This research investigates the optimal design of a charging infrastructure network for heavy-duty battery electric vehicles (HD BEVs) in the Netherlands. It addresses the challenges posed by the limited range of HD BEVs and the need for a robust charging network to support logistics operations. The study identifies key factors influencing fleet owners' charging behaviour and develops a qualitative decision model to estimate the demand distribution between private and public charging. Additionally, an optimization model is proposed to determine the optimal locations for public charging facilities, balancing the objectives of minimising infrastructure costs and maximising the number of trucks that can recharge.

This research presents a mathematical model for routing that incorporates multiple depots, occasional drivers, and multiple depot visits, a problem faced by many companies in reality. An Adaptive Large Neighborhood Search (ALNS) algorithm was employed, using Random and Worst removal operators, Basic Greedy and Regret-2 insertion strategies, a roulette wheel for operator selection, and simulated annealing for acceptance criteria. Computational experiments validated the effectiveness of the ALNS algorithm and model.Sensitivity analysis revealed that adding depots (ODs) can reduce routing costs by up to 15.21%, with various OD capacities offering additional savings (7.86% for 60% capacity and 9.97% for 70% capacity). A case study with the Dutch e-commerce company Ochama, scaled to 150 requests, confirmed practical applicabil- ity, achieving cost reductions of 11.37% for small vehicles and 6.85% for medium-sized vehicles. The results highlight that integrating ODs into routing strategies can significantly lower costs, with optimal outcomes dependent on market research to balance savings, service quality, and OD capacity.

This thesis investigates a container drayage problem involving terminals, depots, and shippers. Each terminal operates a homogeneous fleet of trucks that start and end at their respective terminals, carrying either one 40ft or two 20ft containers, and can make multiple trips within a single planning horizon. Terminals handle full containers and maintain a limited stock of empty containers, while depots provide additional empty containers. Shippers have specific time windows for service, and requests include sending and receiving empty or full containers to designated terminals, with street turns of empty containers possible. An Adaptive Large Neighborhood Search (ALNS) algorithm is implemented to address this problem, outperforming traditional methods like CPLEX. Extensive computational experiments validate the ALNS algorithm's efficacy, highlighting the logistical benefits of optimal depot placement and the impact of street turns, emphasizing the practical implications for container logistics.

The installation rate of offshore wind energy has to be quadrupled by 2030 to meet the green climate ambitions of European countries, but this growth is hindered by logistical challenges. Therefore, this study explores how two transportation and installation strategies, shuttling and feedering, affect the installation rate with the inclusion of manufacturing ports. Shuttling is where the installation vessel collects components itself at a port, and feedering is where the installation vessel remains offshore and gets supplied directly via feeder vessels. This is a novel approach as feedering and manufacturing ports are often not considered and production rates at manufacturing ports have not been considered at all. A rolling horizon simulation model is developed, which uses a Markov simulation model for weather forecasting, with a 72.92% forecast accuracy over two weeks, and a greedy algorithm for transportation and installation optimization. Results indicate that accurate initial buffer calculations, depending on the production rate at the manufacturing ports and project-dependent characteristics, can increase the installation rate significantly for either strategy. Shuttling becomes more efficient than feedering if the distance between the manufacturing ports and the offshore wind farm is too large or the size of the feeder vessels is too small. Feedering is more efficient in all other circumstances and, on average, results in a 9.2% higher installation rate and reduces project duration by 29 days compared to shuttling.

This research explores the integration of electric vehicles (EVs) into the logistics of bulk-liquid transportation, specifically within Heineken Netherlands’ operations, using a two-echelon network. This study is pivotal as it aligns with global moves towards zero-emission regulations and sustainability in logistics. The primary question it addresses is optimizing a logistic network and truck operations for bulk liquid delivery by transitioning to EVs within a two-echelon framework, ensuring efficiency while adhering to sustainability and regulatory demands.
The investigation employs a sequential exploratory strategy, beginning with qualitative analysis to identify core challenges and opportunities, followed by quantitative methods to refine network design and decision-making. Advanced clustering techniques such as the center of gravity, p-median, and k-means are utilized to determine optimal depot locations, essential for overcoming the operational range and charging limits of EVs. This approach aids in developing a logistics network that is both operationally efficient and environmentally sustainable.
A significant portion of the study focuses on vehicle routing within the two-echelon location-routing model. It considers critical factors like the limited range of EVs, multi-compartment transport requirements for bulk liquids, and specific customer delivery windows. The model integrates these elements to optimize vehicle routes for efficiency and regulatory compliance, illustrating its practical use through the two-echelon multi-compartment electric vehicle routing problem with time windows (2E-MCEVRPTW).
The practical application of this research is demonstrated in a case study of Heineken Netherlands, highlighting the logistical complexities of transitioning to an EV fleet for beer distribution. The study examines operational challenges such as vehicle range and product diversity management, proving the viability and effectiveness of the proposed models.
Results discussion reveals that strategic network design using the center of gravity method significantly enhances kilometer savings and operational efficiencies. However, the benefits diminish with additional hubs, indicating an optimal hub number exists. While transshipment costs pose a significant challenge, outweighing the kilometer savings, potential cost reductions through increased reefer capacity and reduced transshipment times are identified, pointing to possible areas for improvement.
The study concludes that the two-step optimization process, integrating network design and vehicle routing, effectively addresses the research question. It not only shows the potential of EVs in transforming logistics but also underscores the economic and operational challenges of adopting a two-echelon network. The findings lay a groundwork for future innovations in sustainable logistics, though they caution the need for tailored solutions across different operational contexts and suggest further research into computational strategies and customer clustering for enhanced route optimization.

With the rapid development of cross-border e-commerce logistics, how to efficiently load goods into Unit Loaders (ULDs) and ensure their on-time delivery has become a key issue in logistics systems. Based on Cainiao's actual logistics challenges, this paper proposes a comprehensive loading planning scheme in two phases: the first phase solves how each item can be efficiently packed into ULDs in 3D space, and the second phase solves when each ULD can be loaded on multiple parallel workstations. This design-oriented approach fine-tunes and integrates existing optimization techniques into a cohesive pipeline to tackle these interconnected problems systematically.

To tackle the 3D Bin Packing Problem (3DBPP), two approaches, Mixed Integer Programming (MIP) and Extreme Point Heuristic (EPH), are used in this paper. The MIP model maximizes space utilization through accurate optimization and is suitable for small-scale packing scenarios, while the EPH algorithm performs well in large-scale scenarios and generates high-quality approximate solutions in a short period. Although its space utilization is slightly lower than that of MIP, its solution efficiency is well suited to real logistics operations that require fast response time.

For the Build-up Scheduling Problem (BSP), a parallel machine scheduling model is used to optimize the assembly sequence and timing of ULDs to ensure that all ULDs can be loaded within a strict time window. Experimental results show that the model performs well in optimizing workstation load balancing and avoiding delays, which can significantly improve the scheduling efficiency of the whole system.

The research results in this paper are validated to show that the proposed two-stage framework has significant application value in improving space utilization, reducing cargo delays, and optimizing workstation scheduling in real logistics scenarios of Cainiao. Future research can introduce a feedback loop between the two phases, and combine real-time data with dynamic adjustment strategies, hybrid algorithms, and other methods to further improve the adaptability and efficiency of the model.

This report, titled Expanding Puerto Rawson: Enhancing Fishery Capacity and Project Cargo Logistics, presents a conceptual masterplan for the eco-friendly expansion of the Port of Rawson. The port’s proximity to a region abundant in marine resources makes it an attractive location for expansion. However, the port is facing strategic expansion problems and it could fail to accommodate the rise in demand from the fishery industry. This report carefully maps out the gap between the expected rise in demand and the currently existing infrastructure. After that, two models are created to transform the rise in demand that is expected by 2030 & 2040, to nautical demands for the port, like number of berths and required waterway width. The results from these model in combination with a strategic overview of the landscape, were used to come up with three conceptual designs that eventually converged into one final conceptual design by conducting an MCA. This conceptual design, together with the port waste management plan, could enable an eco-friendly and future-ready expansion of the Port of Rawson.

This research paper focuses on improving the performance of cross-docking operations under uncertainty in the context of e-commerce logistics. The growth of e-commerce sales has increased product returns and complexity to supply chains. To address this issue, this study investigates how cross-docking operations can be improved under external and internal uncertainty factors. The research begins with a literature review to understand cross-docking facilities (CDFs) and measures to mitigate the effects of uncertainty. The current state of a CDF in a case study for a Fourth Party Logistics (4PL) provider is examined, and by reflecting on the literature overview, two potential means for decreasing the effects of uncertainty are identified: staging-level design and load carrier-type design.

A Discrete Event Simulation (DES) model is developed to test the effects of staging-level design and load carrier types on the performance of the CDF. The simulation model captures input factors such as truck arrivals, freight levels, and the purity level of cross-docking. The simulation model’s performance is tested for different scenarios, and the effects of different design alternatives are analyzed.

The results demonstrate that two-stage cross-docking with pallets can significantly reduce the total makespan and improve operational efficiency compared to single-stage cross-docking with pallets. The results also show that using roll containers significantly decreases the chance of intra-terminal congestion but also results in longer unloading and reloading times. The research contributes to the understanding of cross-docking operations under uncertainty, stresses the importance of staginglevel
and load carrier type design on CDF performance, and provides insights for logistics companies seeking to optimize their e-commerce supply chains.

The global ever growing energy demand, quest for renewable alternatives for fossil fuels and desire to reduce dependency on single countries has driven the increasing demand for offshore wind energy production. The advancing technologies that enable offshore wind turbines to gain efficiency go hand in hand with increasing sizes of components and foundations. Deeper waters can be entered, but the accompanied size and weight increase poses various challenges. Fixed-bottom structures such as monopiles reach diameters of 10m and lengths up to 110m, which complicates the onshore handling of those monopiles in marshalling ports. This study identified a gap in the existing literature regarding marshalling ports and their role in supporting offshore wind farm construction.

By applying a discrete event simulation (DES) to a case study regarding the construction of an offshore wind farm in the Baltic Sea, different scenarios have been evaluated and assessed in their resilience and performance in response to schedule changes. The findings highlight the importance of a compressed project schedule in achieving cost reductions. A strategy with approximately 75% overlap between load-in and load-out schedules was identified as the most cost-efficient approach. With this approach, cost savings are not only achieved by reducing operational expenses such as personnel and equipment rental, but most substantially by the decreased amount of demanded storage area spaces. With less storage spaces needed, both the construction costs for storage bunds and the area rental costs decrease. The analysis of the experiment on schedule overlap revealed that a scenario with only one support for load-out and zero supports for the load-in exhibited higher average waiting times and total maximum fines. However, this scenario still performed best in terms of total costs, as the waiting times for ships did not outweigh the expenses associated with additional supports. The study also examined the timing of arrivals and found that when a barge arrives the day after the installation vessel departs, the waiting time for unloading significantly decreases.

Collaboration among stakeholders is emphasized as a key recommendation stemming from the study. Involving all relevant actors in offshore wind projects from an early stage can yield extensive mutual benefits. By establishing an overarching supply chain management, coordinated by the project developer, overall construction costs can be reduced without harming any particular party.

The developed discrete event simulation might be applied to other projects to extend the research, under the requirement that the included assumptions are structurally evaluated. Investigating different project sizes, schedule variations and load-out methods could improve the overall understanding of the system dynamics and parameters. In combination with a discrete event simulation, a mathematical layout optimization might enable decision makers to make choices regarding the location and priority of placing wind turbine components in marshalling port, based on the installation variability. This could eventually lead to a decision-making tool suitable for cost-optimizing marshalling activities and installation strategies for wind farm constructions globally, contributing to the acceleration of the energy transition.

Inland Waterway Transport (IWT) is an untapped resource that can be mobilised to achieve a more sustainable transport system without compromising competitiveness. It outperforms rail and road alternatives in terms of low emissions, costs, high capacity, energy efficiency, freight safety, and security. Waterway locks are ageing assets in IWT systems and are infamous for creating bottlenecks. The effectiveness and performance of these locks can be measured and integrated into decision making to establish well-informed operational, maintenance, and renewal policies. This study addresses the following research question:How can the effectiveness of waterway locks be assessed to support lock maintenance and operation? Simulation modelling, which offers an efficient and low-risk evaluation of policy options while incorporating the intrinsic variability of the system, is selected as the core methodology. The simulation model incorporates operational aspects of the system, malfunctions, corrective maintenance activities, and the calculation of various performance indicators. An extensive list of performance indicators is complied through literature research. These indicators include infrastructure occupancy, vessel waiting times, costs, and emissions. In addition to existing indicators, three formulations for Overall Equipment Effectiveness (OEE) are proposed in lock complexes. The applicability of the selected methodology is demonstrated by employing a case study of the Volkerak complex, one of the largest and busiest lock complexes in Europe. Quantitative and qualitative data, collected through operational logs, maintenance reports, and interviews with experts, support that as the lock complex gets older, malfunctions become more frequent. SIVAK, a software package utilised by the Ministry of Infrastructure and Water Management of the Netherlands ("Rijkswaterstaat", RWS), is used as the basis of the simulation model. Extensions are made to calculate additional performance indicators and to simulate fluttering doors and slowdowns, two types of malfunctions that are diagnosed to be frequent and impactful based on maintenance reports and interviews. Experiments are designed to explore the performance of different maintenance policies, such as mean time to repair (MTTR) and inspection frequency, and different operational policies such as locking regimes under various fleet mix and lock condition scenarios. Stress tests and univariate analyses are also conducted. The study findings highlight the following:
- With rising demand, the significance of lock condition in maintaining acceptable service levels and minimising CO2 emissions becomes more evident. The findings indicate a trade-off between preventive and corrective maintenance efforts. In challenging lock conditions, faster repairs and more frequent inspections are needed to prevent capacity problems, leading to longer waiting times. Notable differences in handling capacity are observed in the three lock conditions studied.
- The concept of baseline OEE proves to be valuable as a maintenance-oriented metric. It emphasises that proficient maintenance strategies can counteract deficiencies in the lock system, resulting in improved capacity, reduced transit times, and reduced CO2 emissions. A general rule of thumb suggests that improving baseline OEE by one point corresponds to about a 1.2%-1.5% improvement in waiting times and emissions.
- Changing MTTR and inspection policies influences baseline OEE scores, but these adjustments must be aligned with the lock condition. Frequent inspections might yield unnecessary availability losses when the lock is well maintained. Similarly, the extent of benefits of shorter MTTRs depends on the frequency of breakdowns.
- A prominent dilemma in lock systems involves balancing transit times and the number of levellings. Locking regimes capture this trade-off, where reducing waiting time thresholds increases levellings and operational costs. However, some strategies can achieve an improvement in both aspects. These include expanding traffic range and considering the current state of the system when assigning lock chambers to incoming vessels.
- Service-based OEE, integrating operational and maintenance policies, aligns better with waiting times and CO2 emissions compared to the service level alone. This composite index can serve the purpose of monitoring waterway network lock systems, helping to identify losses due to unavailability and reduced speed.

E-commerce is rising in demand and sales are forecasted to reach 7.4 trillion dollars by 2025. Many products are shipped to consumers from warehouses directly rather than being displayed in a physical retail store. Smooth and effective warehousing operations are more crucial with the increased dependency on warehouses nowadays. More than 130 key performance indicators (KPIs) exist for warehouses. Often a summarized and meaningful metric is desired to give an accurate evaluation of the overall warehouse performance (OWP). The most common method of measuring OWP used in the literature is done using data envelopment analysis (DEA). However, it is difficult to find empirical evidence that DEA has significantly improved performance evaluation and benchmarking in actual non-production conditions. A new method for measuring OWP is explored using a widely accepted overall metric in the manufacturing industry, namely overall equipment effectiveness (OEE). The use of OEE as a performance measure for warehouses or in logistics generally has not been researched extensively yet. The research problem lies in evaluating the overall performance of warehouses using a modified OEE framework. Therefore, a challenge to identify the components of a modified OEE for warehousing was present. Additionally, a modified OEE formula for the automated warehouse was made based on the major losses that occur in such warehouse. The nature of the items processed at a warehouse and the warehouse utilization stage impacts the KPIs to be considered by a modified OEE model. A framework for a modified OEE for warehouses was developed using these inputs. It was agreed that one of the main uses of an OWP measure lies in reporting performance to different stakeholders, mainly the warehouse contractor. Additionally, using OEE can give insight to areas of improvement which gives a science-based ground for continuous improvement. The research was done using a single case study in the inbound part of an automated warehouse for online clothing retail with an industry leader. Expanding this research into other warehouse areas is suggested, and a final OWP using a modified OEE model may be achieved after numerous iterations and case studies. Further quantitative analysis of the performance of OEE as an OWP can validate the results more. It is argued that a universal indicator of some sort using OEE can be achieved in the future, this can then be used for benchmarking alike warehouses.

Despite advanced communication, monitoring, and control facilities, train operations are still subject to uncertainties that can disturb train services, cause delay to multiple trains, and propagate through the network. One option is to mitigate delay propagation in the timetable design by adding buffer time to the minimum difference between the time two successive train of either direction enter a section. It is still common practice to design buffer times based on a deterministic value, decreasing operational capacity and requiring large amount of manual checking by planners. Existing approaches to effectively allocate buffer time in timetables lack flexibility and require an initial timetable. In this paper, a data-driven approach for determination of buffer time planning rules suitable for usage in an initial timetable is presented. These planning rules are not necessarily generic, but rather depend on timetable characteristic. Two metrics that describe delay propagation, mean secondary delay and hindrance percentage, are extracted from literature and predicted in a regression analysis with the use of timetable characteristics related to headway situations of two succeeding trains. The results of the regression analysis on a case study of the Dutch railway network between Haarlem, Leiden Centraal and Schiphol Airport are used to determine the amount of scheduled buffer time that would ensure a certain amount of hindrance percentage given a specific headway situation. The results show that the mean secondary delay and hindrance percentage for various headway situations can both be predicted with an accuracy of 90.7\% based on timetable characteristics and is quite heterogeneous. Mean secondary delay appeared not significantly impacted by the scheduled buffer time, contrary to hindrance percentage which is significantly influenced by the scheduled buffer time.

A rapid increase in international trade volumes during recent years has led to truck congestion at container terminals’ gates during peak hours. This has consequences on the costs of trucking companies waiting long times behind the gates. Furthermore, it leads to containers arriving too late at the hinterland, a lower port attractiveness and high emissions of idling trucks. One possible policy to deal with this issue is to steer trucking companies towards night-time transport. However, this requires hubs with extended opening hours. The objective of this research is to investigate the implication of such policy further by evaluating and simulating the actors and their (inter)actions in the container transport system. This research uses a combination of qualitative (semi-structured interviews with actors) and quantitative (agent-based modelling, discrete event simulation, queueing theory and discrete choice modelling) methods to assess the impact of hubs with extended opening hours on freight transport under various scenarios. These scenarios compare possibilities for a chassis-exchange or a container hub and investigate the choice of trucking companies to use such hubs. Results determine that if a container hub would be introduced, other actors need to partly pay for trucking companies using the hub so that the hub capacity is fully used. This study reveals concrete recommendations on the type and location of the hubs to mitigate waiting times at terminals' gates and transport more containers per day. For example, a chassis-exchange hub can reduce the total waiting time at four container terminals by 420 hours per day. If trucking companies would invest in a chassis-exchange scenario, the profit is estimated to be around two to four thousand euros per day.

In practice manufacturing companies have insufficient coordination and synchronisation between production planning and warehousing teams. The lack of coordination regarding production and warehousing decisions in the ordering strategy may result in unforeseen capacity issues at the warehouse due to misalignment between production plans and warehouse availability. These unforeseen issues can result in additional costs due to production line closure or last-minute stock reallocations. For this reason, it is relevant to study the effects of increasing interdepartmental coordination between production and warehousing departments on product flows between factories and warehouses in the supply chain of a manufacturing company. This thesis’ aim is to analyse the effects of coordinating the production scheduling department with the warehouse management department, against the most common practical case where the two entities hardly communicate. To this end, two optimization models are developed representing the warehouse and factory operations within a supply chain. The first model consists of a multi-period multiproduct lot size and warehouse optimization model for the decision on warehouse capacities while minimizing warehousing costs. The second model is a parallel non-identical machine scheduling model for the optimization of production schedules and the simultaneous minimization of the production costs. We compare the outcomes of the two models against a model where both decisions are integrated. The modelling decisions are taken in a setting with deterministic demand over time. An extensive sensitivity analysis aims to provide managerial guidelines for practitioners, to weigh which department can have greater impact on the total cost. Also, the integrated model can quantify the benefits of coordinating the production and warehouse departments. A case study at Kraft Heinz, a popular brand in the food industry, is used to provide a practical setting.

Adapting the size of upending cradles according to the ever-increasing dimensions of offshore wind turbine monopiles is costly and requires structural modifications to the associated Heavy Lift Vessels (HLVs). This study therefore investigates the potential of the Monopile Upending Smart Tool (MUST), which is a platform suspended in the HLV crane, on which a winch is installed. Two grommets of constant length connect the platform with two trunnions attached to the monopile, and the winch cable suspends the bottom of this structure. Unwinding the winch cable allows for in-crane upending of the monopile. This way, the dependency on the size of the available cradle is circumvented. The focus is laid on determining the workability of the application of the MUST in the crane of the Seaway Strashnov and identifying limitations in its design. Moreover, the potential of systems that increase the workability or reduce the limitations is investigated.

The installation of monopiles using the MUST comprises three phases: the barge mooring / lift-off, upending / slewing and lowering / driving phase. Based on the results from a qualitative critical event analysis, the lift-off phase is expected to be limiting. Hence, a hydrodynamic model is developed to quantitatively analyse this phase. Experimental simulations, which are performed in parallel with the model development, result in the preliminary conclusions that multiple pendulum effects influence the results to a limited extent, while viscous roll damping and hydrodynamic interaction effects can be strong determinants of the resulting responses.

The relative z-motion between the lifted monopile and the barge and the barge roll response are identified as governing parameters. To reduce the first limiting factor, a system that allows for instantaneously increasing the vertical clearance between the monopile and the barge is proposed. The effectiveness of this system is tested for two barge loadcases. For the first case, the average workability increase for the optimal heading at a typical location is calculated as 8.3%. For the second, the increase is marginal, as the barge responses are more limiting. Furthermore, is it found that a Passive Motion Compensator (PMC) can reduce the probability of the introduction of snap loads in the winch cable, and therefore allows for system optimisations. A PMC with 10% of critical damping can reduce the required winch capacity with a factor of 2.3 w.r.t. the uncompensated case.

It is recommended to perform follow-up studies into the system performance during upending / slewing and lowering / driving. Also, it is advised to evaluate the effect of a larger barge and a PMC on the workability. To balance the associated investments and workability increases, the logistical models developed in a parallel study can be used. Finally, for iterative calculations, it was found to be beneficial to make an estimate based on a fast simplified model and to subsequently feed the results back into a more detailed, but slower model.

The ever-increasing size of offshore wind turbine substructures and the development of wind farms at sites further offshore, with greater water depths and with extremer weather conditions, raise logistical challenges that have never been faced before. Additionally, the offshore wind industry has to deal with governments cutting subsidies, small profit margins and limited practice guidelines, while it is expected to lower the associated levelised cost of energy to a competitive level in the market. Scientific studies have identified room for optimisation in the substructure (the focus is laid on Monopiles (MPs) with Transition Pieces (TPs) and pre-piled jackets) transportation and installation phases. However, no studies that evaluate the performance of strategies for these phases are identified. Hence, the objective of this study is to “generate insights into the complex system of interdependent strategies for the installation of offshore wind turbine substructures, and to identify and quantify cost-reduction opportunities.” The considered strategies are formed by combinations of transportation and installation strategies, which differentiate based on the number and type of the deployed vessels and the sequence in which the operations are performed.

To quantitatively compare the strategies, and to consider stochastic processes (e.g., weather conditions), a discrete-event simulation modelling approach is adopted. To arrive at substantiated conclusions, a framework is followed, which provides a roadmap and rigour criteria for the design, implementation and evaluation phases. First, a conceptual model is developed and face validated. Next, a numerical “base model” is constructed, which describes the most basic strategy. This model is face validated by industry experts and evaluated by parameter variability, convergence and historical data validation tests. It is concluded that the base model is structured according to shared practical experiences, responds satisfactory to parameter changes, requires 35 simulation runs to converge, and has good predictive capabilities. Hence, it is deemed suitable to function as a “template” for the modelling of the other strategies.

The simulation results are evaluated for each of the considered substructures separately. (i) MP – TP installation. In general, assembly-line installation strategies, in which two Heavy Lift Vessels (HLVs) are deployed, are associated with the shortest installation time. The shuttling – assembly-line and the shuttling–alternating (in which MPs and TPs are installed alternatingly) strategies are associated with the lowest costs. Both involve a shuttling transportation strategy, in which the HLV(s) ensure(s) both the transportation and installation of the components. The mooring of barges alongside an HLV in feeder strategies (feeder vessels supply components to an HLV, which stays at the wind farm under development) and the installation of TPs by a relatively small HLV in assembly-line strategies are identified as the main bottlenecks. Reducing these by relatively simple solutions can result in significant performance increases. Lastly, the project start date is found to be a strong determinant of strategy performance. (ii) Jacket – foundation pile installation. The assembly-line strategies are found to result in the shortest jacket installation times as well. However, only the shuttling – assembly-line strategy is additionally associated with the lowest costs. Furthermore, it is found that a separate pile-dredging vessel can help to reduce the time and costs associated with separate phases installation strategies, in which jackets and their foundation piles are installed in different phases. Also for jackets, the barge mooring alongside the HLV is identified to be the largest bottleneck. Reducing this bottleneck can result in significant performance benefits. Lastly, a relationship is found between the performance of jacket installation strategies and the project start date, although weaker than for MP installation.

The developed decision support tool can provide a platform for further research into the logistics of offshore wind and other industries, whereas the obtained results are only valid within the set boundaries. To widen the applicability, it is recommended to perform follow-up studies in which a stochastic mechanical failure component is included, and the sensitivity to the wind farm size and port-to-farm distance is tested. Furthermore, it is advised to extend this study to investigate the potential of the industry adopting a more holistic process or market point of view.

Emission-free zones are being instituted in cities in the Netherlands to combat the emissions caused by transportation activity causing operators of large commercial vehicle fleets to electrify their vehicle fleet. E-truck fleets need sufficient charging infrastructure and power capacity at the distribution centres they operate from. However, due to the prevalence of grid congestion causes, the capacity of actors’ connection to the power grid is be constrained and solutions behind the electricity meter are necessary to either increase local supply or reduce momentary demand. The main research question is: “What is the most cost-effective electric infrastructural composition and operation to facilitate the electrification of heavy truck fleets of large-scale distribution centres in grid congested areas in various scenarios to ensure that the route schedules can be driven?” This thesis proposes a mathematical mixed-integer linear programming (MILP) model which simultaneously optimizes the composition and operation of the electric infrastructure of a distribution centre, while considering the periodic energy balance, for various scenarios in the electrification process. This has not been done earlier in the literature for distribution centre-like systems, while no similar use case has been studied in the Netherlands or its general region. Before the model was applied to the real-world case, it was verified using a recalculable numerical test and validated using linear regression analysis. In the model validation the K-means method was used to determine a typical day per month, to comprise the simulation period to 288 time periods of an hour. The proposed mathematical model was tested by means of a case study and various scenarios for the distribution centre of a supermarket company to analyse the models’ behaviour in a practical setting. The results show that from a greedy charging subscenario to a variable electricity prices subscenario, the model is able to decrease the yearly total costs and the number of infrastructure components required. The model is able to reduce the yearly total cost by up to 19.4% in the final scenario. The infrastructure investment steps in the electrification process to take, would be to install consecutively or collectively 1, 3 and 7 charging stations and 994, 1623 and 4340 PV panels in 2022, 2025 and 2030, leading to a PV installation with a total rated power of 1.562 MW, which proved to be the more cost-effective than wind turbines and battery storage. The results of the scenarios, the graphs and the sensitivity analysis show that the model is effectively able to optimize the infrastructural composition and operation to minimize the cost in all cases, giving managerial insights into the decisions that need to be made in all steps of a fleet electrification process.

Maritime logistics is important for the international volume of trade. Ports are critical in cargo transhipment and provide services to vessels like traffic management, piloting, towage and mooring, which together are called the Nautical Chain since these services are dependent on each other. Due to the importance of maritime logistics, it is important that the supply chain functions resiliently and thus ports function resiliently. For ports to function resiliently, the Nautical Chain should function resiliently. Currently there is no knowledge on the resilience of the Nautical Chain, which is the gap this research addresses. The aim of this research is to gain insight in the current state of resilience of the Nautical Chain, and gain insight in the effect different mitigation strategies have on improving this resilience. A literature review is performed and a discrete event simulation model is built to gain insight in the resilience of the Nautical Chain. The results show that the largest improvement to the resilience of the Nautical Chain can be achieved by a simultaneous increase in pilots and tugs.

Climate change is triggering among others a larger demand for offshore wind energy. This leads to new developments of which larger next-generation Wind Turbine Generators is the most relevant. These next-generation WTGs create problems for the carrying capacity of current-day installation jack-up vessels that work according to the conventional installation method (shuttling). These installation vessels can carry less or even nil of these WTGs per trip compared to the current-day WTGs. Fewer turbines per trip would allow the larger current-day installation vessels to maintain their work. However, they have more sailing time since more trips are required. This could also be more inefficient since the Offshore Wind Farms are moving further offshore. The other option within the conventional method is to build larger installation vessels that would carry more WTGs per trip.

Another installation method in the literature called feedering is a potential substitute for the conventional method. Very limited research has been done for feedering as well the practical implementation whereas the conventional method has extensive research and is mostly used in practice. The models in the literature compared two different feeder methods, called, the base port feeder and feeder-ship method, to the conventional method. The models are generic and lack different strategies to find the best feeder solution. The feeder-ship method has the most potential to solve the aforementioned problems and is therefore further investigated to find the best installation strategy, being either the conventional or feeder method.

First of all, the feeder-ship method is evaluated based on practical knowledge and adapted so a base port is used to temporarily store all components that come from the production port. The feeder sails back and forth between the base port and the installation site to supply the installation vessel with WTG components. The installation vessel will stay at the installation site to be as efficient as possible (the least amount of sailing time). This research only looks at the feeder and installation side and leaves the port elements out of the problem. Feeder strategies (within this method) differ in the number of feeder vessels/types and transfer options using either barges or Platform Support Vessels and being indirect (transfer components onto the installation vessel) or direct (install components from the feeder). The installation vessel for feedering is also looked into as being either a current-day or a special feeder purpose installation vessel. Different carrying capacities of the installation vessels are used to create different strategies for the conventional method.

A stochastic Discrete Event Simulation model is created and used in this research to evaluate the strategies. The output of the DES simulation is the project duration per sailing distance and strategy. The costs for this duration is calculated in a costs model from the perspective of the contractor as well as the developer. Besides duration and costs (based on the European market), the strategies are also evaluated on emissions (fuel consumption) since this is an increasingly important element in the industry. A sensitivity analysis has been performed to get a better understanding of which critical parameters the feeder strategies are most sensitive to. The analysis is also used to find the best next-generation WTG installation strategy in Europe which is the main focus of this thesis.

In this thesis, the optimization of inventory management within RTI pooler networks is considered. The inventory management by RTI poolers consists of the network configurations on which the network relies, as well as the relocation schedule of RTI poolers that determines the functionality of the system.