimize operation time and energy consumption while addressing path conflicts among container trucks, we designed a two-layer scheduling model to generate an optimal scheduling scheme for each automated device. Given the complexity of the problem, we developed a reinforcement learning-driven hyper-heuristic algorithm (RLHA) capable of efficiently searching for near-optimal solutions. Small-scale experiments demonstrate that our RLHA outperforms other algorithms, improving optimization results by 5.14% to 28.87% when the number of container operation tasks reaches 100. Finally, large-scale experiments were conducted to analyze key factors impacting sea-rail intermodal transport operations at U-ACT, providing a foundation for practical optimization. Full article

Background: The operational roles of ports are deeply intertwined with the development of their surrounding hinterlands, indicating that their relationship is mutually dependent and interactive. Moreover, the growth of a port positively influences the development of its hinterland, stimulating economic activities and improving overall regional development by enhancing competitiveness. Inland terminals, often considered “extended gateways” to ports, play a crucial role in integrating ports with their hinterlands. These terminals facilitate the adjustment of cargo transportation to align with the operational dynamics of ports. This paper presents a framework designed to clarify the importance of fostering strong links between ports and their hinterlands through the establishment of inland terminals. Methods: By applying a conceptual framework and methodologies of literature review and content analysis, this paper highlights the critical importance of developing robust connections between ports and their hinterlands through the implementation of inland terminals. Results and Conclusions: The findings of this research can help map the scientific knowledge related to various metrics of port–hinterland connectivity, outline relevant thematic areas, visualize the relationships between ports and their hinterlands, and identify prevailing research gaps and potential research paths in the context of inland terminal integration into the port system. Full article

The ongoing conflict in Ukraine has significantly disrupted global food supply chains, exacerbating existing food security challenges. To mitigate these disruptions, this study proposes a comprehensive approach to establishing sustainable intermodal terminals and technology parks along the Ukrainian–Polish border. To address this research issue, we analyzed the Ukrainian and global grain markets using publicly available statistical data. This analysis revealed the need to enhance grain transit through Poland, with terminal development identified as a crucial factor. Furthermore, a thorough analysis of the Polish freight rail transport market provided forecasts of potential demand for rail transit. Utilizing Petri nets as a modeling tool, we simulated the transit system at the macro level. Based on this simulation, we identified potential locations for freight terminals at the Ukrainian border near EU countries. Employing the AHP methodology, we evaluated these potential locations and selected Kovel in the Volyn region of Ukraine as the most promising alternative. For this location, we proposed the development of a new technological park. The implementation of this project, with the capacity to process and clear up to 600 wagons per day, would facilitate the transshipment of up to 3000 tons of grain per day from Ukraine to EU countries. Full article

As an effective solution to the first- and last-mile logistics of door-to-door intermodal container transportation, inland container transportation involves transporting containers by truck between terminals, depots, and customers within a local area. This paper is the first to focus specifically on the inland container transportation problem with limited depot capacity, where the storage of empty containers is constrained by physical space limitations. To reflect a more realistic scenario, we also consider the initial stock levels of empty containers at the depot. The objective of this problem is to schedule trucks to fulfill inland container transportation orders such that the overall cost is minimum and the depot is neither out of stock or over stocked at any time. A novel graphical representation is introduced to model the constraints of empty containers and depot capacity in a linear form. This problem is then mathematically modeled as a mixed-integer linear programming formulation. To avoid discretizing the time horizon and effectively achieve the optimal solution, we design a tailored branch-and-price-and-cut algorithm where violated empty container constraints for critical times are dynamically integrated into the restricted master problem. The efficiency of the proposed algorithm is enhanced through the implementation of several techniques, such as a heuristic label-setting method, decremental state-space relaxation, and the utilization of high-quality upper bounds. Extensive computational studies are performed to assess the performance of the proposed algorithm and justify the introduction of enhancement strategies. Sensitivity analysis is additionally conducted to investigate the implications of significant influential factors, offering meaningful managerial guidance for decision-makers. Full article

Numerous studies address the estimation of energy consumption at intermodal terminals, with a primary focus on existing facilities. However, a significant research gap lies in the lack of reliable methods and tools for the ex ante estimation of energy consumption in transshipment systems. Such tools are essential for assessing the energy demand and intensity of intermodal terminals during the design phase. This gap presents a challenge for intermodal terminal designers, power grid operators, and other stakeholders, particularly in an era of growing energy needs. The authors of this paper identified this issue in the context of a real business case while planning potential intermodal terminal locations along new railway lines. The need became apparent when power grid designers requested energy consumption forecasts for the proposed terminals, highlighting the necessity to formulate and mathematically solve this problem. To address this challenge, a three-stage model was developed based on a pre-designed intermodal terminal. Stage I focused on establishing the fundamental assumptions for intermodal terminal operations. Key parameters influencing energy intensity were identified, such as the size of the transshipment yard, the types of loading operations, the number of containers handled, and the selection of handling equipment. These parameters formed the foundation for further analysis and modeling. Stage II focused on determining the optimal number of machines required to handle a given throughput. This included determining the specific parameters of the equipment, such as speed, span, and efficiency coefficients, as well as ensuring compliance with installation constraints dictated by the terminal layout. Stage III focused on estimating the energy consumption of both individual handling cycles and the total consumption of all handling equipment installed at the terminal. This required obtaining detailed information about the operational parameters of the handling equipment, which directly influence energy consumption. Using these parameters and the equations outlined in Stage III, the energy consumption for a single loading cycle was calculated for each type of handling equipment. Based on the total number of loading operations and model constraints, the total energy consumption of the terminal was estimated for various workload scenarios. In this phase of the study, numerous test calculations were performed. The analysis of testing parameters and the specified terminal layout revealed that energy consumption per cycle varies by equipment type: rail-mounted gantry cranes consume between 5.23 and 8.62 kWh, rubber-tired gantry cranes consume between 3.86 and 7.5 kWh, and automated guided vehicles consume approximately 0.8 kWh per cycle. All handling equipment, based on the adopted assumptions, will consume between 2200 and 13,470 kWh per day. Based on the testing results, a methodology was developed to aid intermodal terminal designers in estimating energy consumption based on variations in input parameters. The results closely align with those reported in the global literature, demonstrating that the methodology proposed in this article provides an accurate approach for estimating energy consumption at intermodal terminals. This method is also suited for use in ex ante cost–benefit analysis. A sensitivity analysis revealed the key variables and parameters that have the greatest impact on unit energy consumption per handling cycle. These included the transshipment yard’s dimensions, the mass of the equipment and cargo, and the nominal specifications of machinery engines. This research is significant for present-day economies heavily reliant on electricity, particularly during the energy transition phase, where efficient management of energy resources and infrastructure is essential. In the case of Poland, where this analysis was conducted, the energy transition involves not only switching handling equipment from combustion to electric power but, more importantly, decarbonizing the energy system. This study is the first to provide a methodology fully based on the design parameters of a planned intermodal terminal, validated with empirical data, enabling the calculation of future energy consumption directly from terminal technical designs. It also fills a critical research gap by enabling ex ante comparisons of energy intensity across transport chains, an area previously constrained by the lack of reliable tools for estimating energy consumption within transshipment terminals. Full article

The logistics transportation system is critical to the United States economy. Underground Logistics Transportation (ULT) is a class of automated transportation systems in which vehicles carry freight through pipelines and tunnels between terminals. Being able to use a part of the underground space of existing highways will greatly facilitate the construction of such pipelines and tunnels and reduce their construction costs. Underground Logistics Transportation (ULT) could be the answer to make freight transport more sustainable and competitive. Texas highways and railroads are expected to increase by nearly 207% from 2003 to 2030. Truck tonnage will grow by 251%, while rail tonnage is forecasted to increase 118%. The number of trucks carrying NAFTA goods will increase by 263%, and the number of rail units will grow by 195%. This will have a profound impact on the highway and rail systems. The objective of this paper is to present requirements and operational components for three types of ULT lines: standard shipping containers, a standard crate size, and a standard pallet size. This study examines the use of ULT as a mode of underground transportation with the help of three case studies. This research shows that ULT is financially viable, feasible, greener, cost effective, and can become an important part of intermodal freight mobility. Full article

Intermodal terminals and warehouses operate in different countries and deliver specific services to their customers. For many clients, it is important to receive a full set of the logistics services delivered by a single operator. However, individual intermodal terminals and warehouses may face challenges with providing these services, e.g., just-in-time goods delivery, goods distribution, cargo handling in non-standard situations, and others. In such cases, the cooperation between logistics companies may be required to organize the comprehensive service of cargo within supply chains. One of the possible solutions is to integrate transport and logistics services providers, establishing their cooperation within one virtual logistics center. The aim of this article is to justify theoretically the possibility of creating such a center by combining services performed by the intermodal terminals and warehouses already in operation under a single entity, in order to minimize the cost of logistics services and the time of goods delivery, as well as to create a comprehensive range of logistics services needed by customers. The relevance of the article and the novelty of the idea are associated with justification of the possibility of combining the activities of intermodal terminals and warehouses located separately in the region in order to improve the logistical service of customers. The theoretical basis for creating a virtual logistics center is based on graph theory methods. The article presents a theoretical model, based on a system of edges and vertices of the graph tree, which corresponds to the activities performed by separately located intermodal terminals and individual warehouses. The discussion is focused on the current problems of creating virtual logistics centers. The research results may be interesting for the managers of intermodal terminals, warehouses, and logistics centers, as well as other decision-makers involved in supply chains implementation and development. Full article

This study addresses the critical gap in the literature regarding the energy efficiency of intermodal terminals in smart cities, mainly focusing on crane operations during train loading processes. Novelty’s contribution lies in developing a comprehensive simulation model in FlexSim, where quantitative analysis of crane energy consumption, factoring in container location in the storage yard, rehandling operations, and crane movement strategies were performed. Moreover, the analysis of hoist, trolley, and gantry movements was performed to evaluate their impact on overall container loading process energy efficiency. The findings reveal that the choice of train loading method significantly influences crane energy consumption, thereby affecting the operational costs, environmental footprint, and energy efficiency of the logistics hub in the form of an intermodal terminal. This research provides a methodology for assessing and enhancing the energy efficiency of intermodal terminals and highlights the broader implications for smart city sustainability goals, including reduced greenhouse gas emissions, lower operating costs, and improved transportation infrastructure. The outcomes of this research can possibly support smart city planners and logistics managers in making informed decisions to optimise intermodal terminal operations, contributing to urban areas’ sustainable development and economic resilience. Full article

Efficient damage detection of trailers is essential for improving processes at inland intermodal terminals. This paper presents an automated damage detection (ADD) algorithm for trailers utilizing ensemble learning based on YOLOv8 and RetinaNet networks. The algorithm achieves 88.33% accuracy and an 81.08% F1-score on the real-life trailer damage dataset by leveraging the strengths of each object detection model. YOLOv8 is trained explicitly for detecting belt damage, while RetinaNet handles detecting other damage types and is used for cropping trailers from images. These one-stage detectors outperformed the two-stage Faster R-CNN in all tested tasks within this research. Furthermore, the algorithm incorporates slice-aided hyper inference, which significantly contributes to the efficient processing of high-resolution trailer images. Integrating the proposed ADD solution into terminal operating systems allows a substantial workload reduction at the ingate of intermodal terminals and supports, therefore, more sustainable transportation solutions. Full article