Fast-moving consumer goods (FMCG) firms compete through innovation, but the portfolios that result grow faster than they are pruned.

The literature on SKU rationalisation is extensive, yet it consistently presupposes that the data required to evaluate a delisting candidate is available in analysable form. This paper reports a design science case study in the European personal care division of a large multinational FMCG manufacturer, where that assumption does not hold. The division already received periodic, criteria-based delisting recommendations from a central analytics team, but could not readily act on them: the recommendations contain only a code and its financial figures, and the evidence needed to interpret them was dispersed across more than ten independently maintained brand files and successive, unconnected snapshots of the commercial reporting system.

A decision-support framework was designed, built, and evaluated in response. Two automated pipelines consolidate the fragmented sources, one of which recovers a multi-period view by stacking snapshots that individually contain none, and four dashboards present the resulting evidence for portfolio exploration and for the evaluation of individual candidates. Applied to three recommended products, the framework confirmed two for delisting and surfaced the strategic grounds on which a third, financially indistinguishable, was retained.

The confirmed delistings raise the gross margin of the brands concerned by and percentage points and remove stock-keeping units across country-market combinations, at a cost of € million turnover. The artefacts are in use within the division. The central finding is that the binding constraint on data-driven rationalisation was not the sophistication of the analysis, which already existed, but the accessibility of the evidence required to act on it. ... Read more

Airports are electrifying Ground Support Equipment (GSE) fleets to reduce local emissions, but electric GSE changes both fleet-capacity and operational-demand planning. This paper investigates how operational requirements, uncertainty, and charging affect the required fleet capacity and operational demand of electric Ground Support Equipment (eGSE) during airport turnaround operations. A rule-based discrete-event simulation represents individual vehicles, service time windows, airport travel, battery state, finite charging infrastructure, and service-specific resources. The focused case study considers two operationally distinct vehicle types at KLM Ground Services at Amsterdam Airport Schiphol: depot-based toilet vehicles and stand-side loaders. The model is extensively verified and validated for fitness for purpose using flight and task data, airport distances, track-and-trace movement data, measured energy consumption, state-of-charge and charging observations, waste and dumping logic, historical arrival deviations, and expert review. Water vehicles were also implemented and validated in the underlying study but are omitted from the detailed paper results to avoid repetition. Under combined task-timing and travel-time uncertainty, the strict fleet requirements are 15 toilet vehicles and 25 loaders. Pragmatic lower bounds of 9 toilet vehicles and 23 loaders achieve 99.90% and 99.93% mean on-time completion, respectively, but retain residual service-window risk. Operational-control experiments show that idle forward staging reduces the strict stochastic toilet requirement from 15 to 10 vehicles and substantially reduces deadheading. Feasibility-aware task triage reduces the number of late tasks under scarcity, but increases the lateness of tasks that are deprioritised. Charging is not the binding cause of lateness in the tested reference configurations, although it materially affects charging sessions, charger occupancy, peak use, and infrastructure demand. The scientific contribution is therefore a decision-support method that estimates fleet capacity and diagnoses how demand timing, positioning, dispatching, uncertainty, and charging resources shape operational performance. ... Read more

Airports are increasingly electrifying Ground Support Equipment (GSE) fleets to reduce local emissions and support decarbonisation targets. Although electric Ground Support Equipment (eGSE) are well suited to many airside operations, electrification changes the fleet-sizing problem. Vehicle availability is no longer determined only by task duration, location, and travel time, but also by battery state, charging duration, charger access, and operational charging rules. These constraints are especially relevant during aircraft turnaround operations, where multiple time-critical ground-handling tasks must be completed within narrow service windows.

This thesis investigates how operational requirements, uncertainty, and charging affect the required fleet capacity and operational demand of eGSE in airport turnaround processes. A structured review of the GSE and eGSE modelling literature shows that existing studies provide important building blocks for routing, scheduling, energy management, and charging analysis. However, the joint integration of task execution, individual vehicle availability, charging behaviour, infrastructure constraints, and operational uncertainty remains limited. In particular, many models either simplify charging and vehicle-level states, treat fleet size as a fixed input, or do not represent uncertainty. This motivates the development of a simulation-based decision-support approach in which eGSE fleet sizing is evaluated as a dynamic vehicle-availability problem.

A rule-based Discrete Event Simulation (DES) framework is developed to represent daily eGSE operations. The model includes individual service tasks, vehicle-specific states, airport location groups, travel times, battery state of charge, charging sessions, charger capacity, refilling, dumping, depot-return behaviour, and service-specific operating rules. Vehicles are assigned to tasks based on task urgency, time-window feasibility, travel time, battery state, and operational resource constraints. Task-timing uncertainty and travel-time uncertainty are included to evaluate how stochastic operational variability affects service-window performance and fleet-size requirements.

The framework is applied in a case study at KLM Ground Services (KLMGS) at Apron Services (AAS). The validated case-study scope includes three vehicle groups: water vehicles, toilet vehicles, and loaders. These groups represent both depot-based resource-constrained operations and stand-side service operations. The model is verified using synthetic test cases and validated through operational data checks, expert judgement, service-demand validation, energy and charging behaviour, water-demand consistency, and uncertainty-representation checks. Simulation experiments are then used to assess baseline performance, deterministic fleet sizing, task-timing and travel-time uncertainty, combined uncertainty, demand-case robustness, idle forward staging, feasibility-aware task selection, and sensitivity to operational and charging parameters.

The results show that required fleet capacity depends strongly on the service-level interpretation. Under deterministic operating conditions, the smallest fleet sizes that achieve 100% on-time task completion are 12 water vehicles, 11 toilet vehicles, and 23 loaders. Under combined task-timing and travel-time uncertainty, the strict robust thresholds increase to 16 water vehicles, 15 toilet vehicles, and 25 loaders. These are the smallest tested configurations that achieve 100% on-time task completion across all 30 stochastic replications. However, if a very small number of short internal service-window violations is operationally acceptable, lower fleet sizes may also be defensible. Under this pragmatic interpretation, at least 11 water vehicles, 9 toilet vehicles, and 23 loaders achieve ≥99.90% on-time task completion. These results should be interpreted carefully, because the performance metric measures completion within internal service windows and does not directly measure aircraft departure delay.

The experiments also show that operational-control assumptions can materially affect fleet-size outcomes. Idle forward staging, where idle vehicles remain near the aircraft stand instead of returning immediately to the depot, reduces unnecessary deadheading and improves service performance for selected vehicle groups. For toilet vehicles, the deterministic strict requirement decreases from 11 to 9 vehicles. Under combined uncertainty, idle forward staging reduces the strict robust threshold from 16 to 14 vehicles for water vehicles and from 15 to 10 vehicles for toilet vehicles. For loaders, the fleet-size threshold remains unchanged, but driven distance and depot-return movements are substantially reduced. A feasibility-aware task-selection variant further shows that dispatching logic can reduce the number of late tasks under scarce-fleet conditions, although it may increase the lateness severity of tasks that are already infeasible.

The sensitivity analysis indicates that charging is not the main driver of late task completion within the tested configurations. Energy-related parameters mainly affect charging sessions, charger occupancy, and charging-infrastructure utilisation. Task punctuality is more strongly constrained by vehicle availability during demand peaks, travel-time assumptions, service-time assumptions, and operational positioning logic. Charging therefore remains important for infrastructure planning and vehicle availability, but it is not the dominant bottleneck in the tested case-study settings.

Overall, the study shows that explicitly simulating operational requirements, uncertainty, and charging changes the interpretation of eGSE fleet sizing from a static vehicle-count problem into a dynamic availability problem. Required fleet capacity depends not only on the number of tasks, but also on when and where vehicles are needed, how uncertainty clusters demand, how quickly vehicles can recover between tasks, and how charging and supporting infrastructure affect vehicle availability. A simulation-based approach therefore provides a useful decision-support method for assessing eGSE fleet capacity, operational robustness, and charging-related resource use in airport ground handling. The reported fleet sizes should be interpreted as operational fleet-capacity requirements under the tested service-level assumptions. Final implementation decisions should add a technical reserve for maintenance, failures, battery degradation, charger unavailability, and other sources of vehicle downtime. ... Read more

Modern maritime terminals face increasing challenges such as volatile cargo volumes,fluctuating demand and congestion due to the evolving market requirements.To remain competitive under these changing conditions, terminals need to become more flexible and adaptable.One approach to improve the terminal flexibility is to accommodate multiple cargo types within a shared operational environment, resulting in mixed-cargo terminals.However literature shows that existing storage allocation and operational strategies have primarily been developed for single-cargo terminals and are not sufficient to address the operational conflicts arising from integrating multiple cargo types with different storage, handling and operational requirements in one terminal yard.To address this research gap, this study proposes a novel demand-driven reconfigurable storage framework that dynamically reallocates cargo between storage areas within a mixed-cargo terminal.This study uses container and breakbulk cargo as the two types of cargo that share the terminal storage yard.The proposed framework aims to improve terminal operational efficiency of mixed-cargo terminals through improving storage flexibility and resource utilisation under fluctuating demand conditions.

For this research, a hybrid simulation model combining Agent Based Modelling(ABM) and Discrete Event Simulation(DES) is developed in AnyLogic to evaluate the proposed reconfigurable framework against a conventional static storage strategy.The framework was assessed using multiple demand scenarios; normal demand, container high and breakbulk high demand and analysed using several key performance indicators such as queue length, storage utilisation,cargo dwell time and throughput.The study demonstrates that the proposed framework improves operational efficiency of the terminal under increased demand conditions by reducing congestion, improving storage utilisation, enhancing cargo flow and improving throughput of the terminal.The findings showcase that the demand-driven reconfigurable storage framework offers a practical and adaptable approach for improving the operational performance of mixed-cargo terminals. ... Read more

The gap between sustainability reporting and capital allocation in capital-intensive, project-based industries is commonly diagnosed as a measurement problem. This paper argues that, for yard-level investment decisions in custom superyacht shipbuilding, it is instead a decision-logic problem. Reporting standards produce backward-looking information for external accountability. Investment choice instead requires forward-looking deliberation under deep uncertainty about regulation, legitimacy, and client expectations. Existing valuation, indicator-based, and multi-criteria approaches each address part of this problem but none is sufficient alone. The paper develops a modular non-probabilistic framework with four components. A European Sustainability Reporting Standards-grounded indicator basis and a System Dynamics Representation handle indirect and feedback-mediated consequences. AHP-weighted Multi-Attribute Value Theory aggregates non-monetised value, and minimax regret across bounded scenarios supports cross-context comparison. Monte Carlo perturbation of elicited inputs tests framework robustness. The framework is applied to four investments at a custom superyacht shipyard, selected to span scale, impact pathway, and scenario sensitivity. The application demonstrates three results. First, baseline-attractive and robustness-attractive investments diverge. A large strategic investment wins under additive aggregation but carries the highest maximum regret, while a small governance investment minimises regret across scenarios. Second, the System Dynamics Representation surfaces legitimacy and governance pathways that direct expert assessment systematically overlooks. Third, sensitivity concentrates in the consequence layer rather than the valuation layer, locating productive disagreement in empirical rather than normative questions. The framework does not eliminate uncertainty. It disciplines deliberation by making assumptions, trade-offs, and points of disagreement explicit and contestable.
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This thesis examines the integration of carbon-reducing technologies and distribution decisions within the cement industry's supply chain, with a focus on the impact of these decisions on emerging carbon pricing mechanisms, particularly the European Union's Carbon Border Adjustment Mechanism (CBAM). As the cement industry is a major contributor to global carbon emissions, there is significant pressure to reduce its environmental footprint. Despite the potential of carbon capture technology, its adoption within the cement industry has been slow and remains limited. Moreover, decision‐support tools to guide these decarbonisation decisions remain scarce. This thesis develops and validates an integrated framework that combines long-term demand forecasting, strategic investment in carbon capture technologies, and distribution planning under evolving carbon-pricing regimes.


First, an extensive data collection process assembles detailed information on global cement production facilities, bilateral trade flows, and regional emission factors, providing a robust empirical foundation for the analysis. A country-level consumption series for 1995--2023 is then reconstructed by combining historical trade flows with production capacity data, applying outlier detection and interpolation techniques to ensure trend consistency. Building on this foundation, a systematic forecasting exercise produces reliable country-level demand -- which is thereafter distributed over the three biggest populated cities in the country -- projections for 2025--2050, which are spatially disaggregated to define demand nodes for the optimisation model.


The core of the methodology is a Mixed-Integer Linear Programming (MILP) model that simultaneously optimises binary investment decisions in carbon capture retrofits and continuous cement flows across a 25-year planning horizon. The model’s objective function maximises profit by balancing expected revenues against production, transportation, emissions-related costs, storage and transportation costs for captured carbon, and capital expenditures for retrofit investments.


To evaluate model robustness and the influence of future policy environments, the study conducts comprehensive parameter sensitivity and scenario analyses. Sensitivity analysis systematically perturbs key input parameters -- such as production and transportation costs -- to identify which uncertainties most affect optimal investment and distribution strategies. Scenario analysis contrasts three carbon pricing pathways (STEPS, APS, and NZE), each evaluated with and without the CBAM, to investigate how different policy trajectories shape investment decisions and trade flows.


Results show that the CBAM consistently accelerates retrofitting investments and reshapes international cement trade flows across the STEPS and NZE carbon pricing pathways. Under more aggressive carbon pricing scenarios (APS and NZE), domestic European retrofit projects become viable even without the CBAM, although the mechanism continues to redirect marginal investments toward lower-cost regions. Sensitivity tests reveal that assumptions about regional production costs exert the strongest influence on investment outcomes, while variations in transport-related costs have comparatively limited effects.


This thesis makes three key contributions. Methodologically, it provides a validated, end-to-end decision-support framework that integrates comprehensive data preparation, long-term demand forecasting, mathematical optimisation, and detailed post-analysis. Empirically, it offers novel insights into how the CBAM implementation and alternative carbon pricing trajectories jointly determine the geography of carbon capture investments and global cement flows. Practically, it delivers a strategic tool enabling industry stakeholders and policymakers to plan effective long-term decarbonisation strategies under uncertainty, highlighting the importance of targeted support measures in higher-cost regions and the need to align regulatory designs with the economic realities of global supply chains.
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Our study focuses on employee assignment for component inspections at a majorDutch airline, where externally repaired components and consumables require inspection before release into stock. The problem features limited capacity, heterogeneous skills, component-workstation compatibility, and strict priority ordering. We formulate a binary integer program as a variant of Multiple Subset Sum Problem with Assignment Restrictions (MSSP-AR) extending the general formulation with with a strict priority constraint. Because strict priorities can block capacity, we introduce a phased procedure that iteratively re-optimizes after removing subsequent components in blocked categories, aligning with the logic of the IT system and improving utilization of employees. To keepmulti-run evaluation feasible under stochastic inspection times, we truncate the tail of priority level 3 components, which preserves optimal employee assignment solutions while reducing runtime substantially. Validation against operational data shows the average gap to the 90% target reduced from 65 percentage points to 15.3 percentage points under the model. On weekdays the effect is the highest as the average gap reduces from 57.9 percentage points to 2.4 percentage points. The approach delivers implementable employee assignment solutions under constraints applicable to the operation and provides a basis for future work on worker productivity heterogeneity, adaptive truncation, multi-day planning, tactical and strategic models, and broader applicability inMRO/logistics settings. ... Read more

When a vessel experiences an unexpected failure, the required spare parts are often not available onboard and must be delivered from shore. The current maritime spare parts supply chain depends on extensive logistics networks and large inventories. Although this system allows parts to eventually reach the vessel, it also creates several challenges. Deliveries can take considerable time, extending downtime and disrupting operations. International transport adds significant costs and emissions, while large inventories tie up capital and increase the chance that parts will become obsolete before being used.

Additive manufacturing, also known as three dimensional printing, offers a different approach by producing parts closer to where they are needed. Instead of shipping components across long distances, the design of a part can be sent digitally and printed onboard or at a nearby facility. Polymer based printing technologies have already proven reliable in maritime environments, showing that non critical parts can be produced effectively. Earlier studies have suggested that this approach could reduce costs, inventories, and transport emissions. However, most of this research focuses on individual cases and highlights specific advantages rather than comparing overall performance.

This thesis introduces a scenario based framework to examine how additive manufacturing affects logistics performance in the maritime spare parts supply chain. The study compares the current supply chain with three possible setups for additive manufacturing: onboard vessels, at ports, and in supplier warehouses. A polymer valve seat was chosen as the reference component because it represents a realistic, non critical part that can be printed with compact equipment. The analysis includes three delivery routes, Rotterdam to Hamburg, Singapore, and Dampier representing short, medium, and long distances. Each scenario is assessed using six indicators: lead time, logistics cost, inventory holding cost, spare parts availability, carbon emissions, and the investment required for additive manufacturing.

The results show that the impact of additive manufacturing depends on the location of production and the operational situation. Printing onboard brings the largest improvements, particularly for remote or long distance operations where waiting for deliveries would cause long downtime. Port based printing performs well on medium and long routes, while warehouse based printing mainly improves availability and stock management.

Overall, additive manufacturing can strengthen maritime spare parts logistics when applied thoughtfully and in a targeted way. It should be introduced gradually as a complementary capability that supports more resilient and sustainable operations. ... Read more

This thesis develops a conceptual design methodology for hydroponic systems tailored to specialty crops where biological requirements are often incomplete or uncertain. The proposed methodology adapts established mechanical engineering design principles (Pahl & Beitz, Roozenburg & Eekels, TRIZ) by introducing iterative feedback loops, explicit decision points, and the parallel integration of biological, technical, and economic analyses. A critical innovation is the inclusion of a dedicated Testing phase between the Conceptual and Provisional design phases. The methodology was applied to a case study on Russian dandelions, a potential alternative source of natural rubber that grows in its roots. The case study successfully structured the complex design problem, generated multiple cultivation concepts, and systematically exposed critical knowledge gaps regarding root rubber content, single- or multiple harvesting techniques, and cultivation strategies. Experimental trials confirmed the feasibility of hydroponic cultivation but revealed significant biological challenges, such as plant stress from root trimming. Economic modeling, based on current assumptions, indicated a lack of viability, highlighting a dependency on future agronomic research. The thesis contributes to both literature and practice by bridging engineering methodologies with controlled-environment agriculture, expanding the scope of hydroponics beyond food crops, and offering a design-support guideline for future innovation in non-traditional crop systems. ... Read more

The exponential growth in maritime trade during the 21st century has posed notable challenges for container terminals, resulting in congestion and operational inefficiencies. This study delves into the efficacy of Amphibious Automated Guided Vehicles (AGVs) as an innovative remedy to tackle these issues and facilitate the shift towards autonomous operations at container terminals. Motivated by the need to optimize spatial utilization and reduce reliance on conventional material handling equipment in port areas, this study employs an agent-based modeling approach. Subse- quently, the formulated model is applied in a case study focusing on major Ports of the World. This study undertakes a critical examination of the potential of Amphibious AGVs in alleviating congestion and operational challenges faced by container terminals in the context of an increasingly interconnected global trade setting. By conducting a thorough examination of existing literature and creating a generalised simulation model, this study aims to offer valuable insights that can influence the evolution of container terminal operations. ... Read more

Offshore wind farms are increasing in size and moving further from shore. Service operation vessels (SOVs) are used for offshore wind operation and maintenance (O&M) at these large far offshore sites. These large vessels typically have a smaller daughter craft (DC) on board that can assist them. This DC is however too small to provide the seakeeping capabilities needed at most far offshore sites, causing it to become essentially unusable. Previous studies at Siemens Gamesa Renewable Energy (SGRE) have looked into improving the capabilities of the DC while considering the constraints of the SOV, this was deemed insufficiently possible by Brans (2021). The second study looked at increasing the size of the DC, which saw significant improvements (Kamerbeek, 2022).

The work of Kamerbeek (2022) however raises new questions such as what is the optimum number of these larger DCs? Would another type of craft serve as a better mothership or DC? Is having the mothership perform maintenance the most efficient? SGRE is therefore interested in exploring mother-daughter concepts to perform offshore wind farm O&M activities at large far offshore wind farms, to see if these can outperform the status quo. A mothership is the home of the technicians offshore and the daughters are the craft that bring the technicians from the mothership to and from the turbines. The main research question is therefore:

What method can best be used to explore the design-space of mother-daughter
concepts for offshore wind farm O&M?

This research first focuses on understanding offshore wind farm O&M and finding the most important restrictions and challenges that need to be taken into account within a model. This has been done through a literature review and discussions with experts from SGRE. The work then focuses on selecting a modeling method and explaining the proposed method. This method is validated using a comparison with a real-life wind farm. A case study is done at the end using a dummy wind farm to demonstrate the workings of the method. The method uses a discrete-event simulation that simulates the transport of technicians to and from the turbines to estimate the performance of the concepts. Any wind farm, turbine failure rates, or fleet can be inserted into the model for analysis to ensure a wide range of applications. The performance of the fleets is assessed based on the estimated downtime/availability and emission estimates that the model produces. The financial and technical feasibility should be evaluated in the next stage when a selection of promising solutions has been made based on this first logistical analysis of the fleets. The visits are planned within the model based on the weather conditions, number of available technicians, craft availability, and the evacuation requirement.

The design space of mother-daughter concepts should be explored by running the model using the exploratory set of fleet configurations and inputting various wind farm layouts with varying realistic visit agendas and weather conditions. The output of each of these cases should then be analyzed by dividing all the fleet configurations into groups based on the craft each fleet contains. This grouping allows the performance of each type of fleet to be compared to one another, while the performance difference within the groups shows the effects of different transfer limits. The analysis should then focus on identifying cross-over points between different configurations and on selecting specific fleets based on performance and expected configuration cost.
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The European Union has recently revealed a plan called ”Fit for 55.” The plan aims to reduce carbon dioxide emissions by 55% before 2030. It includes FuelEU Maritime, which will introduce new emission regulations for ships over 5000 gross tonnages. The regulations will require shipowners to reduce their carbon footprint. The European Union has decided not to wait for the International Maritime Organisation’s emission rules (IMO) and will enforce them for ships by 2024. By 2025, the rules will extend to offshore vessels with a gross tonnage of 400 or more and general cargo vessels carrying commercial goods between 400 and 5000 gross tonnages.
Efforts are being made to find a hydrogen carrier that closely resembles conventional oil-based products to comply with these regulations. All these fuels need to be produced with renewable energy sources, which have their efficiency losses. Renewable fuel production is only estimated to have a chemical efficiency of 50%.
Innovation in the shipping sector is necessary to reduce energy losses. The shipping sector fits the rules of rural society, where incremental innovations are preferred over radical changes. However, radical change is necessary to accomplish the energy transition in shipping. According to the DOI theory, innovators are the first group of adopters. Innovators are eager to try new ideas and have a cosmopolitan (global) network. These innovators will play a critical role in the energy transition in the shipping sector.
The study’s objective is to analyse if it is possible to influence a given adoption of the innovator. First, the research outlines the theoretical framework for the study. The literature search aims to determine a transition framework to answer the research sub-questions. The framework’s scope will be refined to the innovator group and the maritime sector. A case study will be conducted to test the defined framework, and factors outside the scope may be included if needed. The literature collection approach involves determining the philosophical framework before researching the sociological framework. The mainstream innovation and inclusive innovation frameworks have been identified from a philosophical perspective. The mainstream innovation framework focusing on radical and technological typology is more appropriate for the research study. Rogers’s sociological framework can be used to describe the adoption process. The Scopus search has been used to identify different theories, including spatial innovation frameworks, sectoral innovation systems (SIS), technological innovation systems (TIS), and path development. Finally, the study provides an overview of the innovation systems and their corresponding frameworks…… ... Read more

The importance of natural environments with rugged deformable terrain (marshes, mangroves, rainforests, coastlines) from biodiversity, carbon capture, and coastal protection to economic livelihood is significant. However, the current systems available for robots to explore those ecosystems are either large, expensive and intrusive, not application focused or consist of custom components that are difficult to integrate with existing robotic mechanisms. This thesis proposes a novel soft adaptable wheel suited for such ecosystems. The mechanism operates as a fluidic elastomer actuator constructed with Dragon Skin 30 able to change its form depending on the task at hand. Various designs were simulated in MuJoCo to test its ability to overcome rigid obstacles and terrain forms (from ramps, steps, smooth undulating terrain and terraced undulating terrain). The design's structural feasibility was then tested and further improved using Ansys with the final result having a loading capacity of 1.25N (at 0kPa) and a maximum blocked force of 1.98N (at 35kPa). Finally, the wheel was tested at 3 distinct operating configurations (neutral, partial and fully inflated) in 4 types of deformable terrain (non-compressible dry, compressible dry, non-compressible sticky, compressible sticky) using EDEM Altair. This evaluated its performance and validated the need for different forms according to the rheological properties of the terrain. The design proposed in this paper is intended to be more application-focused, with its simplicity facilitating integration into robotic systems, using off-the-shelf components, and ultimately reducing the time required to be used in environmental applications. ... Read more

Transport and storage operations in a steel coil manufacturing industry make up a large part of a factory’s operating costs. With the advent of automated forklifts, unmanned transport and storage operations are becoming increasingly viable. Automated forklifts have the potential to lower operating costs, reduce the required number of employees, minimize transport damages and eliminate over-processing. Nonetheless, transitioning from manual forklifts to a fully operational automated transport and storage system requires addressing a range of complex decisions. These include the flow path layout, fleet sizing, vehicle dispatching, storage location assignment, and other relevant subjects.

This thesis presents an integrated approach to how the flow path layout design influences the material flow effectiveness, incorporating vehicle scheduling and storage location assignment policies. Based on a real-world case study, analyzed through Discrete-Event Simulation (DES) software, this study addresses the following research question: What in-plant system design facilitates effective material flow by implementing automated transport in a steel coil manufacturing plant?

First, applicable literature is investigated, thereafter, the system is analysed, and design alternatives are generated. The design alternatives vary in the use of manual and automated forklifts, and the flow path layout considers both conventional and zone-based flow approaches. The experiments test the influence of dispatching policies and fleet sizing on all alternatives. Furthermore, battery management, idle-vehicle positioning, unit-load selection and case-specific system constraints are integrated. The storage location assignment is based on the order identification number and the fill level of the storage parks.

By capturing the dynamic and variable nature of a stochastic production system, the DES evaluates the impact of different configurations on performance, costs, and other performance indicators. The cost-performance relations are plotted, resulting in a Pareto front consisting of a set of non-dominated system design configurations. A preferred automation alternative is selected from this set. Conclusions regarding the most effective transport system design and the integrated system design process are drawn.
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This paper focuses on the effect of different storage policies on the performance of RoboticMobile Fulfilment Systems (RMFSs). The research is conducted under the instructions of the Technical University of Delft and CEVA Logistics the Hague. The aim of the research is to develop a decision support tool that can aid companies in the choice of the storage policy to use for their RMFS. RMFSs have multiple different levels of decision problems that need to be solved. The performance of a RMFS highly depends on the algorithms that are applied to solve these decision problems. This research focuses only on the storage policies of a RMFS. In this case storage policy refers to the decision in which pod items should be stored and where on the storage area the pod should be positioned. In order to gain a good understanding of the effect of such a policy on the overall performance of a RMFS, experiments should be performed. Physical experiments are however very hard and costly to perform. This research therefore makes use of a simulation study to test different storage policies in different scenarios. The simulation model used is an adaptation on an agent-based semi-open queuing network framework model by Merschformann et al. (2018a). In the experiments four different storage policies are investigated under three different storage layouts. The results of the simulations are analysed via the throughput, the pile-on and the distance travelled during the simulation. After this a score is given to
both the picking as well as the replenishment side of the system. It is important to investigate both sides of the system since the flaws on one side can negatively affect the other side. The scores of the replenishment and picking processes are then averaged to gain a final score which indicates the overall performance of the
policies. Finally a fifth policy has been developed where each pod can contain multiple different sized compartments. Unfortunately testing and verification of this policy was not possible in the given time frame. For this reason it has been left out of the experiments. ... Read more

It is becoming increasingly clear that the effects of climate change should be decreased or even mitigated. Green alternative sources of energy are being explored, and wind energy emerges as an important option that can be exploited on a large scale. Wind turbines are placed more and more often offshore due to larger and more stable wind resources. The most common foundations for these turbines are monopiles. The future outlook for these turbines and their foundations is that they will become bigger. An important design characteristic of monopiles is the natural frequency of the pile. In order to keep dynamic effects to a minimum, excitation frequencies should not coincide with the natural frequency. The primary source of excitation of monopiles are bending moments. Therefore, this thesis will take a closer look at the dynamic bending capability of monopiles.

There is a lack of knowledge on dynamic effects of bending moments on steel cylindrical shells (monopiles). While the dynamic axial buckling capacity of cylindrical shells has been researched extensively, there is little known on the dynamic bending buckling capacity of such shells. This thesis investigates the influence of loading rate on the dynamic buckling capacity of monopiles subjected to bending moments. For this, a finite element model is constructed which is validated by analytical models. Later on, the finite element model is used to conduct a parametric study to investigate the effect of loading rate on buckling capacity.

The scope of this study excludes factors such as soil dynamics, fluid structure interactions, residual stresses, and lateral forces. The focus is solely on the cylindrical shell of monopiles, excluding secondary steel from consideration. Initial geometric imperfections are considered as local perturbations necessary to initiate buckling, while other factors that may affect lateral forces or overall structural capacity are excluded.

This study will show that different parameters play part in the dynamic bending buckling behavior of cylindrical shells. The natural frequency of the cylinder, as well as the non-dimensional length together with the yield stress of the material play an important role in the dynamic buckling capacity. This research concludes that cylindrical shells with higher natural periods are more influenced by dynamic bending moments than cylinders with shorter periods. Next, shorter, stocky cylinders exhibit higher dynamic buckling capacities than slender cylinders. Also, imperfections are found to decrease the buckling capacity of cylindrical shells, but this effect diminishes for increasing loading rates.

Keywords: Offshore wind energy; Cylindrical shells; Dynamic buckling; Loading rate; Imperfections; FEM; ... Read more

This study entails the development of a planning model utilizing Centralized Model Predictive Control (CMPC) to optimize the flow of physical goods throughout a network of supply chain nodes, utilizing a Mixed-Integer Linear Programming (MILP) approach to determine the optimal decision variables. Specifically, a Current State CMPC model was created to reflect the current outbound logistic network at Heineken Zoeterwoude, where information asymmetries are known to impact the accuracy of the outbound logistic planning tool. The Current State model was compared against a Future State model, where real-time data is available, thereby eliminating the aforementioned information asymmetries. By assessing four key performance indicators, it was found that the Future State model enables considerably better performance of the logistic network, even during peak production. ... Read more