The supply for luxury yachts is growing which increases the competition between yachtbuilders. Additionally to lead time reduction, yachtbuilders are trying to maximise their output with the available space. This puts extra pressure on the logistics process. Waste in the logistics process reduces the efficiency and for that must be avoided. The available data of the logistics process of yachtbuilders can be limited since it is not their core-business. The objective of this thesis is to explain how a model of the logistics process of a yachtbuilder can be made and how it can be used to improve and predict the logistics performance. This thesis focuses on improving the interior logistics process at yachtbuilder Oceanco by reducing the non-value added movements waste.

An important step in determining the performance of a commercial ship is to determine the vessel fuel consumption in offdesign conditions. In previous studies, the vessel fuel consumption is obtained using machine learning algorithms which use navigational data and meteorological data to train the models. Due to the inaccuracy of the weather hindcasts, the accuracy of these machine learning techniques is not satisfactory for Compagnie Maritime Belge (CMB) which is the company for which this study is performed. Since the vessel motions are a direct result of the meteorological conditions an approach is investigated in this research to replace the meteorological data by motion data to train a machine learning algorithm and obtain accurate results. The vessel motions are captured by a motion sensor on board the vessel. Multiple machine learning techniques were tested in this research. The data used to train the best performing learning algorithm represent the motions by the first and second moment of the nonderivative motion and the second moment of the first derived motion. These moments are obtained from the motion distributions of a time window of 5 minutes. The obtained best parameters for representing the motions were obtained by testing if a spectrum must be provided, how many motion derivatives are required, which moments are needed and how large the time window must be. Although the accuracy of the developed learning algorithm is determined as satisfied no digital twin could be made and tested. Unfortunately, due to the limited amount of data available no separate algorithm could be made which uses the Extra Tree learning, as digital twin base, to determine the overconsumption of the vessel. Only data from one voyage was available. All this data was used to train the learning algorithm to have as much variance as possible in the data set such that a good learning algorithm could be developed.

The parcel transport industry utilizes automated parcel sorting systems to effectively manage incoming parcels. These systems typically comprise multiple infeed conveyors that converge onto a single merge conveyor for parcel merging. However, the conventional First-Come-First-Serve merging method results in reduced utilization of the merge conveyor and throughput. To address this, a novel approach is proposed in this study, combining a dynamic programming-based control algorithm with a maximum heap algorithm. This approach aims to enhance the utilization of the merge conveyor and achieve a balanced load among the infeeds. To optimize utilization, an additional imaginary segment is introduced ahead of the merge conveyor, synchronously moving with the merge conveyor's speed. This segment is intelligently populated with parcels using dynamic programming, eliminating unnecessary empty spaces. Results demonstrate that the dynamic programming approach outperforms other techniques in terms of utilization. This research presents an initial investigation into implementing dynamic programming as a control algorithm to improve merge conveyor utilization in the parcel transport industry.

This report describes a research performed to investigate in which way it is feasible to dynamically alter the floor plan and desired capacity of airport baggage handling systems by making use of autonomous individual transport robots. By researching the state of the art of both conventional baggage handling systems and transport robot systems, a new baggage handling solution called the Baggage Robot Concept is proposed. A simulation model of this Baggage Robot Concept is developed to evaluate the performance of this concept in the sorting process of a baggage handling system at medium-sized regional airports. The research identified that the most important design elements for the Baggage Robot Concept are the number of robots and the floor layout configuration. The Baggage Robot Concept turns out to be a feasible concept. An important design choice that is identified in the research is the trade-off between manoeuvre space of robots and the location of charging and storage positions. This study has been a first exploration in integrating autonomous robot systems in baggage handling systems, contributing to future proof and cost efficient operations.

The shipping industry contributes significantly to the global greenhouse gas emissions due to the use of cheap fossil fuels. The booming cruise ship industry is forced to reduce the emitted greenhouse gases and emission of carbon dioxide to be able to access remote areas with higher restrictions and to comply with upcoming regulations. Fuel cells are a promising alternative to conventional diesel-powered systems to reduce the emissions. Next to the physical implementation, fuel cells have additional operational limiting factors, which have to be matched with the power demand. Compared to conventional combustion engines, longer start-up times and a lower dynamic response ability can be expected. Typically, the energy demand is estimated for all electric power consumers with the load balance approach in the conceptual design phase. The actual power demand is calculated based on the absorbed power and additional predefined factors for specific conditions. This conflicts with the dynamic power demand of cruise ships in various operational conditions. This thesis discusses the used bottom-up approach for such an improved prediction of the total power and energy demand of an expedition cruise vessel for the early design stages. The focus is on identifying the peak loads and load changes under consideration of the passenger behaviour, environmental and operational conditions on the basis of predefined typical days of operations. The dynamic power demand is predicted for the different propulsion, auxiliary and hotel systems, as identified and grouped in the system breakdown. This is done by using separate prediction models for the defined groups of systems and electrical components. The required energy per day is calculated directly from a time-domain integration of the power prediction. The demand for a whole operational profile is obtained by adding different typical operational days. The highest power is required by the main propulsion in sailing mode. However, expedition cruise ships often operate at lower speeds below the design conditions resulting in a considerably lower load. The maximum load of the hotel systems always appears in the morning, when all passengers are on board and several components ramp up from the lower night mode. Comparing the total power demand shows that the load changes caused by the hotel systems gets smaller in relation to the changes within the propulsion system as soon as the speed varies. In port condition, the relatively constant auxiliary load, including the hotel systems, remains and describes the baseload throughout the day. Generally, the dynamic prediction clearly shows the potential to better estimate the required power and energy, if varying operational conditions are considered. The method supports a well-founded decision on the power supply configuration, if it comes to hybrid systems including different power supply components and their operational characteristics. A multi-criteria decision analysis on the power supply side can build up on the required energy and power demand of a certain part load. The required fuel cell size can be quantified based on the estimated maximal load and the fuel tanks by considering the predicted energy demand of the specific group of systems.

Global supply chains currently are inefficient and unsustainable, due to their overlap and lack of interconnection. The Physical Internet (PI) proposes a paradigm shift in the way we move, store, supply, realize and use goods. The goal of the PI is to interconnect global supply chains, amongst others, on a digital level. For this, information systems are needed. Seaports, being drivers of global supply chains, therefore also need information systems on an integral level. In certain ports, Port Community Systems (PCS) serve as port’s information systems. They facilitate information exchange between stakeholders to better connect port processes. This research explores the potential role of PCS in PI ports. It does so by performing a Design Science Research (DSR). The artefact that is designed, is a conceptual information system that addresses key (PI) port functions. A case study analysis at Portbase, the Dutch PCS, has revealed what information is exchanged between which stakeholders. PI literature provides grip for PI information system requirements. The integration of the PCS analysis and the PI requirements has led to a design of an information system that shows the potential of a PCS in a PI port. The design shows that, according to the current offer of PCS services, PCS’ potential role focuses mainly on compliance related processes in PI ports. It also shows that the offer of logistics related services that are needed in PI ports, is limited. It is therefore recommended that PCS focus on an extension of logistics related services that are needed in PI ports. Future research may address the potential of PCS in a federated network of platforms.

Diesel-powered Automated Guided Vehicles (AGV) are currently deployed for container transport at terminal’s water side. Looking at the trends in formal legislation and market developments towards sustainability, these diesel AGVs are most likely to become outdated. Although battery-electric AGVs are an emerging, zero-emission alternative, there are serious technical, operational and financial questions regarding their implementation at brownfield terminals operating 24/7. Taking the northern side of the ECT Delta Terminal in the port of Rotterdam as a case, the operational and financial feasibility of replacing diesel AGVs by a battery-electric AGV fleet have been evaluated by means of a functional design of the AGV charging process, simulation study and total costs of ownership analysis. The results indicate that battery-electric AGVs opportunity plug-in charged at the automated stacking cranes’ transfer points prove to be an operationally and financially feasible alternative to diesel AGVs: operationally under the condition that a sufficient amount of AGVs, charging power and plug-in chargers are installed and financially under the condition that the increasing trend in diesel price and decreasing trend in electricity price will continue in the near future. As environmental legislation for heavy-duty vehicles becomes more stringent while there is a decreasing trend in electricity prices, battery-electric AGVs are most likely to become profitable for deployment at brownfield container terminals. Therefore, this study’s findings could pave the way for terminal operators to replace their environmentally unfriendly diesel AGVs by zero-emission vehicles, potentially becoming the key force increasing the global penetration rate of electric vehicles in heavy-duty industry.

In this thesis, a simulation cost model is presented that can be used to assess the cost-effectiveness of a selection of PdM programs. The proposed model combines a discrete event simulation (DES) model with a cost model. It is specifically designed to analyse the PdM programs for the vacuum compressor pumps at Duyvis. All the PdM programs proposed will work with vibration analysis to detect bearing failure. The simulation model will focus on the sample frequency and detect-ability of a PdM program. The sample frequency is related to maturity levels, and the detect-ability is related to detecting the different stages of bearing damage. The goal is to use the proposed simulation cost model to assess a selection of PdM programs on their overall cost and reliability.

This thesis presents a data-driven design approach for emission reduction using bunker delivery notes (BDNs) to help support the revised IMO strategy to achieve net-zero greenhouse gas emissions by international shipping close to 2050. This research supports the Horizon Europe’s Digital Twin for Green Shipping (DT4GS) project which focuses on the development of digital twins (DTs). Part of this project involves the development of a DT-supported method for the design and retrofit of ships. DTs are a promising approach for supporting maritime decarbonization efforts due to their simulation and big data handling capability. Despite the abundance of shipping data and growing digitalization, the potential of using ship operational data for decarbonization efforts remains not fully exploited. A data-driven method such as a DT could fill this gap. However, as DTs, by definition, require real-time connection between a physical entity and the digital representation, developing a true DT for new-build alternatively fueled ship designs remains a challenge. This research thus starts by looking into retrofitting using data from existing ships. A design framework is proposed to construct digital models to support a DT for retrofitting purpose. The proposed framework is tested on a case-study using a 300-meter bulk carrier. Since January 2019, operational ship data is collected through BDNs, a mandatory data collection method for ships of 5000 GT and above, adopted by the IMO. Constructing a DT based on BDNs is considered to be convenient as it provides a solid source of operational data in the future. First, the available data from the BDNs is preprocessed using an adopted framework based on data science literature. The resulting 5,678 data points are used for the construction of a model representing the bulk carrier and a model representing the green ship technologies part. A fuel consumption model is constructed to represent the bulk carrier. It utilizes a gray-box modeling approach, consisting of a white-box resistance model and a black-box artificial neural network. Both models incorporate environmental-dependent inputs. The investigated green ship technologies for the potential retrofit are represented by various wind-assisted ship propulsion (WASP) systems, namely a towing kite, a DynaRig sail, and a Flettner rotor. These systems are modeled using a white-box modeling approach, together with available wind data. Using an adopted integration framework, based on the propeller-engine matching procedure, both representations are combined into one green ship digital model. An environmental assessment is performed using the IMO's EEXI and CII assessment tools, respectively evaluating the design and operational aspects of the potential retrofit. Additionally, a financial assessment is conducted using the payback period. Results showed the design implications and emissions reduction potential of implementing such systems which will guide the retrofit decision by the ship's owner.

In hospitals, the duration of surgical procedures is susceptible to a number a factors resulting in varying procedure durations of the same procedure. When these procedure times deviate from the predicted surgical duration, scheduling conflicts arise, leading to patient discomfort and inefficient use of hospital resources. Considering the aging population and the subsequent growing demand for healthcare, hospitals are under constant pressure to provide cost-effective care. Indeed, this is also the case for the interventional cardiology unit, where the expected increase of the prevalence of cardiovascular disease is expected to put a strain on hospital human resources. Improving the efficiency has been an area of focus in many hospitals. In fact, advanced methods such as surgical workflow monitoring have been employed to identify points of improvement especially in procedure scheduling. Up until now, this has not been employed in cardiac catheterization laboratories in the Netherland. The purpose of this study, therefore is identify primarily the factors influencing variability of the procedure time of procedures of the cardiac catheterization laboratory of the Reinier de Graaf Hospital in Delft and secondarily the possible sources compatible with video-based workflow monitoring for phase segmentation of the procedures. Retrospective data obtained from DoseWise was used to analyze the procedure times and associating factors of coronary angiograms and pacemaker implantation procedures. In addition, process models of the procedures were created and activity recognition identifiers were studied through observer-based data acquisition. Analysis showed that in coronary angiograms variations of total procedure times are mostly due to variable preparation times, whereas for pacemaker implantation the lead positioning times were the main source for variability. In addition, more experienced operators were shown to complete the procedure in a shorter time (-4.35%|p=0.007) in comparison to less experienced operator. Instrument and equipment use were identified as possible sources for activity recognition along with the hand kinematics, however further analysis of reliability is required. The findings in this study provide a basis for future monitoring of the workflow of these procedures using real-time data acquisition methods to improve the efficiency in cardiac catheterization laboratories.

The demand for accurate and effective cool chains has been expected to increase, especially for pharmaceuticals in the air freight industry. However, several problems and challenges remain such as cool chain breaks and cool storage capacity constraints while there are rarely routine systems in place for consistent insight into the operational quality of such systems. Besides, the concept of a DT has received increasing attention in the literature, while a multitude of applications have been found in fresh cool chains. Therefore, the DT concept has been applied to a pharmaceutical cool chain for operational quality improvement. Firstly, a novel operational quality metric has been proposed: the OCCE. Consequently, a cool chain at KLM Cargo has been studied and modelled by means of the DES technique in order to derive a virtual representation. Consequently, the DT concept has been applied through the implementation of a decision support module for cool storage decision-making. The model implementation has shown that the cool chain operational quality has been improved while the average exposure of freight has decreased.

Although e-groceries is nothing new, the scale at which it is currently growing in the Netherlands has not been seen before. Over the past four years, the market share of online grocery shopping showed an average growth of over 20% per year. While it was already challenging to keep up with this high demand, it became increasingly more difficult due to COVID-19. In fear of getting infected, some consumers no longer visit the supermarkets, and instead, order their groceries online, further increasing the demand for e-groceries. One of the challenges caused by the high demand is handling enough customer orders in the online grocery fulfilment centres. These fulfilment centres can be described as “supermarkets” in which groceries are collected by operators instead of the consumer. After the groceries have been collected, they are either delivered to the consumer, or they can be collected at a pick-up point. While multiple strategies are described in literature that allow for the handling of more customer orders, most of them have only been applied to warehouses and distribution centres. Due to the significantly different operations of fulfilment centres, it is unknown whether the same strategies can be applied here. This thesis examines which strategies are appliable to online grocery fulfilment centres and how much additional customer orders can be handled. The identification of strategies was done through a case study at AH Online’s fulfilment centre in Rotterdam. A discrete-event simulation model was then used to evaluate the strategies. The output of this thesis is an overview of four strategies including their performance on timesaving, order picking productivity, and of course, the additional number of customer orders that can be handled.

Currently, Tata Steel IJmuiden is developing a new ironmaking process, called HIsarna. This technique will result in a significant reduction of carbon emissions, allow for more flexible input requirements for materials, and lead to a reduction of processing costs as well. In 2010, Tata Steel IJmuiden built a pilot plant to test and develop the technology of HIsarna. The next step is an industrial scale installation: the HIsarna demonstration plant. The design of the storage and handling area of this new plant is the subject of this research. At this area, the materials are stored and pre-processed before they will be injected into HIsarna. Thus far, Tata Steel has been working on the first design phase of the demonstration plant. Nevertheless, the design of the storage and handling area is missing. Consequently, the required quantities and necessary pre-processing steps of materials are unknown. Moreover, the existing storage and handling area at the HIsarna pilot plant cannot directly be used for the demonstration plant, because the new plant must meet HIsarna’s annual capacity requirements of around one million mt, be able to process iron-rich waste materials (also called reverts), and comply with HIsarna’s green ambitions. First, an analysis of materials is performed to establish the suitable materials and their properties for processing at the HIsarna demonstration plant. This analysis is essential to identify how these properties may affect the design of the storage and handling area. Second, the storage and handling area of the HIsarna pilot plant is analysed, particularly to identify the necessary processing steps and potential future bottlenecks of the HIsarna demonstration plant. Next, a design of the storage and handling area is created by using multi-criteria analyses and information obtained from literature. A simulation model, created in Simio® software and verified using several techniques, is used to examine the performance of the design, but also to investigate the affecting parameters; these are found by performing a fractional factorial design. Based on these results, optimal parameter settings are found to optimise the design. According to analyses performed, the suitable materials for HIsarna (besides the standard materials such as iron ore and coal) are by-products of the blast furnaces and the oxy steel plant: BF sludge, currently produced BOF sludge and historic BOF sludge. These materials are particularly interesting because of the high levels of iron, carbon and zinc. Less positive are the high moisture level, the stickiness and the inhomogeneity of composition. These might result in problems during processing. The following design is created: iron ore and coal will be transported by conveyor belt to the storage and handling area, historic BOF sludge by truck, and BF and currently produced BOF sludge by pipeline. Both the raw and handled materials will be stored in enclosed storage systems. Each material will be pre-processed at a dedicated handling line; all materials within the storage and handling area are transported by conveyor belt. The raw material storage of iron ore, coal and BOF sludge has a capacity for two days; and that of BF and currently produced BOF sludge for seven days. In addition, all handled material storages have a capacity of three days. Based on the results of the fractional factorial design, three designs for the storage and handling area are proposed. Each of the designs has a different starting point. The second design option provides the highest reliability and is recommended if financial resources are not a restrictive factor. The third design is an interesting choice when fewer financial resources are available but still an adequate reliability is aimed at. Finally, the first design results in the lowest reliability. This option may only be suggested when funds are limited. In all cases, redundancy can be added to the storage and handling area, increasing the reliability and decreasing the required storage capacity. All designs fit the selected location at the Tata Steel site, IJmuiden.