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    <title>Transport Research International Documentation (TRID)</title>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Novel models and efficient heuristic for the vessel-unmanned surface vehicle routing problem</title>
      <link>https://trid.trb.org/View/2652351</link>
      <description><![CDATA[This paper addresses the vessel–unmanned surface vehicle (USV) routing problem (VURP), which jointly determines the routes of a vessel and a USV. The problem is applicable to offshore supply, search and rescue, and inspection operations. In the VURP, a vessel carries a loaded USV and the USV departs from the vessel to deliver goods to offshore platforms when in proximity. The objective is to minimize the total routing costs of both vehicles by optimizing their routes along with the USV’s departure and arrival points. We begin by exploiting the structural properties of the problem and propose two enhanced mixed-integer second-order conic programming (MISOCP) formulations, each further strengthened with valid inequalities to improve computational performance. Due to the NP-hard nature of the problem, we develop a tailored adaptive large neighborhood search (ALNS) algorithm designed to handle practical-sized instances. The proposed ALNS employs a two-phase framework to enable a multi-start mechanism: the first phase generates a diverse set of initial solutions using an effective approximation-based scheme, while the second phase iteratively improves these solutions through a problem-specific ALNS procedure. This structure balances global exploration and local intensification, enhancing both solution diversity and robustness. Extensive computational experiments demonstrate the strong performance of the improved formulations and the heuristic method. The integration of USVs significantly reduces vessel travel costs, especially when the USVs have larger capacities. Moreover, numerical results on benchmark instances show that our ALNS outperforms state-of-the-art heuristics, achieving 45 new best solutions among 72 open benchmark cases.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:20:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652351</guid>
    </item>
    <item>
      <title>A benders-branch-and-cut methodology for global cargo vessel traffic prediction given declining arctic sea ice and changing risks</title>
      <link>https://trid.trb.org/View/2599205</link>
      <description><![CDATA[Global warming has led to declining sea-ice in the Arctic Ocean, making it easier for ice-class vessels to navigate Arctic waters for greater portions of the year. As sailing conditions in these waters improve over coming decades, these passageways are expected to open for larger portions of the year and to become increasingly viable options for unsupported transit and even open-water vessels. This paper proposes a Benders-branch-and-cut methodology for estimating changes in global maritime cargo flow patterns under future climate scenarios with declining Arctic sea ice. The model accounts for changing incident risk along Arctic passageways and corresponding ice-class vessel and icebreaker escort requirements, lower speeds, increased insurance premiums, higher accident probabilities, and constraints on path-based maximum risk exposure. The resulting mixed-integer program involves path-based, continuous decision variables. The solution technique is applied on a model of the global maritime container network including 80 ports, 76 routes, 426 links and 4,303 legs associated with the world’s largest carrier alliance. Embedded acceleration techniques and a label-correcting algorithm that employs specialized fathoming rules for a non-additive, constrained path subproblem enable solution at this global scale. The outcome is an estimate of seasonal future global maritime trade flows along key global routes and through ports predicted under six climate-related scenarios. Results illustrate that the developed model can provide support to companies, nations and regions as they prepare for a changing global landscape and climate.]]></description>
      <pubDate>Thu, 30 Oct 2025 08:49:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2599205</guid>
    </item>
    <item>
      <title>Manoeuvring Prediction for Safe and Efficient Ship Handling in Training &amp; Ship Operation â€“ Status Quo and Outlook</title>
      <link>https://trid.trb.org/View/2572897</link>
      <description><![CDATA[Prediction methods and forecast of future developments and trends as well as related decisions or actions and reactions have been playing a vital role in human live and evolution. Human intelligence allowed for a dominating role in the development of our planet using our brains and more and more computers and merging artificial intelligence in the future. In the maritime domain, among others, the forecast of ships motion has been developed: from simple straight forward voyage planning in paper charts based only on rough measurement of estimated positions and ship motions, up to the latest developments using electronic navigation, ECDIS, communication technologies and high sophisticated Fast Time Simulation (FTS) methods. In the paper the already known technologies for supporting the ship handling process will be compared with potential new methods from single prediction up to multiple prediction and even step ahead prediction with unrivalled extension of the decision horizon. It reflects new requirements for preplanning of manoeuvres as part of berth-to-berth voyage planning specifically for arrival and departure segments - and increasing demands for safety and efficiency for the execution of manoeuvres in the conning process. Computer-based systems have been developed for â€sSimulation Augmented Manoeuvring Design, Monitoring & Conningâ€ and are used to analyse and demonstrate the different prediction methods. Complex models of ship manoeuvring dynamics are implemented in order to forecast the immediate response on commanded control settings or even external effects as wind and shallow water for a suitable time period of the future motion. The paper provides insights into the potential benefits of various prediction methods based on FTS, discussed both for long term preplanning and short-term prediction for real-time support during the manoeuvring process. The benefits for increasing the effectiveness of lecturing and simulator training using these methods are obvious specifically for complex manoeuvring systems and will be demonstrated in this paper. It is a break-through in lecturing and training for immediate knowledge generation and checking ideas, it works perfectly for self-study individual learning and is excellent for the preparation of trainees for Full Mission simulator training, covering not only aspects of safe but also energy efficient and emission-minimized manoeuvres. Finally, potential future applications for wider navigation areas will be addressed.]]></description>
      <pubDate>Fri, 18 Jul 2025 09:06:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2572897</guid>
    </item>
    <item>
      <title>Ensuring safety and reliability in offshore operations: A fuzzy DEMATEL study on dynamic positioning system hazards</title>
      <link>https://trid.trb.org/View/2568112</link>
      <description><![CDATA[Dynamic positioning (DP) systems are vital for offshore operations such as oil and gas extraction, wind energy installations, and submarine cable laying, ensuring vessels maintain position despite environmental forces like wind, waves, and currents. These systems integrate GPS, wind sensors, and gyrocompasses to control thrusters, allowing precise positioning. However, failures and operational hazards in DP systems can compromise safety and efficiency. This study uses the Fuzzy DEMATEL method to identify and analyze critical hazards in DP systems, offering valuable insights for enhancing safety in offshore operations. DP systems consist of a power management system, thrusters, sensors, computers, and an operator console. The power management system ensures energy reliability, thrusters adjust vessel position, and computers process data for continuous operation. Operator consoles provide human-machine interaction for real-time interventions. This complex interdependence underscores the critical nature of system performance. The study employs expert surveys to analyze cause-effect relationships among hazards using the Fuzzy DEMATEL approach. Key factors evaluated include human error, environmental conditions, power failures, cargo shifting, collisions with support vessels, and impacts on vessel stanchions. Results indicate human factors exert the most critical influence (ri + ci: 6.14, ri: 2.88), emphasizing their role in system reliability. Environmental factors (ri + ci: 5.98) and power failures (ri + ci: 5.92) also significantly impact DP operations. Cargo shifting (ri−ci: 0.33) poses stability hazards, particularly under adverse weather. Collisions with support vessels (ri−ci: 0.29) and stanchion impacts (ri−ci: 0.24) highlight physical risks requiring careful management. To mitigate these hazards, the study recommends regular maintenance of power and electrical equipment, enhanced operator training, improved communication between crane operators and deck personnel, and the development of resilient technologies against harsh environmental conditions. This research contributes to advancing safety standards and minimizing hazards in offshore operations.]]></description>
      <pubDate>Fri, 18 Jul 2025 09:05:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2568112</guid>
    </item>
    <item>
      <title>Syncrolift dry dock scheduling with a capacitated ship transfer system</title>
      <link>https://trid.trb.org/View/2554442</link>
      <description><![CDATA[Despite the rapidly increasing maintenance demand driven by an aging fleet and the growth of global fleet size, optimizing dry dock operations remains an underexplored area in academic research. This study addresses this gap by focusing on enhancing efficiency through improved dry dock scheduling, aiming to alleviate the growing disparity between maintenance needs and limited dry dock resources. The authors propose a practical syncrolift dry dock scheduling problem that incorporates a capacitated ship transfer system, which requires determining the optimal sequence of ship maintenance services to minimize total waiting time. A mixed-integer linear programming (MILP) model is developed to effectively tackle the challenges of this problem, particularly the ship transfer system—a critical bottleneck involving the syncrolift and railways—by modeling it as a series of segments with varying capacities and transfer restrictions. The model accommodates the complexities of the scheduling process, such as limited transfer capacity, diverse segment characteristics, bidirectional ship transfer flows, ship compatibility constraints, and sequence-dependent setup times. Recognizing the NP-hard nature of the problem, the authors introduce a novel column-generation-based heuristic method to solve the MILP model efficiently. The efficacy of the proposed solution method is validated through extensive numerical experiments using real operational data from the NOSCO shipyard. Results demonstrate significant improvements in operational efficiency, with ship waiting times reduced by 32.5% compared to first-come-first-served scheduling solutions.]]></description>
      <pubDate>Fri, 20 Jun 2025 11:58:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2554442</guid>
    </item>
    <item>
      <title>Optimization of ship-deployed AUVs synergistic scheduling for offshore wind turbines underwater foundations inspection</title>
      <link>https://trid.trb.org/View/2530158</link>
      <description><![CDATA[Guided by the IMO’s GHG reduction strategy and the “dual-carbon” goal, offshore wind power has become vital in renewable energy, and more attention has been paid to the regular inspection of offshore wind turbines (OWTs). The Autonomous Underwater Vehicle (AUV) has significantly improved inspection, but the current technology limits it to independently perform long-distance and complex tasks. We propose a ship-deployed AUVs synergistic mode to cover larger area inspections in a shorter period. A mixed-integer programming model is developed to optimize the ships’ routes and schedule AUVs’ drop and pick-up time. An adaptive large neighborhood search heuristic based on constraint programming (ALNSCP) is developed for large-scale instances. The simulation instances-based computational experiments verify the superiority of the synergistic mode and solution method in improving inspection efficiency. Sensitivity analysis further reveals how AUV debugging time and allowed float time affect inspection efficiency and cost. The analysis of variants with limited deployable AUVs and soft time windows enhances the applicability of the proposed solution. This study can realize the efficiency of AUV utilization and provide decision support for OWTs underwater foundations inspection]]></description>
      <pubDate>Mon, 12 May 2025 09:46:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2530158</guid>
    </item>
    <item>
      <title>Ship emission monitoring with a joint mode of motherships and unmanned aerial vehicles</title>
      <link>https://trid.trb.org/View/2519344</link>
      <description><![CDATA[Ship emission monitoring is crucial for improving compliance with emission control area (ECA) policies. To address the limitations of traditional base station-based monitoring methods, the authors propose a highly maneuverable mothership-based unmanned aerial vehicle (UAV) monitoring mode. The authors develop a mixed integer non-linear programming model to maximize the total profit (i.e., the revenues of ship emission monitoring minus the fixed costs of motherships and UAVs, the fuel cost of motherships, and the electricity cost of UAVs). Three types of integer variables are relaxed to continuous variables based on the model properties. The authors then design a tailored Benders decomposition algorithm to solve the model. Moreover, to improve the performance of the algorithm, the authors also present a variety of acceleration strategies, including lower bound limit inequalities and knapsack inequalities. Finally, the authors verify the effectiveness of the proposed algorithm using experimental instances based on the North American ECA. The authors also find a relationship between the width of emission inspection area and the total monitoring cost.]]></description>
      <pubDate>Fri, 02 May 2025 08:49:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2519344</guid>
    </item>
    <item>
      <title>Cruise onboard itinerary planning for multi passengers with service venue capacity and time-window constraints</title>
      <link>https://trid.trb.org/View/2480602</link>
      <description><![CDATA[The design of personalized onboard itineraries for multiple cruise passengers plays an important role in improving the tourism experience on cruise. Different from itinerary planning for city traveling, the venues on cruise suffer from strict capacity and time-window constraints due to the safety requirements, which results in the coupling effects between itineraries of multiple passengers. This study constructs an optimization model to balancing the gap between passengers while maximizing passenger benefits, develops an Adaptive Large Neighborhood Search algorithm based on Improved destruction-repair operators (IOALNS) to solve multi-passenger itinerary planning. Extensive analyses are performed to demonstrate the performance advantages of the IOALNS algorithm in solving instances with varying sizes. Finally, the test results of cruise passenger instances show that compared with existing multi-passenger travel planning algorithms, the proposed algorithm can improve passenger efficiency by at least 9.98% and reduce computation time by more than 50%. These effectively improve passenger satisfaction and operational efficiency in the cruise industry.]]></description>
      <pubDate>Tue, 18 Feb 2025 10:56:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2480602</guid>
    </item>
    <item>
      <title>A GRASP-based multi-objective approach for the tuna purse seine fishing fleet routing problem</title>
      <link>https://trid.trb.org/View/2453896</link>
      <description><![CDATA[Nowadays, the world’s fishing fleet uses 20% more fuel to catch the same amount of fish compared to 30 years ago. Addressing this negative environmental and economic performance is crucial due to stricter emission regulations, rising fuel costs, and predicted declines in fish biomass and body sizes due to climate change. Investment in more efficient engines, larger ships and better fuel has been the main response, but this is only feasible in the long term at high infrastructure cost. An alternative is to optimize operations such as the routing of a fleet, which is an extremely complex problem due to its dynamic (time-dependent) moving target characteristics. To date, no other scientific work has approached this problem in its full complexity, i.e., as a dynamic vehicle routing problem with multiple time windows and moving targets. In this paper, two bi-objective mixed linear integer programming (MIP) models are presented, one for the static variant and another for the time-dependent variant. The bi-objective approaches allow to trade off the economic (e.g., probability of high catches) and environmental (e.g., fuel consumption) objectives. To overcome the limitations of exact solutions of the MIP models, a greedy randomized adaptive search procedure for the multi-objective problem (MO-GRASP) is proposed. The computational experiments demonstrate the good performance of the MO-GRASP algorithm with clearly different results when the importance of each objective is varied. In addition, computational experiments conducted on historical data prove the feasibility of applying the MO-GRASP algorithm in a real context and explore the benefits of joint planning (collaborative approach) compared to a non-collaborative strategy. Collaborative approaches enable the definition of better routes that may select slightly worse fishing and planting areas (2.9%), but in exchange for a significant reduction in fuel consumption (17.3%) and time at sea (10.1%) compared to non-collaborative strategies. The final experiment examines the importance of the collaborative approach when the number of available drifting fishing aggregation devices (dFADs) per vessel is reduced.]]></description>
      <pubDate>Thu, 19 Dec 2024 11:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2453896</guid>
    </item>
    <item>
      <title>A wall climbing robot based on machine vision for automatic welding seam inspection</title>
      <link>https://trid.trb.org/View/2411168</link>
      <description><![CDATA[With the ongoing progress of industrial technology such as shipbuilding, the importance of weld quality in industrial production is becoming increasingly prominent. Intelligent and automated welding seam inspection robots are more efficient than traditional manual inspection and can avoid dangerous accidents. This article describes the design of a welding seam inspection robot suitable for high-altitude ship operation. The robot uses machine vision and object segmentation models to automatically detect the position of welding seams, and uses a cubic polynomial to fit the welding seam path. The upper and lower computers of the robot communicate through WIFI transmission and TCP protocol, which can realize remote real-time detection of weld surface defects. In addition, this article designs a permanent magnet adsorption structure for robot high-altitude wall climbing, which has been verified through simulation and experimental verification. To verify the intelligence of the robot, this paper conducted performance analysis experiments on weld line recognition and tracking models and surface defect models. The experimental results showed that the average detection accuracy of the weld line recognition and tracking algorithm was 96.8%, and the average detection accuracy of surface defects in the three types of welds was 94.2%. The method proposed in this article combines two algorithms and a special robot structure, providing a new approach for the automated inspection of welds in large industrial products such as ships.]]></description>
      <pubDate>Wed, 07 Aug 2024 11:16:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2411168</guid>
    </item>
    <item>
      <title>Research on Collision-Avoidance System of Ship with AIS Based on APT</title>
      <link>https://trid.trb.org/View/2282031</link>
      <description><![CDATA[With the development of computers and technical correspondence, ship navigation is gradually digitizing. A mathematical model applied in the dynamic environment is presented, which is based on the artificial potential field (APT) method. The intelligence system of ship automation collision-avoidance, for multi-vessel coordination using behavior based on multi-objective decision, has been established. The information of the target ships in this system, which has been obtained from AIS system is employed in an analytic function. And it has been successfully used for mobile ship collision-avoidance path planning in the unknown complex environment. Simulation results demonstrate that the proposed method performs path planning very well and has good practicality.]]></description>
      <pubDate>Fri, 26 Apr 2024 14:15:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2282031</guid>
    </item>
    <item>
      <title>Classification and regression in prescriptive analytics: Development of hybrid models and an example of ship inspection by port state control</title>
      <link>https://trid.trb.org/View/2313244</link>
      <description><![CDATA[In prescriptive analytics, unknown quantities are involved in practical decision-making problems, and these unknown quantities need to be predicted using auxiliary data. A classic approach is to develop machine learning (ML) models to generate point estimates, which are then input to the decision making problem in a deterministic manner to prescribe the optimal decision. However, the limited quantity and inevitable errors in the auxiliary data lead to inaccurate predictions and thus sub-optimal decisions. One viable approach to addressing the above issue is to consider the uncertainties in data by inputting the conditional distributions of the unknown quantities on the auxiliary data to the optimization problem on hand, and the distributions are predicted by regression ML models. Meanwhile, it is observed that the quantitative target in some problems are discrete, and these properties are analogous to categorical targets in classification problems. Considering the fact that describing and estimating the distribution of categorical variables are much easier than quantitative variables, this study innovatively develops random forest (RF) models with regression and classification features to generate the distribution of quantitative targets that are discrete. Especially, nodes splitting criteria in the RF models is in a regression manner, while the outputs of individual decision trees and the whole RF model is in a classification manner. Numerical experiments using real port state control (PSC) inspection records and settings at the Hong Kong port are conducted to validate and compare the above prescriptive analytics approaches. The superiority of applying the newly proposed RF model into the development of prescriptive analytics approaches is also demonstrated.]]></description>
      <pubDate>Fri, 19 Apr 2024 09:38:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2313244</guid>
    </item>
    <item>
      <title>Real-time simulation of ship structure based on virtual-real fusion interaction</title>
      <link>https://trid.trb.org/View/2330344</link>
      <description><![CDATA[Virtual-real Fusion is a technology that enables rapid integration of physical information and decision-making through visualization and interaction. By combining with the field of ship structure design, this technology is applied to improve the competitiveness of enterprises. In this paper, a method of virtual-real interaction and symbiosis between engineering analysis and physical scenarios is proposed, aiming to enhance the design and evaluation of hull structures. Initially, the establishment of a high-fidelity digital twin model is ensured to be consistent with the physical scene, paired with a collision detection mechanism consistent with the physical scene, which plays a key role in the correlation of loads and boundary conditions for subsequent real-time numerical simulations. Secondly, measured data from physical sensors are used to drive virtual scenes to ensure that computers understand and reproduce specific physical phenomena. Subsequently, the real-time numerical simulation data is filtered and transformed. At the same time, engineers can explore finite element analysis results on-site through virtual reality visualization technology to track the mechanical interactions between physical components, thereby reducing spatial and logical misunderstandings and enabling collaboration with other departments. Finally, a prototype system with basic functions is developed. A case study focusing on mechanical experiment of ship structures is carried out, which demonstrates the practicability of the proposed method.]]></description>
      <pubDate>Thu, 15 Feb 2024 17:04:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2330344</guid>
    </item>
    <item>
      <title>Cyber Risk Assessment for SHips (CRASH)</title>
      <link>https://trid.trb.org/View/2310036</link>
      <description><![CDATA[The maritime industry is undergoing a digital transformation, with an increasing integration of Information Technology (IT) and Operational Technology (OT) systems on modern vessels. Its multiple benefits notwithstanding, this transformation brings with it increased cybersecurity risks, that need to be identified, assessed, and managed. Although several cyber risk assessment methodologies are available in the literature, they may be challenging for experts with a maritime background to use. In this paper the authors propose a simple and effective cyber risk assessment methodology, named Cyber Risk Assessment for SHips (CRASH), that can be easily implemented by maritime professionals. To showcase its workings, they assessed 24 cyber risks of the Integrated Navigation System (INS) using CRASH and they validated the method by comparing its results to those of another method and by means of interviews with experts in the maritime sector. CRASH can aid shipping companies in effectively assessing cyber risks as a step towards selecting and implementing necessary measures to enhance the cyber security of cyber-physical systems onboard their vessels.]]></description>
      <pubDate>Fri, 22 Dec 2023 08:46:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2310036</guid>
    </item>
    <item>
      <title>An improved genetic algorithm for the berth scheduling with ship-to-ship transshipment operations integrated model</title>
      <link>https://trid.trb.org/View/2244964</link>
      <description><![CDATA[In this paper, the authors consider a dynamic variant of Berth Allocation Problem with ship-to-ship transshipment in a container terminal, namely (DBAP), where ships can arrive after the start of the planning plan. A novel mixed integer linear program (MILP) is developed to optimize the berthing schedule and build a transshipment connections planning between feeder and mother vessels. The aim is to reduce vessels dwell times in the terminal, and the penalty due to late vessels and decide the mode of transshipment needed. First, the authors develop a packing heuristic, and later they use an improved genetic algorithm based heuristic (GA) to solve the problem efficiently. The authors conducted a statistical analysis to identify relative importance and effective settings for parameters control of the GA, and applied this algorithm with the control parameters settings determined to carry out the computational experiments on random generated instances. The proposed tailored method is able to solve the problem in an acceptable computing time for medium and large instances while a commercial solver was able to solve only small size instances.]]></description>
      <pubDate>Wed, 01 Nov 2023 09:34:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2244964</guid>
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