The last decade has marked a rapid and significant growth of the global market of warehouse automation. The biggest challenge lies in the identification and handling of foreign objects. The aim of this research is to investigate whether a usable relation exist between object features such as size or shape, and barcode location, that can be used to robustly identify objects in a bin.

A deep convolutional neural network (CNN) is built in MATLAB and trained on a labeled dataset of thousand product images from various perspectives, to determine on which surface of a product the barcode lies. Training results show that while the training set accuracy reaches 100%, a maximum validation accuracy of only 45% is achieved. A larger dataset is required to reduce overfitting and increase the validation accuracy. When sufficient classification accuracies are reached, smart picking strategies can be implemented to efficiently handle products.

Autonomous sailing is considered by many to be the next big thing in the shipping sector. Many companies, including Rolls Royce and DNV are investing heavily in research that helps in the development of autonomous shipping. Most research is currently being done regarding autonomous navigation, communication and cyber security. What is often neglected in these studies is the need for maintenance within the engine room. With all ships having multiple crew members solely dedicated to keep the engine room going, their absence will have a big impact on the design of engine rooms.
This paper aims to find what equipment will become a weak point in engine rooms once these are no longer occupied and maintained by crew members, and how these weak points can be eliminated. With the absence of reliability data that corrects for maintenance and crew interference, this paper tries to find these weak points by analysing crew member behaviour. It is assumed that if a crew members spends more time on equipment, or deals with it more frequently, then that piece of equipment is less reliable.
In order to find what equipment can be seen as a weak point, a redundancy reduced risk index was created. This index is a combination of the frequency index, which states how often engineers spend time on equipment, the severity index, which lists the consequences of failure for equipment, and the redundancy index, which is dependent on the level of redundancy of components. The redundancy reduced risk index is split into three levels: low, medium, and high risk. Medium and high-risk components were considered weak points, while low risk components were not.
For all high risk and for most medium risk components, solutions were generated which will reduce the risk index of the components or system overall. For some components, the risk was deemed acceptable, as any solutions would be too costly to justify the risk reduction. Solutions were split into two categories: specific solutions, which were all catered to specific components, and a bulk solution, which was the installation of a second drive train. A second drive train would include a second main engine, gearbox, propeller and rudder.
With the solutions found, the two categories were subjected to a financial analysis to see if they are commercially viable. The solutions were applied to ships of four different sizes, ranging from a 6,000 GT feeder to an 85,000 GT capesize bulk carrier. For these ships, the OPEX were calculated and divided into four categories: Operational costs, fuel costs, capital costs and crew costs. The initial investment for the machinery plant was also calculated. This was the basis to finding the increased cost for solutions found in this paper.
Using these specific solutions, the machinery plants will become roughly 50% more expensive, while adding a second drive train will increase the machinery cost by 110%. It is assumed that these solutions will eliminate the need for a crew and therefore eliminate all crew costs. Using these figures, it was found that the specific solutions will lead to a potential yearly savings of $600,000.– for all ship sizes, and adding a second drive train will lead to a potential yearly savings of $500,000 – for the small ship and $163,000.– for the larger ships.
With both the specific solutions and the second drive train, the machinery plant is deemed reliable enough for continuously unmanned operation, which proves the technical feasibility of continuously unmanned engine rooms.

A traceability system for primary packaging materials is compulsory by law for the food- and beverage industry. With a good traceability system an organisation is able to quickly recall unsafe products to protect consumers health. This research aims to analyse and improve the traceability of the primary packaging materials at Heineken Netherlands. A second focus of this paper is to reduce the impact of a potential recall on packaging materials. This research is performed based on the method called the five-stage-of-a-prescriptive-model. Using this model results in a step-wise development of an improved design. A literature study, a problem analysis and the customer requirements will be the input of this design. Analysing the current traceability systems showed traceability issues as unregistered in- and outgoing flows of packaging materials at pallet level and registration issues as double scanning. Another issue that is observed is lot segregation at different breweries, at different production lines and on different production dates. The lot segregation will cause that in times of a recall an organisation has to withdraw significantly more products. Using a Multi Criteria Analysis and the Ease and effect method results in a final design based on organisational changes, periodic evaluations, barcode technology and RFID technology. Implementing this design guarantees the traceability since all the incoming and outgoing flows are registered at pallet level and the registered data is periodically evaluated. By procedure adjustments at the supplier and at Heineken also results that the impact of the recall volume can be reduced.

Problem statement - Damen Shipyards (Damen) wants to use Big Data analysis to gain new market insights and forecast vessel demand. These insights are valuable because Damen keeps standardised vessels in stock in order to significantly reduce delivery times. This concept gives Damen a competitive advantage, but is not without risk since they must speculatively build vessels beforehand. As a case, Damen wants to improve the current forecasting methods for the thriving offshore wind market - for which they supply vessels - based on data derived from the Automatic Identification System (AIS). AIS data includes among others information about identification, location, speed, date and time of approximately 200.000 vessels worldwide. Made Smart Group (MSG) is an information service provider specialised in nautical information, and maintains the world’s largest AIS database. MSG and Damen joined forces for this research.

Objective - The objective of this research is to determine if and how Big Data analysis can be used to model (future) demand for Crew Transfer Vessels (CTV) being used for Crew Transfer Operations (CTO) in the offshore wind industry.

Methodology - Almost 45 million AIS location reports of 39 CTVs servicing 263 turbines in 3 offshore wind farms throughout 2016 are analysed to derive key figures of the executed CTOs. Key figures are e.g. the weather window, and the number of executed CTOs per hour. The CTV demand is modelled based on these key figures, and three wind farm specific input parameters: number of turbines, distance between wind farm and port, and the sea state distribution.

Results - The CTO demand of the 263 analysed wind turbines was on average 113 per year in 2016. This average CTO demand has a variation of almost 50% between turbine types. With an accuracy of 4%, it is modelled that a yearly average of 12.4 CTVs are needed to service these 263 turbines. Furthermore, the CTV demand decreases on average with 11% in the three analysed wind farms if the CTO limit can be increased from 1.5 m to 2.0 m mean significant wave height. This result in a potential cost saving around € 5.1 million on a yearly basis for these three wind farms alone.

Implications - AIS data can be used to model vessel demand and gain insight into the market size. The accuracy of the developed model can be improved by adding: more wind farm specific variables; and/ or data of more CTVs/ wind farms. The gained knowledge about using Big Data analysis to forecast the CTV market size is useful and important for the introduction and future development of commercial AIS based data analysis. Furthermore, it provides insights into the operational profile of CTVs. This can be used to develop better vessels, better service the market and ultimately help to lower the cost price of offshore wind energy. It is believed that the maritime sector could profit from AIS data analysis.

There is a never-ending quest to search for better ways of planning and controlling projects. Currently the majority of all projects are planned with the critical path method and controlled using earned value management. This way of planning and controlling projects has been used for decades now and can be seen as traditional project management (PM). This current PM paradigm does not work well enough for one-off projects. Risk assessment and risk management are not sufficiently covered in most standard PM methods. This thesis looks at the potential of critical chain project management (CCPM) in combination with extensive risk management techniques for improving project performance. The thesis will cover the development of a risk-based critical chain project management (RCCPM) methodology and its application to case studies at a shipyard. The study concludes that RCCPM is a viable alternative for traditional PM in the maritime industry. Improvements include, amongst others, a reduction of the lead-time, more effective monitoring and controlling, and improved dealing with risks and uncertainties.

This thesis contains the model development of a simulation model, to aid the selection of a heavy lift vessel for a North Sea decommissioning project. The model considers fixed steel platforms with maximum topside weight of 1.400 [tons].