Containerized Research Articles

An exploration of shipbuilding price prediction for container ships: An integrated model application of deep learning

Shipbuilding price forecasts are key to the maritime industry's foresight, cost management, and competitive edge. This study fills a gap in the existing theoretical and empirical literature on shipbuilding price forecasting by collecting and analyzing weekly price data from October 4, 1996 to September 30, 2022, covering 17,641 observations. The study employs a CNN-BILSTM-AM model, which combines a CNN, BILSTM, AM, for shipbuilding price prediction. The findings suggest that this ensemble model effectively captures the non-linear and time-varying characteristics of shipbuilding price fluctuations. It demonstrates good adaptability to random sample selection, data frequency, and structural disruptions in the sample. This model boasts an impressive predictive accuracy, with an R 2 value of 94.3 %, surpassing many standalone, composite, and traditional forecasting approaches. This study proposes a CNN-BILSTM-AM integrated model that significantly improves the shipbuilding price prediction accuracy and extends the application of machine learning in shipping economics. This study furnishes decision-support and risk management tools, utilizing historical big data to forecast shipbuilding prices, tailored for governments, financial institutions, the shipbuilding industry, and the global shipping industry.

Limitations of and proposals for L/C application of optional on-board B/L under the IncotermsⓇ2020 FCA rules

PurposeThis study examines the limitations of the practical application of letter of credit transactions requiring an on-board bill of lading under the FCA and presents opinions on the practical use of FCA-ruled bills of lading for letter of credit transactions.Design/methodology/approachIt is very significant that the IncotermsⓇ2020 FCA rules have added new content regarding the optional description of the on-board bill of lading. However, even if the seller has received the on-board bill of lading, there is a limitation in that the bank may not judge the presented documents as a consistent presentation for payment. Identifying these limitations and suggesting solutions are very important for future international commerce transactions. This study is conducted in the following order. First, we review the UCP regulations that banks apply to confirm the consistency of transportation documents in letter of credit transactions. Second, we confirm the limitations in applying the selective description of the on-board bill of lading newly established in the FCA rules proposed by the ICC to letter of credit transactions. Lastly, necessary opinions are presented on how the buying and selling parties can properly utilize the Incoterms in letter of credit transactions.FindingsThe establishment of the ICC’s Incoterms 2020 FCA rule’s on-board bill of lading option regulation is intended to resolve merchants’ inconvenience caused by the document screening standards of banks that ignore changes in the trade environment such as the emergence of containers and maintain traditional ship trading practices. It can be interpreted. However, there are still problems with the presentation of on-board bills of lading under FCA rules, and there are also problems with practices between merchants and banks. Existing prior research is limited to dealing with the problems of using FOB rules in container transportation and the limitations of the onboard bill of lading option provisions of FCA rules. The Fourth Industrial Revolution has also affected trade, ushering in the digital era. Trade platforms developed with new technologies are either under development or being prepared for commercialization.Originality/valueThis study reaffirmed the main implications covered in previous studies and proposed the use of multimodal transport documents rather than bills of lading as an alternative to problems that may arise in letter of credit transactions that require on-board bills of lading under FCA rules. Above all, it presented opinions on changes in banks’ practices that require on-board bills of lading in letter of credit transactions, which are inconsistent with the mainstream container-based intermodal transportation.

Optimization design of a windshield for a 12,000 TEU container ship based on a support vector regression surrogate model

This study presents the design of a windshield for a 12,000 TEU container ship using a multidisciplinary optimization system aimed at enhancing its aerodynamic performance. The framework integrates parametric modeling, design of experiment (DOE), numerical simulation, support vector regression (SVR)-based approximation, and a genetic algorithm (GA). Initially, the flow field around the ship is predicted using Reynolds-averaged Navier–Stokes (RANS) equations, with accuracy validated against computational fluid dynamics (CFD) and experimental fluid dynamics (EFD) results. The windshield’s shape is then defined by three Bezier curves, and six design variables are selected based on these curves to ensure smooth deformation. Subsequently, 120 sample points are chosen within the design space using the DOE method, and an SVR-based surrogate model is established. Optimization is performed using the multi-island genetic algorithm to determine the windshield configuration. Validation tests confirm the robustness of the optimization process. On this basis, the drag reduction mechanism of the windshield is summarized and analyzed. Results demonstrate that the optimization system effectively enhances the aerodynamic performance of the container ship, achieving a maximum drag reduction rate of 20.67% under headwind conditions and significant improvement across wind angles from 0° to 60°.

Train assignment and handling capacity arrangement in multi-yard railway container terminals: An enhanced adaptive large neighborhood search heuristic approach

Expanding terminal scale and constructing multiple railway handling yards have become popular strategies for leading railway container terminals globally to cope with the ever-increasing handling volume. Although such an approach greatly enhances the terminal’s productivity, it also intensifies handling operations and introduces additional inter-yard interactions, complicating terminal management. To address these challenges, this paper investigates an integrated optimization approach for multi-yard railway container terminals, which feature both rail-road and rail-rail container transshipment operations. The train assignment plan for each yard and the handling capacity arrangement for each train are jointly optimized while considering inter-yard container transits, workload allocation for yards, and safety requirements in train shunting. This problem is formulated as a nonlinear programming model, with the objective to minimize operational delay and service time for each incoming train, and to minimize workload differences and container transit volume among yards. To efficiently solve this problem, this research develops an enhanced adaptive large neighborhood search (EALNS) heuristic, which includes several customized operators and feasibility repair methods, and is further enhanced with a local search method and backtracking mechanism compared to the standard ALNS framework. Computational experiments with different data scales and problem settings demonstrate the superiority of the EALNS in terms of solution quality and stability compared with three other solution methods. Additionally, practical insights for terminal operations are drawn through detailed analysis of different infrastructure configurations, transshipment train characteristics, and unit cost settings.

A modified adaptive large neighborhood search algorithm for solving the multi-port continuous berth allocation problem with vessel speed optimization

Despite its fast-growing popularity in maritime transportation, container shipping is still fraught with risks and uncertainties with its complex operating environments. This paper studies the multi-port continuous berth allocation problem with speed optimization (MCBAP). In the MCBAP, vessels visit multiple ports sequentially, and the problem aims at minimizing the sum of vessel sailing cost, waiting cost, delay cost and port handling cost, while satisfying various constraints related to vessel sailing and berthing. A mixed integer linear programming (MILP) model for MCBAP is formulated, and a modified adaptive large neighborhood search (MALNS) algorithm is proposed for solving large-scale MCBAPs. In the MALNS, an efficient initial solution generation strategy is developed, and a series of neighborhood solution generation operators are proposed. Finally, the proposed MILP model and MALNS algorithm are tested on a range of MCBAP instances. The numerical results demonstrate that the MILP model can be solved to optimality with CPLEX, and the MALNS can efficiently solve instances at various scales. In addition, sensitivity analyses on fuel prices and vessel design speeds (the planned maximum speeds) are performed, and management insights have been provided.

Strategic approaches to crisis management: The global challenges of the pandemic on the Turkish container shipping industry

The Covid-19 pandemic has posed significant challenges to the sector, and Türkiye’s strategic location plays a crucial role in crisis management. This study aims to explore the repercussions of the pandemic on the sector and propose appropriate solutions. To identify the most effective solutions for prioritizing the challenges posed by Covid-19, 53 sector experts participated in the study. The Stepwise Weight Assessment Ratio Analysis method was used to weigh the criteria representing these challenges. The solutions were ranked using the Weighted Aggregated Sum Product Assessment and Combined Compromise Solution methods. The results indicate that the primary challenge is the increased shipping cost, followed by the prolongation of the transport time, global-scale impacts, and port congestion. The most appropriate solution identified was alternative vessel loading, followed by mixed-mode transportation usage, reservation guarantees, long-term contracts with shipping operators, integrated logistics services, container purchases, and technological integration

Optimizing Maritime Energy Efficiency: A Machine Learning Approach Using Deep Reinforcement Learning for EEXI and CII Compliance

The International Maritime Organization (IMO) has set stringent regulations to reduce the carbon footprint of maritime transport, using metrics such as the Energy Efficiency Existing Ship Index (EEXI) and Carbon Intensity Indicator (CII) to track progress. This study introduces a novel approach using deep reinforcement learning (DRL) to optimize energy efficiency across five types of vessels: cruise ships, car carriers, oil tankers, bulk carriers, and container ships, under six different operational scenarios, such as varying cargo loads and weather conditions. Traditional fuels, like marine gas oil (MGO) and intermediate fuel oil (IFO), challenge compliance with these standards unless engine power restrictions are applied. This approach combines DRL with alternative fuels—bio-LNG and hydrogen—to address these challenges. The DRL algorithm, which dynamically adjusts engine parameters, demonstrated substantial improvements in optimizing fuel consumption and performance. Results revealed that while using DRL, fuel efficiency increased by up to 10%, while EEXI values decreased by 8% to 15%, and CII ratings improved by 10% to 30% across different scenarios. Specifically, under heavy cargo loads, the DRL-optimized system achieved a fuel efficiency of 7.2 nmi/ton compared to 6.5 nmi/ton with traditional methods and reduced the EEXI value from 4.2 to 3.86. Additionally, the DRL approach consistently outperformed traditional optimization methods, demonstrating superior efficiency and lower emissions across all tested scenarios. This study highlights the potential of DRL in advancing maritime energy efficiency and suggests that further research could explore DRL applications to other vessel types and alternative fuels, integrating additional machine learning techniques to enhance optimization.

The Role of Container Security Initiative (CSI) in Improving Supply Chain Security in Indonesia

The Container Security Initiative (CSI) is a pivotal program in enhancing global supply chain security, particularly in Indonesia. Introduced as a response to terrorist threats post-September 11, 2001, CSI aims to secure international container shipments through pre-screening at ports of origin. This article analyzes CSI implementation in Indonesia, its impact on supply chain security, and its role in bolstering maritime security. CSI has proven effective in mitigating smuggling risks and improving logistical efficiency. The program also fosters international cooperation in addressing transnational threats. Despite challenges such as increased operational costs, CSI contributes significantly to Indonesia's national security and economic stability. Effective CSI implementation has the potential to strengthen Indonesia's position in global trade and enhance resilience against maritime security threats.

Analysis of Key Factors and Correlations Influencing the Adoption of Autonomous Ships by Shipping Companies—A Study Integrating Revised DEMATEL-AHP with BOCR

In response to achieving the United Nations Sustainable Development Goals (SDGs) of Climate Action (#13) and Life Below Water (#14), the promotion of autonomous shipping technologies has advanced from the experimental stage to specific regional implementation, presenting the maritime industry with rapid and significant changes and challenges. In the future era, where autonomous vessels dominate shipping, with automated operation systems taking the lead, how successfully shipping companies harness these new maritime transport modes will critically impact the safety, efficiency, and reliability of future vessel operations. With the emergence and development of autonomous vessels, it is crucial to effectively assess the importance and correlation of key factors influencing shipping companies’ adoption of autonomous ships. This study utilizes the Analytic Hierarchy Process (AHP) and Revised Decision Making and Trial Evaluation Laboratory (RDEMATEL) to survey senior managers in container and bulk shipping from Taiwan, China, Japan, and the European Union. Through a literature review on the benefits, opportunities, costs, and risks brought by autonomous shipping, this study aims to understand the critical factors important to shipping companies in adopting autonomous shipping, as well as the correlation between these influencing factors across different shipping sectors. The research findings indicate that “emergency response capability” is a critical factor influencing overall and bulk shipping in the adoption of autonomous vessels, while “incomplete regulations” are the primary factor influencing container shipping in the adoption of autonomous ships. Regarding the correlation of critical influencing factors, “vessel technology development” is the main influencing factor for overall, container, and bulk shipping; “operational performance enhancement” is the primary affected factor for overall and container shipping; and “enhancing personnel and vessel safety” is the main affected factor for bulk shipping. It is hoped that the results of this study can serve as a guide for shipping companies in understanding the benefits and opportunities to be emphasized when adopting autonomous shipping and assist in developing effective strategies to reduce costs and risks.

Hybrid GA-ACO algorithm for optimizing transportation path of port container cargo

Hybrid GA-ACO algorithm for optimizing transportation path of port container cargo

Navigation risk assessment for ocean-going ships in the north pacific ocean based on an improved dynamic Bayesian network model

As a central driver of global trade, the safety of navigation on ocean-going voyages holds paramount importance. This study improved dynamic Bayesian network model for comprehensive ocean-going ship risk assessment. Firstly, addressing the risk posed by harsh natural environments in ship navigation, information nodes from the three dimensions were selected, and a dynamic Bayesian network was incorporated to capture the dynamic information. Second, for the uncertainty problem of unstructured data, the dynamic Bayesian network was improved to effectively integrate multi-source information through the hesitant cloud model. Subsequently, a multi-level risk assessment framework was constructed to achieve a refined assessment for the risk changes under different human behaviors, ship vulnerability and navigational environment conditions. An empirical study of container ship accidents in the North Pacific Ocean verifies that the experimental model effectively captures dynamic information, enabling a more accurate determination of depression locations and consequently achieving a more precise navigation risk assessment. Furthermore, the model is capable of capturing seasonal variations in marine environmental risks within the study area. Consequently, the improved model furnishes a scientific foundation for devising targeted risk response strategies.

Structural Analysis of the Geometric Alternatives of Double-Bottom Floor Plates of a Panamax-Class Container Ship

To reduce weight and simplify maintenance, ship structures frequently include openings and cutouts. While these features offer practical advantages, they can weaken the structural integrity of key components. This study explores the effects of these geometric discontinuities on the double-bottom floor plates of a Panamax-class container ship under axial and transverse loads. Through numerical simulations and experimental testing, we analyzed different cutout configurations and stiffening strategies to assess their impact on stress distribution, plate thickness, and fatigue behavior. The results reveal that side cutouts significantly increase stress, particularly under transverse loads, while central openings have less impact. Additionally, increasing plate thickness consistently reduces stress levels across all models, improving structural durability. Fatigue analysis shows that certain stiffening configurations, particularly those with longitudinal stiffeners in the bottom plates, enhance fatigue life. These findings offer critical design insights for optimizing hull structures, balancing weight reduction with strength improvement. This study provides valuable recommendations for reducing stress concentrations and extending the fatigue life of ship components, contributing to more efficient and safer ship designs.

Experimental Investigation of the hydrodynamic and acoustic performance of a Gate Rudder® system over a conventional rudder

The Gate Rudder® is a new concept of energy saving device and manoeuvring system, consisting of two asymmetric and independent blades at each side of a propeller which combines the advantage to deliver additional thrust, similar to an accelerating ducted propulsor, and to enhance ship manoeuvrability and seakeeping ability due to the individual control of each rudder blades.The present study reports the results of a comprehensive experimental survey in which the hydrodynamic and hydro-acoustic performances of a Gate Rudder® system (GRS) were investigated with respect to a conventional rudder (CRS), for a reference container vessel. The survey, that was conducted in the framework of the EU H2020 Research Project “GATERS”, included detailed flow measurements by 2D-PIV and Stereo-PIV, cavitation tests and acoustic measurements on a scaled model of a target container ship. The tests matrix covered different rudder/rudder-blade angles, speeds and displacements. The study highlights the beneficial effects provided by the installation of a Gate Rudder® over a conventional rudder in terms of sound mitigation and reveals the fundamental underlying hydrodynamic mechanisms of the propeller wake interaction with the Gate Rudder blades under different operative conditions.The results provide useful insights on how to improve the hydrodynamic and acoustic performance of a GRS.

Promoting or Inhibiting? Influence of Railway Container Transportation on Regional Economic Development

Promoting or Inhibiting? Influence of Railway Container Transportation on Regional Economic Development

Classification of Ship Type from Combination of HMM–DNN–CNN Models Based on Ship Trajectory Features

This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. For model optimization, both ship datasets were transformed into various formats corresponding to multiple models, incorporating data enhancement and augmentation approaches. Speed over ground, course over ground, rate of turn, rate of turn in speed, berth distance, latitude/longitude, and heading were used as input parameters. The HMM–DNN–CNN combination was obtained as the optimal model (average F-1 score: 97.54%), achieving individual classification performances of 99.03%, 97.46%, and 95.83% for fishing boats, passenger ships, and container ships, respectively. The proposed approach outperformed previous approaches in prediction accuracy, with further improvements anticipated when implemented on a large-scale real-time data collection system.

Research on Course-Changing Performance of a Large Ship with Spoiler Fins

The poor maneuverability inherent to large ships is a non-negligible problem that restricts the development of the shipping industry, as large ships can only cruise at an excessively conservative speed when they encounter complicated traffic conditions; nevertheless, ship collision accidents still occasionally occur. In the present study, the novel concept of spoiler fins for modern large ships is proposed. In order to assess their effectiveness in enhancing ship maneuverability, a KRISO container ship (KCS) was selected to carry a pair of spoiler fins, after which a simplified simulation approach for saving the calculation resource was designed for ship collision avoidance conditions, and a full-scale numerical model, including the ship hull, fin, and fluid field domain, was established. Transient-state hydrodynamic forces were calculated during collision avoidance maneuvers using the CFD method; the pressure and velocity contours around the ship were demonstrated; and the ship motion trajectories under different initial ship speeds were simulated and predicted through the adoption of overset mesh and 6-DOF dynamic mesh techniques. Eventually, the improved course-changing performance, dependent on the spoiler fins, was validated.

Investigation of container strength when fixed in an open wagon equipped with pneumatic bags

Ensuring the efficiency of railway transport in international traffic needs the development of combined transport systems. Container transportation is the most relevant among them. Containers are usually transported by rail on platform wagons. Along with this, the lack of platform wagons in operation makes it necessary to use other types of wagons for container transportation, such as open wagons. The fastening of containers in open wagons is carried out using pneumatic bags. To study the effectiveness of applying such a fastening scheme, the load of the container during transportation in an open wagon was determined. The conducted research will contribute to creating recommendations for improving rail transport operations' efficiency.

A unified cross-series marine propeller design method based on machine learning

Propeller design diagrams, like Bp-δ diagram, are widely applied in ship propeller design. However, different propeller series use various selection approaches, so comparisons between designs are only possible after individual candidates are chosen.This paper proposes a unified approach based on machine learning to allow efficient comparison and facilitate the selection of the optimal propeller amongst the available propeller series. The process starts by compiling propeller series data to generate a comprehensive dataset on propeller performance. This dataset is then used to train an Artificial Neural Network (ANN) model, which accurately predicts open-water propeller performance. Optimization techniques are applied to maximize propeller efficiency based on the specific needs of the vessel, while ensuring compliance with cavitation and noise constraints for safety. The model's accuracy is validated using data from the KRISO Container Ship (KCS), demonstrating the prediction's reliability. The method is then applied to select both open and ducted propellers for a variety of ship types to meet specific operational requirements. Ultimately, the optimized results are ranked by efficiency, offering an organized set of options for selecting the most suitable propeller. This approach eliminates the need for manual dataset correlation, significantly improving the efficacy of generating an outperforming initial design.

Directional wave spectrum estimation through onboard measurement data utilizing B-spline basis functions

The utilization of digital twin technology, which integrates real-world measurement data with a digital representation of the vessel, is garnering increasing attention within the realm of structural integrity management. Due to limitations in sensor deployment on ships, the integration of the digital twin framework, which merges measurement data with the ship's digital design model, becomes crucial for ensuring the effectiveness of structural integrity management. This investigation introduces a methodological approach for estimating localized responses at unmeasured locations, leveraging both measurement and design datasets. To approximate the response at unobserved sites, the directional wave spectrum is initially determined utilizing the least squares method, thereby, minimizing the disparity between the estimated and measured response spectra. The methodology notably incorporates cubic B-spline interpolation specifically to smooth the directional wave spectrum deployed on the heading-frequency domain. This deliberate choice of employing cubic B-spline interpolation underscores the emphasis on achieving a refined and continuous representation of the directional wave spectrum, thus enhancing the accuracy and reliability of the estimation process. To validate the proposed methodology, synthetic pseudo-measurement data derived from the wave spectrum and RAO of a 13,000 TEU container ship are employed. Subsequently, the directional wave spectrum is estimated utilizing the pseudo-measurement data, and the estimated directional wave spectrum, in conjunction with the Response Amplitude Operator (RAO), is harnessed to approximate the response spectrum at the location where sensors are not installed.

Enhancing energy efficiency in shipping container house: A novel approach using hybrid louver systems

Reusing shipping containers for residential purposes offers a promising approach to address global energy consumption challenges from economic and environmental perspectives. This study parametrically designed hybrid louver shadings by combining fixed vertical triangular slats with variable-depth horizontal rectangular slats, offering a novel approach to shading prefabricated buildings. The energy consumption, daylighting performance, and visual comfort were assessed for various shading configurations. Results indicated that implementing the proposed hybrid louvers, featuring fixed vertical triangular slats and variable-depth horizontal rectangular slats, significantly reduced Energy Use Intensity (EUI) to 133.95 kWh/m2. Moreover, Useful daylight illuminance (UDI), Daylight Autonomy (DA), and Glare Autonomy (GA) values improved to 95.38%, 89.97%, and 91.16%, respectively. The study highlighted the potential of the proposed shading system to significantly reduce overall energy consumption across various ASHRAE climate zones, including Miami (1A), Guangzhou (2A), Melbourne (3C), Esperance (3C), San Diego (3A), and Milan (4A). Notably, energy reductions of up to 50.2% were projected for climates such as Miami (1A) and San Diego (3A). Furthermore, container buildings in warm climate zones exhibited a significantly lower EUI range of 76.58 to 91.95 kWh/m². This study underscores the transformative potential of hybrid louver systems in promoting the widespread adoption of sustainable residential architecture, contributing to global sustainability efforts.