Search Results (59)

The berth allocation and crane assignment problem (BACAP) is a key challenge in port logistics, particularly under dynamic and uncertain vessel arrival conditions. To address the limitations of existing methods in handling large-scale and high-disturbance scenarios, this paper proposes a novel optimization framework: Ternary Historical Memory-based Robust Clustered Particle Swarm Optimization (THM-RCPSO). In this method, the initial particle swarm is divided into multiple clusters, each conducting local searches to identify regional optima. These clusters then exchange information to iteratively refine the global best solution. A ternary historical memory mechanism further enhances the optimization by recording and comparing the best solutions from three different strategies, ensuring guidance from historical performance during exploration. Experimental evaluations on 25 dynamic BACAP benchmark instances show that THM-RCPSO achieves the lowest average vessel dwell time in 22 out of 25 cases, with the lowest overall average rank among five tested algorithms. Specifically, it demonstrates significant advantages on large-scale instances with 150 vessels, where it consistently outperforms competing methods such as HRBA, ACO, and GAMCS in both solution quality and robustness. These results confirm THM-RCPSO’s strong capability in solving dynamic and large-scale DBACAP scenarios with high disturbance levels. Full article

Reverse engineering of internal vehicle communication is a crucial discipline in vehicle benchmarking. The process presents a time-consuming procedure associated with high manual effort. Car manufacturers use unique signal addresses and encodings for their internal data. Accessing this data requires either expensive tools suitable for the respective vehicles or experienced engineers who have developed individual approaches to identify specific signals. Access to the internal data enables reading the vehicle’s status, and thus, reducing the need for additional test equipment. This results in vehicles closer to their production status and does not require manipulating the vehicle under study, which prevents affecting future test results. The main focus of this approach is to reduce the cost of such analysis and design a more efficient benchmarking process. In this work, we present a methodology that identifies signals without physically manipulating the vehicle. Our equipment is connected to the vehicle via the On-Board Diagnostics (OBD)-II port and uses the Unified Diagnostics Service (UDS) protocol to communicate with the vehicle. We access, capture, and analyze the vehicle’s signals for future analysis. This is a holistic approach, which, in addition to decoding the signals, also grants access to the vehicle’s data, which allows researchers to utilize state-of-the-art methodologies to analyze their vehicles under study by greatly reducing necessary experience, time, and cost. Full article

Structure-from-motion (SfM) is a foundational technology that facilitates 3D scene understanding and visual localization. However, bundle adjustment (BA)-based SfM is usually very time-consuming, especially when dealing with numerous unknown focal length cameras. To address these limitations, we proposed a novel SfM system based on pose-only adjustment (PA) for intrinsic and extrinsic joint optimization to accelerate computing. Firstly, we propose a base frame selection method based on depth uncertainty, which integrates the focal length and parallax angle under a multi-camera system to provide more stable depth estimation for subsequent optimization. We explicitly derive a global PA of joint intrinsic and extrinsic parameters to reduce the high dimensionality of the parameter space and deal with cameras with unknown focal lengths, improving the efficiency of optimization. Finally, a novel pose-only re-triangulation (PORT) mechanism is proposed for enhanced reconstruction completeness by recovering failed triangulations from incomplete point tracks. The proposed framework has been demonstrated to be both faster and comparable in accuracy to state-of-the-art SfM systems, as evidenced by public benchmarking and analysis of the visitor photo dataset. Full article

Routing and application mapping are critical stages in the design of continuous-flow microfluidic biochips (CFMBs). The routing stage determines the channel network connecting components and ports, while application mapping schedules fluid transportation and wash operations based on the designed biochip architecture. Existing methods typically handle these stages separately: routing focuses solely on physical metrics without considering subsequent scheduling requirements, while application mapping adopts one-shot scheduling strategies that can lead to suboptimal solutions. This paper proposes an integrated path-driven methodology that jointly optimizes routing and application mapping. For routing, we develop a hybrid particle swarm optimization algorithm that incorporates conflict awareness and channel utilization strategies. For application mapping, we introduce an iterative approach that leverages historical scheduling information to progressively optimize fluidic-handling and wash operations. Experimental results on both real and synthetic benchmarks demonstrate significant improvements over state-of-the-art methods, achieving reductions of 22.05% in total channel length, 21.79% in intersections, 21.97% in total delay time, and 8.30% in biochemical reaction completion time. The proposed methodology provides an effective solution for the automated design of CFMBs with enhanced physical and operational efficiency. Full article

As global sustainability goals gain momentum, seaports are playing a pivotal role in driving environmentally and socially responsible practices. In light of the International Maritime Organization’s emission reduction targets, transparent and effective Environmental and Social (E&S) reporting has become increasingly vital. This study critically examines current E&S reporting practices in the port industry through an analysis of recent disclosures from major European and global ports, supported by a review of academic and industry literature. The research explores how sustainability reports address key themes such as CO₂ emissions, energy efficiency, health and safety, operational performance, and biodiversity. While the presence of numerous indicators reflects a commitment to comprehensive sustainability, the proliferation of metrics poses challenges for clarity, comparability, and stakeholder engagement. The abundance of data risks diluting focus, complicating benchmarking, and may even contribute to greenwashing. Without standardization and strategic alignment, reporting can become counterproductive. This study advocates for a harmonized set of performance indicators that remain flexible enough to reflect port-specific strategies, yet are consistent with global benchmarks. Achieving this balance will require collaboration among researchers, industry leaders, and policymakers to develop transparent, adaptive E&S reporting frameworks that support meaningful progress in ports’ sustainability. 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

Synthetic aperture radar (SAR) is characterized by its all-weather monitoring capabilities and high-resolution imaging. It plays a crucial role in operations such as marine salvage and strategic deployments. However, existing vessel detection technologies face challenges such as occlusion and deformation of targets in multi-scale target detection and significant interference noise in complex scenarios like coastal areas and ports. To address these issues, this paper proposes an algorithm based on YOLOv8 for detecting ship targets in complex backgrounds using SAR images, named DFENet (Denoising and Feature Enhancement Network). First, we design a background suppression and target enhancement module (BSTEM), which aims to suppress noise interference in complex backgrounds. Second, we further propose a feature enhancement attention module (FEAM) to enhance the network’s ability to extract edge and contour features, as well as to improve its dynamic awareness of critical areas. Experiments conducted on public datasets demonstrate the effectiveness and superiority of DFENet. In particular, compared with the benchmark network, the detection accuracy of mAP75 on the SSDD and HRSID is improved by 2.3% and 2.9%, respectively. In summary, DFENet demonstrates excellent performance in scenarios with significant background interference or high demands for positioning accuracy, indicating strong potential for various applications. Full article

Continuous-flow microfluidic biochips (CFMBs) automatically execute various bioassays by precisely controlling the transport of fluid samples, which is driven by pressure delivered through fluidic ports. High-level synthesis, as an important stage in the design flow of CFMBs, generates binding and scheduling solutions whose quality directly affects the efficiency of the execution of bioassays. Existing high-level synthesis methods perform numerous transport tasks concurrently to increase efficiency. However, fluidic ports cannot be shared between concurrently executing transport tasks, resulting in a large number of fluidic ports introduced by existing methods. Increasing the number of fluidic ports undermines the integration, reduces the reliability, and increases the manufacturing cost. In this paper, we propose a port-driven high-level synthesis method based on integer linear programming (ILP) called SlimPort, integrating the optimization of fluidic port number into high-level synthesis, which has never been considered in prior work. Meanwhile, to ensure bioassay correctness, volume management between devices with a non-fixed input/output ratio is realized. Additionally, two acceleration strategies for ILP, scheduling constraint reduction and upper boundary estimation of fluidic port number, are proposed to improve the efficiency of SlimPort. Experimental results from multiple benchmarks demonstrate that SlimPort leads to high assay execution efficiency and a low number of fluidic ports. Full article

Container intermodal scheduling is critical for advancing low-carbon logistics within inland port systems. However, the scheduling process faces several challenges, including the complexity of coordinating transport modes and complying with carbon emission policies. To address these issues, this study proposes a multi-objective optimization model that simultaneously considers transportation cost, carbon emissions, and time efficiency under soft time window constraints. The model is solved using an improved grey wolf–Harris hawks hybrid algorithm (IGWOHHO). This algorithm enhances population diversity through Tent chaotic mapping, balances global exploration and local exploitation with adaptive weight adjustment, and improves solution quality by incorporating an elite retention strategy. Benchmark tests show that IGWOHHO outperforms several well-established metaheuristic algorithms in terms of convergence accuracy and robustness. A case study based on an intermodal transport network further demonstrates that adjusting the objective weights flexibly provides decision support under various scenarios, achieving a dynamic balance between cost, efficiency, and environmental impact. Additionally, the analysis reveals that appropriate carbon tax pricing can encourage the adoption of greener transport modes, promoting the sustainable development of multimodal logistics systems. Full article

Small- and medium-sized ports (SMSPs) face multifaceted challenges, including environmental pressures (pollution, resource depletion) as well as socio-economic strains (community impacts, trade dynamics). Governance complexities, regulatory fragmentation, limited funding, and stakeholder conflicts further exacerbate these challenges. This study identifies context-driven sustainability indicators for SMSPs and proposes best practices to enhance their resilience. Four small/medium-sized ports (SMSPs) in Crete, Greece—Souda (CHA), Heraklion (HER), Rethymnon (RET), and Sitia (SIT)—were evaluated to benchmark their sustainability performance using tailored indicators. The primary objectives were the following: (a) to identify sustainability indicators relevant to SMSPs, focusing on economic, environmental, and social aspects, and (b) to propose best practices for enhancing sustainability in these ports, considering their unique characteristics and constraints. A combination of the Best Worst Method (BWM) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) analysis was employed. The findings revealed that “environmental monitoring and management” and “security of employment and social protection” are top priorities for medium-sized ports, while “competitiveness” was a crucial indicator for small ports, emphasizing the need for financial sustainability in the context of global trade. These results offer a strategic roadmap for policymakers, guiding interventions aimed at improving the sustainability and resilience of SMSPs, thereby supporting regional economies and international trade networks. Full article

This paper proposes a novel hybrid metaheuristic, called JADEGMO, that combines the adaptive parameter control of adaptive differential evolution with optional external archive (JADE) with the search strategies of geometric mean optimizer (GMO). The goal is to enhance both exploration and exploitation stratifies for solving complex optimization tasks. JADEGMO inherits JADE’s adaptive mutation and crossover strategies while leveraging GMO’s swarm-inspired velocity updates guided by elite solutions. The experimental evaluations on IEEE CEC2022 benchmark suites demonstrate that JADEGMO not only achieves superior average performance compared to multiple state-of-the-art methods but also exhibits low variance across repeated runs. Convergence curves, box plots, and rank analyses confirm that JADEGMO consistently finds high-quality solutions while maintaining diversity and avoiding premature convergence. To highlight its applicability, we employ JADEGMO in a real-world multi-cloud security configuration scenario. This problem models the trade-offs among baseline risk, encryption overhead, open ports, privilege levels, and subscription-based security features across three cloud platforms. JADEGMO outperforms other common metaheuristics in locating cost-efficient configurations that minimize risk while balancing overhead and subscription expenses. Full article

This study addresses the critical gap between academic training and the competency demands of the maritime logistics and port management sector. Using a mixed-methods approach, it integrates benchmarking of postgraduate programs from leading universities, interviews with 15 stakeholders representing diverse industry profiles, and an in-depth curriculum analysis. The research identifies and categorizes essential technical, management, and interpersonal competencies, culminating in the development of a Competency Matrix to guide the alignment of academic curricula with industry requirements. Key competencies identified include strategic decision-making, operations management, data analysis, adaptability, teamwork, and customer engagement, all of which are critical to ensuring efficiency and competitiveness in the sector. This study introduces an innovative framework by combining benchmarking with qualitative insights, addressing a crucial gap in the literature while offering actionable strategies for academia to enhance training programs. The findings highlight the urgent need for universities to develop courses tailored to global challenges, such as digitalization, sustainability, and supply chain resilience. Although this study is exploratory and based on a limited sample size, it provides meaningful insights into the Portuguese maritime and port logistics sector, laying a solid foundation for future research. Further studies should investigate how innovation and emerging technologies, such as artificial intelligence and blockchain, are reshaping competency requirements in this dynamic and globalized industry. Full article

This paper details the development of a collision avoidance algorithm for unmanned surface vehicles (USVs) and its validation using free-running tests. The USV, designed as a catamaran, incorporates a variety of sensors for its guidance, navigation, and control system. It performs turning maneuvers using thrusters positioned on the port and starboard sides. The robot operating system is used to streamline communication, transmitting data such as position, orientation, and situational information from diverse sensors. Using the collision risk index (CRI) method, the algorithm calculates risk based on the distance to obstacles and the angle to the desired waypoint, directing the USV on a path with minimized risk. Noise within the data captured by the two-dimensional light detection and ranging system is filtered out using the k-dimensional tree and Euclidean distance methods, ensuring single obstacles are distinctly identified. To assess the efficacy of the CRI-based collision avoidance algorithm, it was benchmarked against other algorithms rooted in the artificial potential field and safety zone methods within an artificial tank setting. The results highlight the CRI method’s superior time efficiency and optimality in comparison to its counterparts. Full article

Introduction: Proper bladder drainage is crucial. Children with bladder dysfunction may require alternative methods like clean intermittent catheterisation (CIC). However, CIC can be challenging for individuals with impairments. The Mitrofanoff procedure provides a solution by connecting the appendix to the bladder and creating a stoma on the skin, allowing for continent catheterisation. Minimally invasive techniques, including robotics, have been adopted recently. The aim of this study is to review the existing literature on robotic Mitrofanoff procedures. Materials and Methods: A systematic review on paediatric robotic Mitrofanoff procedures on the PubMed, Cochrane, and Scopus databases was conducted according to the PRISMA Statement. Critical appraisals of the included studies were performed with the Newcastle Ottawa Scale. Results: Six studies were included about the robotic Mitrofanoff procedure. Sex was reported in 50% of the studies. Ages were within the twelve-year age limit, as per the inclusion criteria. The mean operative time was 499.3 (±171.1) min. Four of the six studies reported a length of stay with a median of 6 days (±4; range 1.8–23). The incidence of complications was in line with established benchmarks. Only one study compared the Mitrofanoff procedure to open surgery, finding similar outcomes but longer operating times. Port placement and surgical strategy was described. Conclusions: Robotics can offer potential advantages for the Mitrofanoff procedure, despite its application still being in its early stages. This study emphasises the potential safety and efficacy of the robotic approach and promotes the need for further prospective high-quality studies. Full article

Currently, the operational risk assessment of quay crane operators at ports relies on manual evaluations based on experience, but this method lacks objectivity and fairness. As port throughput continues to grow, the port accident rate has also increased, making it crucial to scientifically evaluate the risk behaviors of operators and improve their safety awareness. This paper proposes an automated evaluation method based on a Deep Q-Network (DQN) to assess the risk behaviors of quay crane operators in virtual scenarios. A risk simulation module has been added to the existing automated quay crane remote operation simulation system to simulate potential risks during operations. Based on the collected data, a DQN-based benchmark model reflecting the operational behaviors and decision-making processes of skilled operators has been developed. This model enables a quantitative evaluation of operators’ behaviors, ensuring the objectivity and accuracy of the assessment process. The experimental results show that, compared with traditional manual scoring methods, the proposed method is more stable and objective, effectively reducing subjective biases and providing a reliable alternative to conventional manual evaluations. Additionally, this method enhances operators’ safety awareness and their ability to handle risks, helping them identify and avoid risks during actual operations, thereby ensuring both operational safety and efficiency. Full article