Containerized Research Articles

IOT IN CONTAINER SHIPPING INDUSTRY: APPLICATIONS AND EXAMPLES OF SOLUTIONS

The study analyzes the participation of technologies related to the development of the Internet of Things (IoT) network concept in the intermodal container transportation system. Since the beginning of the XXI century, IoT-related technologies have been dynamically spreading in almost all areas of international business, and this has changed the understanding of cargo owners about the quality of transportation services, among other things. Since transparency in monitoring the location of high-value goods is extremely important for transport companies, cargo owners, and all other stakeholders, and the intermodal transportation system is extremely complex due to the number and diversity of communications, the need to use IoT is obvious. As analysts predict that the introduction of IoT technologies in containerization will become widespread in the coming years, a study to assess the level of research on the introduction of such technologies and implementation in the practical activities of transport enterprises is relevant. The aim of the proposed review is to determine the degree of IoT implementation in the container transportation industry, areas of research on the topic, and identify factors that may impede the predicted rapid development of IoT in containerization. The proposed study includes an analysis of scientific publications related to the use of the Internet of Things in the field of international container transportation, identifies the main priority areas of such publications, establishes the areas of current implementation of IoT technology and determines the prospects for its use. Since the solutions that can potentially be obtained with the help of IoT are quite heterogeneous, the literature analysis is organized by grouping such publications into the following areas: container monitoring and tracking, enhancing supply chain transparency and visibility, optimization of operational efficiency, security and risk management, environmental impact and sustainability, challenges and barriers. The paper provides examples of online platforms and applications currently offered by major container operators that are related to this technology.

Quality Evaluation and Shipping Export Potential of ‘Eliteggul’ Korean Melon (Cucumis melo var. makuwa) to Singapore Using MAP Technology

Korean melons (Cucumis melo var. makuwa), which have a short shelf life and are prone to browning, face challenges when exported over long-distance via maritime shipping. However, the high firmness and low suture browning rate of the ‘Eliteggul’ cultivar suggest its potential suitability for long-distance export. This study aimed to evaluate the export suitability of ‘Eliteggul’ by comparing it to the widely grown cultivar ‘Alchankkul’. Consumer preferences were assessed based on fragrance, sweetness, overall preference, appearance, and firmness. Both cultivars were grown and harvested in a commercial greenhouse in Seongju-gun, South Korea. After being treated with modified atmosphere film, the fruits were stored at 6 °C for 1 day, transported to Singapore in a shipping container at 7.4 °C for 13 days, and distributed at 22 °C for 3 days. After 14 days of storage, analysis in Singapore showed that ‘Eliteggul’ maintained a significantly higher firmness at 12.9 ± 0.3 kg·cm2 compared to ‘Alchankkul’, which showed firmness at 8.1 ± 0.4 kg·cm2. Additionally, ‘Eliteggul’ exhibited less browning on the white sutures at 1.3 ± 0.1, indicating better preservation of its quality than ‘Alchankkul’, at 2.0 ± 0.3. Even at 14 + 3 days, ‘Eliteggul’ maintained a firmness of 12.8 ± 0.2 kg·cm2, which was significantly higher than that of ‘Alchankkul’, which showed a firmness of 7.7 ± 0.5 kg·cm2. Consumer preference surveys (n = 78) conducted in the local market revealed that customers favored the fragrance and sweetness of ‘Alchankkul’. Correspondingly, an analysis of volatile organic compounds (VOCs) from melon juice using a Twister stir bar method showed that ‘Alchankkul’ had significantly higher levels of ethyl(methylthio)acetate, ethyl propanoate, and benzyl alcohol, known to contribute to a sweet flavor. Furthermore, a thermal desorption tube VOCs test indicated that hexyl acetate and 2-methylbutyl 2-methylbutyrate were also significantly higher in ‘Alchankkul’. Despite this result, there was no significant difference in the overall preference scores between the two cultivars. This study indicates that ‘Eliteggul’, with a superior firmness, reduced browning, and high consumer acceptance, is ideal for long-distance export, offering foundational data to bridge the research gap in export-oriented melons and support the broader distribution of Korean cultivars.

Full-Scale Assessment of the “5GT System” for Tracking and Monitoring of Multimodal Dry Containers

A novel tracking and monitoring system for ISO 668 dry containers was realized by the ESA-funded “5G SENSOR@SEA” project, integrating 5G cellular technologies for massive Internet of Things with a GEO satellite-optimized backhauling link. The scope is the development of monitoring and tracking new services for multimodal container shipping. With the cooperation of four industrial partners and a telecommunication research center, the so-called “5GT System” was designed, developed, tested and validated up to field trials. Several modules of the system were designed, built and finally installed on the ship and in the teleport: the container tracking devices placed on the containers, the NB-IoT cellular network with optimized satellite backhauling, the Ku-band satellite terminals and the maritime service platform based on the OneM2M standard. The field trial conducted during the intercontinental liner voyage of a container ship showed primary technical achievements, including fair switching between terrestrial and satellite networks, reduction in packet loss in the open sea scenario and seamless integration of the BLE mesh network over the container tracking devices as NB-IoT/BLE LE Mesh gateways.

Women seafarers in Taiwan: policies, benefits, challenges, and bias in the data

ABSTRACT Gender imbalance and equality have been highlighted in the global seafarer market. Taiwan relies heavily on shipping development and three container shipping companies (Evergreen, Yang Ming, Wai Hai Line) in Taiwan are ranked as top 11 in the world. To highlight the important contribution of women seafarers in the maritime industry, and add to the growing body of research in this area, this paper adopted in-depth interviews (n = 30) with both female (n = 24) and male (n = 6) seafarers to explore their perceptions of women seafarers regarding policies, benefits, and challenges. Key themes were barriers to employment such as company policies but also perceived benefits of employing women such as their ability with specific tasks such as administration and conciliation, aspects not highlighted before. Also, and uniquely, we identify a significant obstacle to achieving gender equality, and that is that the data gathered was biased; often delivered through the paradigm of the very problem itself: gender inequality and a male-biased environment. Nevertheless, and through identifying this obstacle, we make suggestions for engaging with and overcoming such biases in future work, and also help increase gender equality in the industry in line with United Nations Development Goals.

Ship-Route Prediction Based on a Long Short-Term Memory Network Using Port-to-Port Trajectory Data

In this study, a ship-route prediction model based on a long short-term memory network using port-to-port trajectory data is presented. Data from a traditional automatic identification system—often used for ship-route prediction—are limited by uneven sampling intervals and noise. To address these issues, equal-interval data collected every 10 s from a target ship, which is a liner container vessel, were employed. Our study focuses on predicting the entire trajectory between the Gunsan and Busan ports. The root mean square error (RMSE), mean absolute error (MAE), and average distance d¯ between two trajectories were used as the key evaluation metrics. The analysis yielded excellent predictive performance, with the values RMSE = 0.000999, MAE = 0.000672, and d¯ = 0.101 km. This study thus provides a foundation for predicting complete port-to-port routes and offers practical insights for managing vessel operations. Accurate route prediction contributes to reducing port congestion, improving fuel efficiency, and lowering carbon emissions.

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°.

Electrifying Maritime Shipping: Evaluating the CO2 Reduction and Viability of Future Battery-Powered Container Ships Calling at the Port of Los Angeles

Abstract Maritime shipping plays a pivotal role in global logistics, influencing efficiency, emissions, and adaptability. This academic study envisions future electrified shipping fleets and evaluates the potential for CO2 reduction and the cost delta between grid electricity and marine fossil fuels using three types of container ships (5000-7999 TEU, 8000-11999 TEU, and 12000-14499 TEU). The scenario evaluated envisions an electrical charging infrastructure at the Port of Los Angeles, supported fully by wind and solar energy sources, to recharge future battery-powered ships. It is recognized that electric-powered ocean-going long-transit ships are not practical, but this study provides baseline groundwork to justify and support future development of hybrid or partially electric ships to help meet the International Maritime Organization’s (IMO) target of a 50% reduction in CO2 emissions from the world’s shipping by 2050. Additionally, a real case study analysis of all container ships at POLA was conducted using container vessel activity to further evaluate the practical implications and benefits of this transition. This analysis underscores the significant electrical infrastructure investments required and highlights the potential economic challenges as well as the environmental benefits of integrating electric power into maritime operations.

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

Development of Measurement Tool for Energy Efficiency Operational Indicator (EEOI)

Abstract The Energy Efficiency Operational Index (EEOI) is a value that represents the energy efficiency of a ship’s operations in relation to CO2 emissions. Improving energy efficiency is one of the important ways to achieve cleaner shipping. Statistical methods are used to predict a number of parameters that determine the value of the EEOI. The aim of this research is an EEOI measurement tool. The analysis was carried out with 100 container ships. The Ship Traffic Emission Model (STEAM) and Multiple Linear Regression (MLR) methods were used in this research. STEAM modeling is based on estimated fuel oil consumption and MLR generated from STEAM EEOI results. This modeling also considers the following parameters overall length, width, draft, depth, service speed, container capacity, average speed of last trip, average draft of last trip, installed main engine power, and travel time. Furthermore, EEOI measurement tools were created in the Android-based version which represent EEOI values based on the ship’s last trip per voyage.

THE EVOLUTION OF DEEP SEA SHIPPING CONTAINER ROUTES CALLING AT ITALIAN PORTS IN THE PANDEMIC PERIOD

In this paper, the resilience of the Italian deep sea shipping (DSS) container routes in situations of severe crisis (in this case, the COVID-19 pandemic), is studied quantitatively, distinguishing gateway and transshipment DSS routes. In addition, DSS container routes in the pandemic period (i.e., from 2020 to 2023), have been compared to those before the pandemic, in particular from 2011 to 2019. The results have shown that while gateway traffic decreased from 2019 to 2023, due to the reduction of the demand connected to the pandemic, transshipment traffic has increased, in contrast to the trend before the pandemic that has shown a considerable reduction from 2011 to 2019. The results also show that the naval gigantism phenomenon increased from 2011 to 2019 but seems to have stopped since 2019. This analysis is highly important in the field of maritime studies, as it covers the two most important phenomena concerning maritime transport that have occurred over the past 10 years (i.e., naval gigantism and the COVID-19 pandemic).

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.

Collaborative Optimization of Container Liner Slot Allocation and Empty Container Repositioning Based on Online Booking Platform

The shipping market is unpredictable and volatile due to some uncontrollable factors such as epidemic, conflicts and natural disasters. There is always an imperfect match between the supply capacity of liner companies and the actual demand of the market, which leads to a waste of slot resources and/or unsatisfied customer demand. Furthermore, the trade off between empty container transportation and laden container transportation is the crucial problem of strategic importance for liner companies. To deal with the above problem, this paper aims to develop a new solution to the collaborative optimization problem of container slot allocation and empty container repositioning by exploring the resource allocation, storage, and repositioning methods collaboratively. An online booking platform is introduced in this paper, and no-shows and customer preferences are considered in the analysis. An innovative integer programming model is established based on an online booking mode and a delivery-postponed strategy. A new branch-and-cut algorithm is then proposed to solve the problem. Finally, numerical experiments are conducted to verify the effectiveness of the proposed model and algorithm. The experimental results show that collaborative optimization can remarkably enhance the revenue of liner companies along with increasing the utilization of slot resources.

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.