Ship Operation Research Articles

Satisfaction of passengers - process comparison between three different cruise ship classes

In cruise ship design, a ship designer often focuses on the ship’s function, while a cruise operator’s focus is on the creation of a comfortable and enjoyable cruise experience for passengers. Today, these two viewpoints are strongly connected, and thus, the way the inside of a ship is designed can impact how satisfied passengers are on their cruise journey. Thus, we need to figure out the best way to design a passenger ship from the perspective of the passengers themselves. In this paper, we analyse the differences in the combination passenger ship environmental elements and overall consumer satisfaction in two different cruise ship classes from the same ship operator, but from different eras. First, we present a theoretical framework and model for the cruise ship environment that consist of ambient, layout/design, social, product/service and onboard enjoyment factors. Then, by using data collected from the public domain, we compare two types of cruise ships using open-source data (N=755). This allows us to identify the factors contributing to the discrepancy in expectations across cruise guests. Based on this limited data, we create several linear regression models which indicates a favourable and statistically significant link between environmental elements and passengers' conduct while on board. Information processed this manner can be utilised to make informed decisions on cruise ship layout and amenities. In addition, the developed innovative KPI proved instrumental in influencing decision-making processes related to cruise ship designs and operations. Therefore, the findings from our research show a positive link between the onboard environment and the overall happiness of passengers.

Modernisation of Domestic Ro-Ro Passenger Ships Operating in the Philippines

This paper forms part of a wider study in the form of a Formal Safety Assessment of the domestic passenger ships operating in the Philippines, undertaken on behalf of the Philippines Government, and financed by the World Bank and the International Maritime Organisation. The paper focuses on design deficiencies of the domestic RoPax ships, primarily in damage stability. The process of ship selection for representation of the wider fleet risk assessment is explained, leading to one medium RoPax and one large RoPax, typical of some 500 of these ships, serving the open sea domestic trade in the Philippines. To this end, the selected designs have been subjected to a systematic process of damage stability and flooding risk analysis in order to identify design vulnerabilities, leading to risk estimation in the form of Potential Loss of Life. A number of risk control options have then been identified, enabling a thorough risk assessment and identification of cost effective RCOs, as well as impact assessment, using IMO risk acceptance criteria as basis. Despite the poor state of the domestic Ro-Ro passenger vessels operating in the Philippines, many of these aged, badly retrofitted, poorly maintained, and operated, it has been possible to identify cost-effective design solutions to raise the damage stability (and safety) standard of these ships to international standards of newly designed ships; it seems an unprecedented achievement, but evidently true. This presents the Philippine Government and the owners of these vessels with a unique opportunity to upgrade their domestic fleet of RoPax vessels and showcase these against the best in the world. Because of the similarity in the approach, the selection of risk control options and the overall analysis adopted, only one of the typical RoPax vessels selected is presented in this paper.

MariData – Digital Twin for Optimal Vessel Operations Impacting Ship Design

Energy efficiency is a key element for reduced shipping emission to meet future environmental regulations. Both design and operation play an important role to meet this goal and the perfection of the interplay between these aspects promises quick improvements to meet the requirements of short term emission standards. The MariData project [https://maridata.org] developed a forward-looking energy management and decision support system (DSS) for ship operation based on rational methods and data created during ship design and sets out to bridge the gap between design and optimized operation. The “digital performance twin” of a vessel, which is based on design data, is enhanced with lifecycle data covering the entire operational envelope and provides valuable feedback into design processes.

Rancang Bangun Sistem Monitoring Kinerja Fresh Water Generator Menggunakan Arduino Mega 2560

Fresh water is a basic need of the ship's crew in supporting the creation of smooth ship operating activities, therefore it is also necessary to have an auxiliary aircraft that can produce its own fresh water on board to reduce ship operating costs, which is called the Fresh Water Generator (FWG). FWG is an auxiliary aircraft that can convert seawater into fresh water by evaporation and condensation processes. Therefore, it is necessary to have a tool to monitor the performance of the fresh water generator. The purpose of this research is none other than to monitor temperature conditions, water TDS, and conditions on the input and output streams of the fresh water generator evaporator filter to be more optimal and efficient and to determine the reliability of the fresh water generator performance equipment system using a long distance in order to create better and higher quality fresh water production on board. This research uses a prototyping method system. After designing and testing the tool monitoring system for the performance of fresh water generators using Arduino mega 2560 based on LoRa Ra-02, this system is one of the new technologies that can monitor the performance of fresh water generators to be more efficient and reliable. Then the readings of all sensors can work properly with an average percentage error on the Max 6675 temperature sensor of 0.58%, pressure transmiter sensor 1 of 2.04%, pressure transmiter sensor 2 of 2.04%, and TDS sensor of 1,70% and the distance to communication between the receiver and transceiver reaches a range of 200 meters without obstacles and 97 meters through obstacles around.

Improved tide level prediction model combined GA-BP neural networks and GNSS SNR data

High-precision tidal forecasting plays a crucial role in providing reliable tidal information to coastal communities, ship operators, fishermen, and government agencies. Existing tidal prediction models, such as backpropagation neural networks (BPNN) and genetic algorithm-optimized BPNN (GA-BPNN), frequently difficulty in in achieving high precision because they rely solely on past tidal variations for real-time predictions. Signal-to-Noise Ratio (SNR) data have emerged as a valuable indicator closely related to real-time sea level changes due to the widespread adoption of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology. In this paper, we propose a nowcasting tide level prediction method by adding SNR data to BPNN, i.e., Improved BPNN and Improved GA-BPNN, which predicts the next momentary tide level value by using the previous 15 tidal values and real-time SNR as input factors to BPNN. The results of 100-day, 200-day and 1-year tide prediction based on Astoria and Friday Harbor tide gauge stations show that the improved model prediction accuracies are better than the traditional model. Meanwhile, among the two improved models, Improved GA-BPNN has the higher prediction accuracy and can adapt to different time lengths of tide level prediction.

Study on the Operation Safety of Sea Sightseeing Tourist Ships in the Waters of Kowloon Bay, Weihai

With the rapid development of social economy, tourism as a tertiary industry has developed rapidly, and sea sightseeing has become a popular project in coastal areas. These areas not only meet the requirements of tourists, but also promote the rapid development of the regional economy and make a great contribution to the development of local tourism. Tourist ship operation is one of the important ways for people to enjoy the scenery of the sea and experience boat travel, but also faces many problems such as navigation safety. In recent years, tourism and aviation safety incidents at home and abroad have frequently attracted social attention, and strengthening tourism navigation safety supervision has become a top priority. This paper selects the operation safety of maritime sightseeing and tourist vessels in the waters of Weihai Jiulong Bay as the research object, starts from the analysis of the factors affecting the operation safety of sightseeing and tourism vessels in Kowloon Bay, and establishes a hierarchical model of the operational safety factors of tourist vessels in Weihai Jiulong Bay from the perspective of the "man-ship-environment-management" system. The grey correlation analysis method is used to study the risk factors affecting the operation safety of tourist ships, and corresponding improvement measures are proposed to improve the safety of maritime sightseeing tourism and the safety of tourists.

A machine learning approach towards reviewing the role of ‘Internet of Things’ in the shipping industry

The technology of the Internet of Things (IoT) represents a cornerstone of the fourth industrial revolution. We adopt a machine learning approach to examine the effect of IoT technology on shipping business operations. Text mining and the probabilistic latent Dirichlet allocation are applied for an unsupervised topic modelling analysis of two hundred and twenty-eight academic papers. Our findings reveal the potential of IoT to provide more efficient approaches to business operations and improve the quality of services, highlighting the value of instant and secure information flow among all parties involved. Problematic areas of the new technology are also identified, in reference to issues of standardization and interoperability. Relatively few studies have used machine learning techniques to elicit insights into the holistic effect of emerging IoT technology in the shipping industry. The research findings highlight the potential of IoT technology to transform shipping operations, offering useful and practical implications to academics and professionals.

Comparison and Evaluation of Learning Capabilities of Deep Learning Methods for Predicting Ship Motions

The development of intelligent ship control systems in real-world conditions relies heavily on the accurate identification and prediction of ship seakeeping and maneuvering trajectories. In this study, we comprehensively evaluate a selection of deep learning methods to assess their learning capabilities in terms of idealizing ship motion behavior in realistic operational environments. To recover real conditions, we utilize historical Automatic Identification System (AIS) data and a time domain 6 Degree of Freedom (6- DoF) grounding dynamics model to generate ship motion sequences for a Ro-Ro passenger ship operating in the Gulf of Finland. Via a rigorous evaluation process, we validate the performance of these methods using extensive data streams. The analysis includes the identification and estimation of uncertainties between two ports. The paper demonstrates the proficiency of the selected deep learning methods in capturing ship maneuvering features, their potential use in the design of ship control and intelligent decision support systems.

Ship Design in the Era of Digital Transition

The evolution of ship design from a manual toward a computer-aided, digital approach has been drastic after the 1970s, with the explosive development of computer hardware and software systems. In today’s era of smart digitalization in the frame of Industry 4.0, recently introduced digital/software tools and systems increase the efficiency and quality of the life-cycle ship design process, but also the operational complexity and the demand for proper training of users of software platforms. Parametric optimisation and simulation-driven design, product lifecycle management, digital twins and artificial intelligence are nowadays frequently used by the maritime industry during the commissioning/quality control activities and in the various phases of ship design, ship operation and ship production. This paper presents an overview of notable developments in the above areas and the way ahead to respond to present and future challenges of the maritime industry.

Ship Operation Analysis and Optimization via Mobile Application

This paper is devoted to studying the structure and prospects of ship operations analysis and optimization via mobile applications, focusing on integrating multiple existing onboard monitoring and control systems. The main parts of the paper describe the current state of the most essential components of future overall shipping and ship design optimization using onboard and cloud-based monitoring systems from a dual transition point of view. Special attention is paid to the ship’s and its equipment’s efficiency improvements, fuel consumption and emissions reduction, cost-effectiveness enhancement, metrological accuracy, and compliance with current regulations. Timely development and deployment of the proposed onboard monitoring systems, in combination with up-to-date mobile applications and cloud computing, should play a crucial role in promoting sustainable and environmentally friendly shipping practices, improving operational performance, and reducing risks to human life at sea and the environmental impact of shipping. Another objective of this research is to review the current state of infrastructure used for fuel control, focusing on measurement systems and related analytics using appropriate mobile applications. The recommendations for integrating new and adopting existing monitoring systems and equipment for promising alternative fuels must be given to meet current regulations and provide required safety levels and measurement quality.

Possible influence of shipping operations on trace metals gradients along the Commodore Channel, Lagos State, Nigeria

The impact of shipping operations on the level of trace metals along the Commodore Channel of the Lagos lagoon is a topic that requires careful consideration. Shipping activities can have a significant impact on the water quality of the lagoon, which in turn can affect the health of the flora and fauna that depend on it. This study attempts to assess the impact of shipping operations on the level of trace metals along the Commodore Channel of the Lagos lagoon. Concentrations of trace metals in the Lagos Lagoon specifically the Commodore channel were determined in March 2016, and the downstream variation was compared. Trace metals (Pb, Cd, Fe, Zn and Co) were determined using Atomic Absorption Spectroscopy (AAS). The concentration of trace metals were found to be beyond the permissible limits stipulated by World Health Organization (WHO) and European Union (EU). There exists an exception in the mean level of Zn (0.23±0.1107mg/L) in the harbor water which was lower than the WHO standard of 5mg/L as at the time of this study. The trace metals showed the decreasing order of Pb > Fe > Zn > Co > Cd with concentration of 1.0±0.6mg/L, 0.62±0.2mg/L. 0.23±0.1107, 0.14±0.12mg/L and 0.05±0.02mg/L respectively. The result of contamination factor showed the harbor water had very high degree of Pb and Cd contamination. This high CF can be caused by the shipping and vessel transportation route and gateway of the harbor into the country. Continuous monitoring is required to assess environmental quality and adopt suitable management techniques in order to prevent the negative impacts of shipping operations on trace metal occurrence in harbors.

Systems Reliability and Data Driven Analysis for Marine Machinery Maintenance Planning and Decision Making

Understanding component criticality in machinery performance degradation is important in ensuring the reliability and availability of ship systems, particularly considering the nature of ship operations requiring extended voyage periods, usually traversing regions with multiple climate and environmental conditions. Exposing the machinery system to varying degrees of load and operational conditions could lead to rapid degradation and reduced reliability. This research proposes a tailored solution by identifying critical components, the root causes of maintenance delays, understanding the factors influencing system reliability, and recognising failure-prone components. This paper proposes a hybrid approach using reliability analysis tools and machine learning. It uses dynamic fault tree analysis (DFTA) to determine how reliable and important a system is, as well as Bayesian belief network (BBN) availability analysis to assist with maintenance decisions. Furthermore, we developed an artificial neural network (ANN) fault detection model to identify the faults responsible for system unreliability. We conducted a case study on a ship power generation system, identifying the components critical to maintenance and defects contributing to such failures. Using reliability importance measures and minimal cut sets, we isolated all faults contributing over 40% of subsystem failures and related events. Among the 4 MDGs, the lubricating system had the highest average availability of 67%, while the cooling system had the lowest at 38% using the BBN availability outcome . Therefore, the BBN DSS recommended corrective action and ConMon as maintenance strategies due to the frequent failures of certain critical parts. ANN found overheating when MDG output was above 180 kVA, linking component failure to generator performance. The findings improve ship system reliability and availability by reducing failures and improving maintenance strategies.

The Importance of Anchor Chain Maintenance at MT. Griya Flores

Anchoring is tying the ship to the bottom of the water so that the ship is not exposed to currents and wind. When anchoring anchors, there are often various kinds of problems stemming from internal and external factors. Internal factors themselves include problems with parts of the anchor that support anchor performance caused by lack of maintenance, especially on the anchor chain. This is what makes the author choose to raise the issue of the role of anchor chain maintenance. The research method used is qualitative descriptive by describing the role of maintaining a good and correct anchor chain. The data used is in the form of primary data obtained through field observations and interviews. Secondary data are obtained through documentation and literature studies. The results showed the need for anchor chain maintenance procedures to support the implementation of PMS on board, to create safe conditions when docking and support anchor work. The impact that will arise if you do not carry out the role of maintaining a good and correct anchor chain, namely corrosion, exhaustion of wildcat gears, influence on the anchor berthing process, the heavy up anchor process is disrupted and can hamper ship operations.

Exploring the ship operation safety indicators of international ports in Taiwan

Port safety plays an important role in port operations. Ship size has become larger and the port environment has rapidly changed in recent years. Ship accidents occur in the port area due to the complex environment in the port area. To improve past decision analysis methods on port operation safety fields and reduce fatalities and financial loss for potential accidents, the novelty of this study is to construct a safety index of ports with the application of the Best Worst Method (BWM). Four dimensions and 14 indicators were summarized based on an intensive literature review. The BWM was implemented to prioritize the weights of dimensions and indicators. Based on 21 expert questionnaires, the results indicate that the ranking of dimensions is ‘human’, ‘ship facilities’, ‘port facilities’, and ‘documentation check’. Regarding the ranking of indicators, the top three are ‘fire-fighting and life-saving equipment’, ‘captain’, and ‘pilot’. Safety improvement strategies (e.g. revising inappropriate operational rules and strengthening human safety education and training) based on these research findings are provided. The merits of this paper are presenting a simpler questionnaire-filling method and overcoming the traditional complicated questionnaire survey process and research limitations (e.g. indicator independence problems in the Analytic Hierarchy Process, and the complexation of filling out a questionnaire in the Analytic Network Process). In addition, the findings can help decision-making for port management authorities, port practitioners, and shipping operators (shipowners) regarding policy implementations of port safety.

Bi-objective ship speed optimization based on machine learning method and discrete optimization idea

With the introduction of increasingly stringent carbon dioxide (CO2) emission regulations in the shipping industry, shipping companies face significant pressure to reduce emissions while ensuring profitable ship operations. A practical bi-objective speed optimization (BSO) algorithm for container ships is developed based on the variable weight idea, the discrete optimization idea, and the ideal point method to meet the urgent needs of shipping companies to improve operating profits and reduce emissions. By constructing two BSO models of ship main engine fuel consumption-ship sailing time and ship operating costs (SOC)-ship CO2 emissions (SCE), the effectiveness of the BSO algorithm is confirmed through extensive optimization experiments. The results show that compared to the historical sailing results, the BSO reduces the SOC by 4.43 % without causing a significant increase in the SCE, and reduces the SCE by 0.25 %. Moreover, the results demonstrate the effectiveness and feasibility of the BSO in reducing the cost of ship operations while reducing the impact on the environment. The results provide valuable references for shipping companies in the trade-off analysis between economic and environmental benefits and optimization decision-making.

A hybrid graph attention network based method for interval prediction of shipboard solar irradiation

Solar energy ships came into being to reduce carbon emissions of global shipping and fossil fuels consumption. Accurate prediction of photovoltaic power generation can improve the economical operation of solar energy ships and reduce the power fluctuation of ship power systems. This study proposes a novel hybrid method to forecast ultra-short-term solar irradiation in a solar energy ship along the east coast of China. An improved graph attention framework is trained to describe the dynamic spatial-temporal topology among five weather stations and a solar energy ship. Initial interval prediction results are then determined by an improved Divided Period Optimization (DPO) method. According to distribution characteristics of the prediction error of shipboard solar irradiation, a Dynamic Prediction Interval Modification (DPIM) method is proposed to further optimize the prediction accuracy and interval width of ultra-short-term solar irradiation. Comparison experiments show that the dynamic connection matrix effectively improves the prediction accuracy, and the DPIM method reduces the PI width of 28.84%–37.79 % in shipboard solar irradiation. The proposed prediction method can accurately predict the shipboard solar irradiation at the period of significant disturbance, and provide effective technical support for the economic scheduling of solar energy ships.

Estimating Accident Reduction Rate after Maritime Traffic Safety Assessment Using Synthetic Minority Oversampling Technique and Machine Learning Algorithm

This study was focused on deriving the MTSA-related accident reduction rate (ARR) required to calculate the safety benefits before and after expanding the scope of the system. By performing spatial analysis using geographic information system technology, MTSA-related accidents were identified on maritime routes near both assessed and unassessed project sites from 2010 to 2014. Subsequently, by applying the synthetic minority oversampling technique to balance the data, the algorithm learned from the random forest using the operational data of coastal passenger ship operations and accident data near unassessed locations where MTSA is not implemented. Then, the trained model was applied to predict accident occurrence in the absence of MTSA near the latest operational information of coastal passenger ship operations at the assessed project sites. The MTSA-related ARR was then calculated by applying the actual accident occurrences during operation near the assessed project sites where MTSA was implemented. The MTSA-marine ARR calculated at 17.41% can be applied to the calculation of safety benefit for MTSA. The results of this study can provide quantitative evidence for the application of higher-level systems considering the burden on regulatory targets when improving MTSA or similar systems.

Evaluation of dynamic risk factors for ship operations

ABSTRACT The comprehension of the risk sources behind ship accidents is important not only due to environmental destruction, and loss of life and property, but also because of their economic effect on a global level. There are static risk evaluation forms on ships to decrease or prevent risks stemming from the nature of routine operations. However, ship operations are performed under the effect of static and dynamic risks. For this reason, the risks that may be caused by changing conditions must be taken into account to prevent accidents. However, the safety of operations can be maintained with self-renewing risk profiles. In the present study, dynamic risk factors that affect the safety of ships were identified and a questionnaire was prepared to evaluate the effects of these risk factors on operations. It was examined quantitatively how dynamic risk factors increase the risk of operations. Consequently, the study examines the effects of dynamic risk factors on ship operations and provides a framework for pre-accident risk management, a practical contribution to the creation of a safe and efficient working environment for the ship.

A novel cooperative optimization method of course and speed for wing-diesel hybrid ship based on improved A* algorithm

The wing-diesel hybrid ship, utilizes a wind propulsion system, which is significantly influenced by weather conditions. Therefore, a cooperative optimization method for course and speed is proposed based on an improved A* algorithm, considering weather changes and the ship characteristics, to achieve further improvement in the ship operational energy efficiency. Firstly, a meteorological and hydrological dataset is utilized to establish a dynamic maritime area model with complex weather conditions. Based on this model, the paper considers the actual navigation circumstances to define prohibited and navigable areas. Secondly, the improved A* algorithm incorporates the impact of weather on wing-sails. The improved A* algorithm considers the ship operating position and enhances the evaluation function by variables normalization of fuel consumption and voyage time. This cooperative optimization ensures the maximization of wind energy utilization, thereby achieving the goal of reducing fuel consumption while meeting the overall voyage time requirement. Experimental validation is conducted with the ship “New Aden” in the Indian Ocean region, and the results show that, compared to the original route, the optimized route reduces total fuel consumption by 5.48%, increases the navigation distance by up to 2.18% and increases the navigation time by 5.74%.

Investigation on toxicity of ammonia releasing from storage tank onboard through CFD simulations

ABSTRACT This study investigates potential risks associated with ammonia releases from a case ship to be constructed in a Korean Shipyard and operated in Korean coastal areas. The focus is on understanding how ammonia disperses from the onboard storage tank’s Vent Master and the associated toxic zones. Numerical simulations using CFD tools were employed to model ammonia gas dispersion scenarios. The study revealed that if the safety valve opens, the high internal pressure of the ammonia tank could release a significant amount of ammonia through the vent mast. Thus, determining the position and height of the vent mast is crucial for safety design. Simulations showed that even if ammonia is released at sea through the vent mast, it poses minimal risk to nearby residential areas. The vent mast, positioned at least 4 m above the ship’s deck, ensured rapid gas dispersion, reducing the chance of human exposure. The findings aim to enhance the safety design and operation of ships, providing valuable reference material for policymakers, regulators, engine manufacturers, and ammonia suppliers, ultimately contributing to the establishment of safe ship design and construction practices in the future.