Nowadays the interest for environmental issues is steadily gaining ground. Inescapably, the shipping industry, is also called to contribute its fair share towards the reduction of air pollution. Harbour tugboats need to comply both with international but also with local exhaust air emission regulations, which are anticipated to become more stringent in the future. Kotug, is interested to proactively explore suitable powering solutions, which can be applied at its Rotortugs’ newbuilding scheme, to comply with upcoming regulations of the intended port of operation. The goal of this thesis is to develop a decision-support tool to facilitate the selection process between alternative prime mover combinations, based on what is needed in terms of operation while considering technical challenges, environmental performance and economic returns. The effect of a variety of promising alternative fuels matched with suitable prime movers was investigated for the period 2018-2033, which was set as the study’s time horizon. LNG, Methanol, Biodiesel and DME were assessed as feasible solutions. The first two were associated with gas-burning engines, whereas the rest were associated with conventional 4-stroke compression ignition engines, similar to the ones already installed in the existing Rotortugs running on MGO. The environmental performance differs substantially to a conventional prime mover running on MGO. However, to adhere to future stricter regulations in certain sea areas, additional after-treatment systems are necessary. Research indicated three post-treatment solutions are the most effective in limiting the majority of the combustion-related emissions; Selective catalytic reduction, diesel particulate filter and oxidation catalysts. The total environmental fingerprint of a vessel can be however assessed only under the context of its propulsion configuration layout. The operational profile of a tugboat is highly variable, allowing room for the exploitation of different drivetrains than the traditional diesel-direct layout, which could lead in benefits mainly in terms of fuel efficiency and maintenance savings. Three drivetrains were selected; a diesel-electric and two hybrid, based on AC and DC topology. Before deciding to invest on a future boat a ship-owner is expected to be eager to comprehend the cost-related issues between alternatives. Moreover, the already proven design of the diesel-direct Rotortug, complemented by the necessary after-treatment technologies, makes sense to be the first option to consider, provided it complies with the anticipated regulations. This option is considered the baseline case. It is reasonable to compare all other options against the baseline case. To this end, a decision framework based on a techno-economic evaluation is proposed for supporting ship-owners to take informative decisions on investment considerations; Three objectives were set; economic performance, environmental performance and cost-effectiveness. To quantify economic performance a comparative TCO analysis was implemented, while for environmental performance the emission output of alternatives is compared. Last, cost-effectiveness adds valuable insight into how good money are allocated towards emission reduction targets and provides a methodology to compete for funding from a regulatory authority. Two case studies are presented that demonstrate the utility of these methods and enhance the understanding of the impact of decisive factors in decision-making. It is concluded that the proposed decision-support tool enables decision-support at an early-stage; nonetheless can still be improved, by incorporating additional factors and expanding certain modules with more details.

Maritime transport covers about 90%of world trade and is seen as one of the most cost-effective way of transporting goods. In order to safely accommodate vessels calling into ports, tugs are operated to assist various sized vessels with navigating, manoeuvring and berthing. Competition within the ship handling market has always been fierce. Boskalis Towage, through a network of joint ventures, offers harbour- and terminal towage services around the world, comprised of about 450 vessels. Operating a large fleet comes large Operating Expenses (OPEX). In order to deliver a more cost-effective service, Boskalis turns to business intelligence (BI) to help with the decision-making process. Maintenance is crucial for the operation of the vessels, but also a large part of the expenses. Large deviations in maintenance costs and operational performance exist between the joint ventures. However, the reasons behind these deviations are yet unclear due to the absence of continuous monitoring capabilities. The objective is therefore to increase the management capabilities of detailed performance monitoring of M&R expenses and performance of tugs, through which possible improvements can be determined. The M&R of tugs is influenced by various factors, such as utilisation, operational location and installed equipment. This research has evaluated the current Enterprise Performance Management (EPM) of Boskalis Towage and used literature on Maintenance Performance Management (MPM) in order to erect a multi-criteria hierarchical function-specific framework which is a framework for maintenance performance monitoring. The presented framework covers aspects of alignment with business objectives and plan, strategy and implementation. Moreover, the framework presents the maintenance function and its areas of performance measuring of equipment-, cost- and process performance across the organisational hierarchy. By performing a regression analysis of the maintenance data, this has also resulted in an increased understanding of the relationship between maintenance costs, operational data and vessel characteristics. As a result, an equation with acceptable statistical accuracy for running repairs has been found. The implementing of the MPM framework on the maintenance of tugs has resulted in the MPM model. This model focuses on the running repairs and dry docking direct costs and activities, incorporating basic maintenance types, i.e. Preventive Maintenance (PM) and Corrective Maintenance (CM). Running repairs have been evaluated in the time domain while the evaluation of dry dockings have been evaluated as events due to their relationship with regulatory surveys. The model incorporates operational-, maintenance- and financial data, tug specifications and operational conditions in order to evaluate maintenance performance. Maintenance Performance Indicators (MPIs) have been established to determine deviations and deficiencies among the different aspects of M&R. Challenges were faced on the aspects of data quality and data management, especially in the areas of maintenance process. The model has been validated through a detailed evaluation of five selected tugs. The underlying reasons for the underperformance of these tugs have been found and verified with knowledgable bodies and thus validating the model and its ability to determine underperformance and deviations. An alternative performance analysis method, Data Envelopment Analysis (DEA), has been researched and shows promise. The Slack-Based Measure (SBM) DEA model is a non-parametric frontier approach, based on Linear Programming (LP), to evaluate performance based on slacks (room for improvement) of Decision Making Units (DMUs). The method is capable of determining similarities in inefficiency in maintenance performance, comparing it to results from the MPM model. It, therefore, proves the method is capable of quick evaluation of maintenance performance and of determining new maintenance targets. However, the model is simplistic and requires further detailing and is unable to determine underlying reasons for underperformance. The regression equation and DEA have been used to formulate a so-called performance region which describes the lower- and upper bounds of the running repair costs for individual tugs. Unfortunately, data quality doesn’t allow for the determining of these bounds for all vessels nor for dry docking. New key benchmarking targets have been established through quartile values for respective Maintenance Performance Indicators (MPIs). With this newly obtained knowledge, future maintenance performance evaluation of tugs can now be performed with a higher detail through analysis conducted with the developed MPM model.

Jumbo Maritime is a shipping company active in the heavy lifting market. Jumbo uses stabilisers to enlarge the stability of the ship during lifting. By using stabilisers Jumbo can perform heavy lifts with a relative small ship, this is one of the major advantages. Downside of using stabilisers is that the installation and de-installation is a time-consuming process. Besides that the usage also causes safety issues, more and more port authorities no longer approve the usage of stabilisers. Research is done in finding alternatives for the use of stabilisers, focusing on alternatives for new ships that need to be build. Looking at Jumbo’s position in the market shows the following unique selling points, for the J-type: • Limited draft compared to its competitors • Length < 150 meters On the one hand the usage of smart stabiliser to speed up the installation and de-installation of stabilisers is looked at. Because smart stabilisers are still stabilisers, also alternatives are analysed that do not require a stabiliser at all. By widening the ship, the ship can be stable enough to perform lifts without extra support from stabilisers. A model is created to be able to determine the required width for each concept. This model is generated based on the input and requirements set by literature, market analysis and Jumbo’s specifications. Some of these requirements are: limited dimensions, minimum required stability, anti heeling and deadweight. The four concepts that are created as input for the model are: • Base concept, concept 1..., Concept 2... Concept 3...Concept 4...: A case study is performed to be able to compare the concepts. Three cases are created, which are actual relevant cases for JumboMaritime: For each concept the costs are determined to sail the certain case. Because the heavy lift market consists of somany single jobs that differ a lot on weight, complexity and distance it is difficult to map the revenues. The costs are determined per job, to be able to compare the different concepts. Costs consists of the capital costs, fuel costs and operational related costs. Besides cost per job, also cost per ton/mile is calculated to determine the economical speed. For case 1 the economical speed is 14 knots, which is similar to the required design speed. This means that the ships sails most cost efficient at this speed. The time that is saved by eliminating the stabiliser is used to reduce the sailing speed. In this way the same amount of jobs can done in the same time. This research shows that the saved fuel consumption as a result of slower sailing can compensate the longer sailing time and increased resistance for a wider hull concept. Comparing the concepts in the different cases shows that concepts 3 and 4 are more beneficial in case the stabiliser usages goes up. It can be concluded that awider hull shape can operate at 2-5%lower costs. The fuel consumption of the V-shape decreases more significantly at lower drafts. It is an advantage at ballast sailing or during sailing light cargoes. Besides the lower costs, the increase of deck space for the wider concepts is added value in the heavy lift market. An other important advantage of the wider ship concepts is the elimination of safety issues because the usage of stabilisers is not needed any more to lift heavy cargoes safely.

In this thesis, it was examined how high-frequency operational data collected from an automated recording system, can be used to develop a vessel’s performance model. Both the traditional and the Machine Learning (ML) approach were examined. In the traditional approach, every component of the propulsion plant was examined separately, while empirical or semi-empirical methods were used for the added resistance due to waves, wind and shallow water. Historical data were used for verification and tuning (where needed) during the development of the model. In the Machine Learning approach, the method of Support Vector Machines was used. Also, it was examined whether a vessel’s performance model can be developed by combining the methods mentioned above. Thus, in total four models were developed. The data regarding the last year of operation of the vessel Schippersgracht were used (45000 data points) to test the models. It was found that with all the four models, 80 % of the examined dataset was calculated with an error of less than 10 %. The daily fuel consumption was calculated for the same dataset and for 90% of the days examined, the daily fuel consumption was calculated with an error of less than 10 %. The main conclusions about the developed performance models is that all the models resulted in more less the same accuracy. The model based on the traditional approach has the advantage that it offers more information and more outputs can be taken, compared to the ML approach which only provides the output of the fuel consumption. On the other hand, the model based on the ML approach is significantly faster and allows many iterations to be performed in a short time. Regarding the models which were developed by combining the approaches mentioned above, they did not result in additional advantages. Finally, two case studies were performed. In the first case study, it was presented how the model, developed following the traditional approach, can be used to examine how modifications in the propulsion system will influence the vessel’s fuel consumption. The case of switching from fixed rpm (that is currently used) to a variable rpm system was examined, which is a real scenario that the company is considering. It was calculated that installing the variable rpm system and a frequency converter will result in annual savings of around 250,000 € while the investment’s payback time is estimated to be around two years. In the second case study, a future voyage simulation algorithm was developed. This algorithm receives as inputs the voyage plan(route and speed) and performs a calculation for the voyage’s fuel consumption by taking into account the weather predictions. The voyage simulation algorithm is making use of the ML model. A few cases were examined in order to examine how many days in advance the calculation is reliable (due to the uncertainty of the weather predictions) and about the time interval that should be used in the algorithm. However, clear conclusions were not derived, and further examination is suggested(by examining more voyages).

Many recycled materials in different industries are traceable. The recycled content is often known in percentages. Steel is a material which gets recycled almost 100%. However it is not common practice to specify the recycled content for steel products. From a sustainability point of view this information can be very valuable. This thesis identifies the barriers and opportunities for a class notation that specifies the recycled content of a vessel or marine structure. Furthermore, the way certification of steel would influence trust, traceability and transparency of a circular economy in the shipbuilding industry has been investigated. This was done from the point of view of a classification society but to reach the conclusion, the entire shipbuilding value chain has been investigated. Semi structured interviews were held with different stakeholders from the ship building value chain. These interviewees were steel manufacturers, original equipment manufacturers, ship yards, recycling yards, classification societies, financial institutions, ship repair yards and shipping companies. Barriers that were found are, amongst others, technical feasibility, steel market, yard logistics, customer willingness to pay, green washing, ship recycling standards, demographic differences, etc. The first conclusion is that, although sustainability is high on the agenda for most companies, the efforts and goals on this matter are scattered and uncoordinated. All stakeholders, except for the steelmakers, see the benefit for certification of recycled steel in terms of increased sustainability. The steelmakers possess the ability to provide all necessary information and there are no technical barriers. This means that they are able to provide steel products with a specified percentage of recycled content. The main barrier for different stakeholders are the extra resources they need to spent to incorporate a new type of certification into their business processes. None of these stakeholders want to take the risk as a single player, but are willing to take part once collaboration amongst all stakeholders are guaranteed. This creates the business opportunity for classification societies to provide this opportunity and take the lead for this innovation. A review of current certification steps for steel products has been made and adapted to include extra steps to certify the recycled contents. The effect of this extra steps with regard to stakeholders downstream has been investigated and validated by means of the interviews. Another topic is the digitalisation. Because of the increased ability to process big data, there are opportunities to tackle certification of recycled products and the subsequent traceability by means of blockchain technology. These opportunities are identified and are proposed as optional further research topics.

This study aims to develop a decision support tool which enables shipowners to select the most appropriate alternative fuel technology to comply with possible different imposed emission regulations while ensuring optimal business performance. In this context, most appropriate is defined as a fuel alternative which minimises required freight rate (RFR) while maximising overall performance on technological, environmental and other criteria. A decision support tool was devised combining a decision model based on the simple multi-attribute rating technique (SMART) with a financial model based on discounted cash-flow (DCF). Additionally, an optimisation model was implemented to optimise for minimal required freight rate through slow steaming. The decision tool provides shipowners with a quantified impact on their current business if they do not transition to alternative fuels under a 'market based measure' (MBM) regulatory scenario, as well as best-alternatives if their current fuels do not meet regulations under an 'emission cap' (EC) scenario. The decision tool is evaluated under optimistic, average and pessimistic scenarios in 2020, 2030 and 2050. Under an emission cap scenario, the results showed an overall preference for Fischer-Tropsch diesel (FTD) as the most promising alternative maritime fuel both in terms of SMART performance and required freight rate, followed by upgraded bio-oil (UBO). Nevertheless, the average difference in required freight rate of alternative fuels compared to HFO remains substantial, at 43% and 38% higher for Fischer-Tropsch diesel under a 'no regulation' scenario and 'market based measures' scenario, respectively. It is therefore evident that without regulatory intervention, heavy fuel oil is expected to retain dominance based on cost. Even under a market based measure (MBM) scenario, the average required freight rate of HFO increases only by 3.4% overall. For LNG, market-based measures lead to an average increase in RFR of 4.1%. These results suggest that in order for the maritime industry to transition towards sustainable alternative fuels, policymakers, governments, international organisations and lenders need to align their policies to collectively enable a more sustainable shipping industry - not only by enforcing stricter regulations, but also by providing the correct financial incentives.

Container liner shipping organisations facilitate trade between all regions globally by deploying vessels in a network with a corresponding sailing schedule. Since the container shipping network is part of a larger network, it is favourable for the continuation of the supply chain to maintain this schedule. Although the importance of the continuation is recognised, the industry is not known for a high degree of schedule reliability. This is due to the presence of regular and irregular uncertainties in the network. It is found that more than 90% of these uncertainties find their origin in the port area. By deploying the LSS methodology, the causes and effects of port uncertainties are further analysed. It is found that the port uncertainties lead to transportation waste at sea in the form of fuel costs and emissions. The shipping sector is held accountable for approximately 3% of the global carbon dioxide emissions. A Dynamic Arrival Time (DAT) design is proposed to recover a schedule after a delay takes place to avoid waiting times and arrive JIT in the port. This differentiates from the current state where a First Come First Serve policy is maintained. An Agent-Based Discrete Event Simulation model in Anylogic is proposed to test the effects of a DAT implementation in a Short Sea Shipping (SSS) network that is subject to regular and irregular port uncertainties. It is found that a fuel and emissions reduction of up to 6.1% is achieved when a DAT is implemented under irregular uncertainties in the network. If the DAT incorporation is tested for regular uncertainties, the effect is equal to a 0.5% reduction in fuel and emissions. Furthermore, it is found that the design leads to a decrease in the port turnaround time due to improved arrival predictability. This improved predictability enables the shipping line (or agent) to recover the schedule and avoid drastic fuel increase during the departure transit. This study's findings are important for the policy-making for shipping companies due to the substantial energy savings that are measured. Furthermore, this study contributes to the MEPC.323(74) resolution, where voluntary cooperation between the port and shipping sectors is encouraged to reduce the GHG emissions from the shipping industry.

With the energy transition taking up speed and strong decarbonisation ambitions offshore wind is becoming a major source of green electricity. European countries are among the leading drivers of the offshore wind expansion on both the wind turbine as well as the vessels side. It is expected that until 2030 170 GW of capacity can be installed, equalling 16000 wind turbines. The wind turbines are serviced using either small vessels or commissioning and service operation vessels (C/SOVs) when parks are larger and in more challenging conditions further away from shore. The C/SOV market is still in development and it is not known how many of these vessels are needed to serve the European offshore wind industry in 2030. It is further unknown until now which factors influence the need for these vessels as both market dynamics as well as operations have not been researched until now. This research first defines the quantifiable factors influencing the need for C/SOVs in offshore wind parks. These are the park parameters such as the distance to shore and the number of turbines in the park. These data are used in a factor model and Monte Carlo simulation to make an assumption on the required number of vessels. The results are then compared against qualitative factors influencing the need for C/SOVs indirectly. Out of a high and a low case, the low case was shown to be the most likely fit for the research results. It showed that to serve the offshore wind market in 2030 between 122 and 138 vessels are needed, which is 12 to 28 more than currently are active or on order. Considering the fact that the industry needs to adapt to a new market, it is crucial to know which factors drive that market and how they influence it. This project allows for researchers to dive further into these factors and research them in more detail. Further the research can assist industry players in their investment decisions and yards can accordingly plan capacity.

Port performance is under pressure due to high congestion levels and a need to reduce GHG emissions. The industry is looking for ways to increase efficiency in ports. A promising concept is known as port call optimization where waiting times and turn-around times can be reduced. This research contributes to the body of port call optimization literature by identifying the importance of performance attributes for innovations. Identifying the performance attributes and how stakeholders weight these has never been explored before in literature. The relative importance has been derived using the Bayesian Best-Worst Method. The performance attributes: incidents, near misses, robustness and added value, are most important for evaluating an innovation for port call management. Results on micro level show heterogeneity and homogeneity in preferences for port attributes. Both ports with complex nautical restrictions prefer performance attributes related to safety. The port with centralised management is more commercially driven and there is homogeneity within a port. Furthermore, heterogeneity is seen between stakeholder groups and between port authority (PA). PAs from centralised port value attributes relatively different than a PA with decentralised and licensing activities. Some differences and similarities can be explained by the port contexts. The findings can be used for the design of innovations in port call management. Research could be repeated in other port cases to validate the significance of these results.

This study was conducted to address the lack of insight port authorities have in port competitiveness. Within this thesis a data-driven model is presented that allows to assess port competition within a bounded scope. This model is developed from the ground up and programmed in the Python programming language as a stand-alone solution. The model doesn’t rely on extensive black-box models as often found in related literature. Due to its generic nature, the logistic chain choice model can also serve as a base model for different scenario analyses. Scenarios could include the change of ship-size, (oil) price or cost scenario’s and future throughput forecasts. The model can provide insights regarding effective port investments and policy making.

The IMO has proposed Just-In-Time arrivals and services as one of the short-term solutions to reduce greenhouse gas emissions in maritime shipping. The Just-In-Time concept allows vessels to optimize their speed during the voyage. If a vessel earlier knows at which time it is requested to arrive at the pilot boarding place, the vessel can adapt its speed to arrive when the berth, nautical services and the fairway are available. Besides environmental reasons, shipping companies as MSC consider the Just-In-Time initiative as a major port call process optimization. It has obvious advantages for MSC since this concept aims to minimise unnecessary ad hoc waiting times and reduces fuel consumption per mile steamed. In the past, it has become clear it is extremely complicated to implement this initiative. Research is required in order to find out what needs to be improved to enable Just-In-Time arrivals and services. The current port call processes must be analysed. As start point, a case study is conducted on the port call processes of the world’s second largest shipping company in the biggest port of Europe. In this study, the current port call processes of MSC container shipping in the Port of Rotterdam are identified and improved in order to enable Just-In-Time arrivals and services. First, an actor-stakeholder analysis is conducted to understand the incentives and relations between actors involved in a port call. Subsequently, this information is used in the process analysis. In this study, the port call processes are analysed by both qualitative and quantitative research. The research has emphasized the complexity of the port call business processes. Actors primarily act on the basis of their own incentives. In order to enable Just-In-Time arrivals and services, collaboration is required among the involved actors. Today, most actors see the benefits of this concept. However, some actors are not willing to share data about their processes since this is considered as sensitive information. In case actors are willing to share data, it can be concluded that the information is not exchanged frequently enough or does often not meet any data standards. As last, this study shows that adaptations are required to the current business process of a port call in the Port of Rotterdam in order to enable Just-In-Time arrivals and services.

The consequences of climate change are becoming more and more visible. A significant cause of this is CO2 emissions; the shipping sector is responsible for 3% of global CO2 emissions. As a result, the Fourth IMO GHG Study 2020 presents pathways to reduce the GHG emission of the shipping industry by 50% by 2050. Recent IMO goals have overtaken this to reduce net emissions to zero by that year. As a result, research in renewable energy sources has grown in significant interest, offering a wide range of potential solutions. Recently, (green) ammonia (NH3) has been added to these pools, as it is carbon-free and has a higher storage density than liquid or pressurized hydrogen. However, when comparing ammonia to the current conservative fuels, its energy density is still not at the same level, and more fuel volume would be required to deliver the same amount of energy. There are two ways to address this challenge. More frequent bunkering or larger volumes for the fuel tanks on board at the cost of cargo space and thus income. This is a difficult choice to make in the pre-design as it depends on the choices of other owners as well. This report investigates the impact of a fuel switch to ammonia on the ship design and bunkering pattern based on the current operational profile of 1025 seagoing ships. A mixed integer linear programming model will establish the optimal fuel tank volume and bunkering strategy for each vessel. This model considers rerouting for trips that are not feasible and two approaches for the bunker strategy. Besides, a port model will establish the ammonia bunker pricing based on the resulting demand in each port. The estimated ammonia bunker prices are implemented in the bunker strategy model. This is repeated till a balance is found. The two models represent an Ammonia Powered Shipping Network considering a homogeneous shipping market. The report presents the results and key factors influencing the balance between the fuel tank volume and the sailing range. The simulated bunker strategies show different possibilities for finding this balance and reducing the operational impact caused by the transition to ammonia.

Liner shipping companies engage in strategic cooperation to deal with the market’s overcapacity, capital intensiveness and volatile freight rates. However, these partnerships do not reach their full potential and have lowered the level of service in the industry. To identify how carriers would be able to realise further synergies, this study has investigated the current performance of liner shipping companies and developed a model to identify and assess potential partnership opportunities. This model supports liner shipping companies in their decision of who to partner with and what kind of partnership to pursue. The study has been confined to a single transpacific route. On the basis is an overview of vessels that sail this route over a period of several years. This overview includes information on arrival dates, number of containers on board, capacity and operator for each vessel. This is used to examine local demand and deployed capacity, vessel utilisation and competition. The analysis confirms trends found in literature, such as the growing size of vessels that are enabled by strategic cooperation. Most importantly, it indicates flexibility on the side of vessel operators in their decision of which vessels they deploy per route during the year. A synergy model has been developed based on the findings in literature and the results of this analysis. This model uses the identified flexibility in vessel deployment and builds on the notion that liner shipping companies should pursue more integrated partnerships to realise further synergies. The model was also applied to the case of CMA CGM’s acquisition of APL in 2016. After calculating the costs for sailing each of the available vessels, potential partnerships were identified for the analysed route. This was done by optimising vessel deployment for the cargoes to be transported at minimum cost and with a limited number of partners. A strategic-level assessment was then performed to determine whether a partnership would be feasible among the identified companies and to what extent integration should take place. By including both the companies’ motivations (drivers) and factors that facilitate a positive environment (facilitators) for the partnership, this assessment brings multiple perspectives to the table. The conclusion is that carriers can adjust which vessels they deploy to match transport demand and that they can use this to realise further synergies. By using a model with both a quantitative and a qualitative component, it is possible to identify potential partnerships that result in cost reduction, maintain high vessel utilisation and allow for improvements in the level of service that companies offer to their customers. It is recommended to extend the coverage of the model to include more routes, ports and vessels for a more comprehensive analysis. Furthermore, using variable transit times in the model to determine when vessels are available would make the model more realistic and would also allow changes in vessel speed to be included in the analysis. It is also advised to explore opportunities for regulatory bodies to apply this model for monitoring and assessment of the effects partnerships have on competition per route.

This thesis provides a 'proof of concept' for a vanadium bromide redox flow battery-powered ship (E-Conoship) in the short sea shipping market of North West Europe. An elaborate analysis of historical voyage data has been carried out to determine operational feasibility. To determine economic feasibility, a cost-comparison model has been constructed, including the capital, operational and voyage related expenses of shipping. Case study method, including scenario analysis, has been used to determine under which circumstances E-Conoship is cost-competitive with diesel-powered ships. In conclusion, its operational feasible to operate an E-Conoship with a maximum range of 500 nautical miles. Two options are available: (1) direct replacement of vessels, and (2) acquiring voyages. It is economical feasible, provided that more than one of the following factors are addressed: battery system cost reduction, CO2-tax, lower electricity prices in comparison with marine gas oil, subsidies, increased amount of annual sailing days, and lastly financing at lower interest costs compared to diesel-powered ships.

The work focuses on evaluating the feasibility of a Sale and Purchase online platform. Drivers and barriers to implement it have been identified performing a qualitative analysis, involving people directly employed in the industry. Later, a concept design for the platform has been analysed, overcoming the barriers and using the drivers as strength points. Finally, an economic evaluation has been performed

To mitigate global warming, worldwide CO2-emissions have to be cut. Similarly, the shipping industry has to drastically decrease it's emissions. Rules and regulation on emissions are becoming more stringent. Moreover, the public and clients are demanding less pollution from ship operators. While many shipping operators are looking at a suited fuel alternative, no consensus has been reached on the best alternative to entirely dispose of harmful emissions from shipping in the long-term. Fugro is one of the ship operators interested in entirely bringing down the emissions of their vessels that are built from 2030 onwards and therefore this research is conducted. This thesis will research how and if Fugro can achieve the IMO target of 70 percent CO2 reduction in 2050, by deploying net zero-emission fuels in Fugro vessels as of 2030. An evaluation will be done to assess which alternative fuels are feasible and most suitable, based on current technologies and considering future scenarios around technology. The research will approach this problem by firstly conducting an extensive literature research into alternative fuels and Multiple Criteria Decision Analysis methods. Thereafter the Fugro fleet, potential future vessel developments, future scenarios and alternative fuels are researched to set up a technical framework for this case problem specifically. With this technical framework in place, the method itself is conducted. This is done in two ways. First, by conducting a qualitative approach using the Analytic Hierarchy Process, also involving stakeholders from Fugro and the industry. Second, by carrying out a case application of the four best alternatives on a representative Fugro vessel. This approach, including a qualitative approach extended with a quantitative one results in a substantiated advise. Both methods complement each other on points where the other falls short. The AHP includes stakeholders and takes into account criteria that are difficult to quantify. The application approach tests the feasibility of the proposed alternatives and tests the potentially subjective or intuitive outcome of the AHP method. With methanol scoring well in both approaches, the conclusion of this research is that methanol is the advised alternative fuel for future Fugro vessels. Liquid hydrogen is the highest rated fuel alternative from the AHP. The application example shows that this fuel isn't able to comply with the required Fugro operability however. Contrary to that, ammonia is a highly rated alternative in literature and is also scoring well in the application part of this thesis, but is not rated as a suitable alternative by stakeholders in the AHP. The last considered alternative in both parts of the method is synthetic diesel, which is comparable to MDO. This fuel scores well in both methods but is considered to remain too expensive to become a viable alternative in the coming ten years. In this thesis, estimates of different fuel options are presented and some design concepts and considerations on the different alternative fuel applications are given. The next step for Fugro would be to work out a methanol concept, by going through the design spiral more than once and work out the design choices and challenges this fuel brings. With alternative fuels being more expensive than MDO, this thesis pointed out that the economic speed will change when using alternative fuels, which is another point that requires more research. While the AHP proved to be a suitable way to score fuel alternatives while including stakeholders and assessing criteria qualitatively, it also pointed out that it is difficult to solely base an alternative fuel advise on this method. Potentially, the AHP results would improve when the questionnaire is simplified by reducing the amount of considered criteria.

With the current focus on climate change, the shipping industry is forced to decarbonise its fleet. The need for decarbonisation is not only fuelled by the aspirations of the sector but also by laws and regulations from countries and organisations. As these laws are gradually tightening and there is not one clear technical or operational solution, there is a need for a strategy to combine multiple operational and technical solutions. These solutions, as well as other aspects, are associated with risks and uncertainties, such as fuel prices, technical aspects and cost. To help the decision makers in this field, this research aims to present a method that can be used as the basis for a decision making tool. The method will mainly focus on finding the lowest total cost per tonne mile but will also consider other aspects that are found to be necessary, such as safety and technological readiness. The difficulty in the decision is the number of factors that could influence the decarbonisation of the vessel and how these aspects influence each other. The addition of CRSs (Carbon Reduction Systems) will affect the operation profile of the vessel and, with that, the fuel use of the vessel. With fuel being one of the main expenses of ships, fuel use will affect the total cost of the vessel. The method will combine several different analyses to consider all aspects and risks. The strategy will be created by adding CRSs every time the vessel does not comply with the regulations anymore, affecting the vessel's operational profile and fuel use. The cost of fuel use, and other OPEX, will be evaluated with an NPC (Net Present Cost) analysis to compare costs in time. These costs will then be entered into an MCA (Multi Criteria Analysis) and combined with the other criteria, such as safety and technological readiness level, leading to a strategy selection. To increase the method's robustness, the whole set of calculations will also be subjected to a Monte Carlo simulation to mitigate the risks that concern future inputs. The method was translated into a model where a case study was carried out to validate the method and to find some early insights. From this came the result that the method creates valid answers and that there is one method that is the most cost-effective manner to comply with the regulations. This is plainly sailing slower; it has almost no expense and significantly reduces the cost of shipping.