This study demonstrates that unloading performance at dry bulk terminals can be effectively assessed using open data sources such as AIS and aerial imagery. By structuring terminal operations into measurable performance indicators and applying the Overall Equipment Effectiveness (OEE) framework, we show that productivity, utilization, and occupancy can be quantified and benchmarked across terminals. The method is validated using data from two dry bulk terminals, revealing consistent patterns and highlighting operational differences. This approach enables scalable, low-cost performance analysis without requiring direct access to proprietary terminal data.

The decarbonization of heavy-duty road freight transport requires overcoming the dual challenge of limited driving range and insufficient charging infrastructure for battery-electric trucks (BETs). This thesis develops a nationwide bi-level optimization framework that integrates the deployment of static charging stations (SCS), electrified road systems (ERS), and truck-level battery assignment, leveraging large-scale tour-based freight data from the Netherlands. By operating explicitly at the tour level rather than the trip level, the framework captures cumulative energy feasibility across multiple linked trips, thereby providing a more realistic representation of freight operations. The model is solved using a Genetic Algorithm (GA), capable of evaluating more than 1.5 million tours and 3.5 million trips, and validated against exact MILP solutions on small instances. The results show that the optimization raises feasibility from 58% to 89.9%, reducing infeasible tours to 10.1%, while overall fitness improves by 34.7%. ERS emerges as the backbone of electrification, with 12,792 km deployed (€25.7 billion, 65.6% of CAPEX), while 251 SCS facilities (€50.2 million, <1% of CAPEX) provide low-cost redundancy at regional hubs. Battery allocation is highly heterogeneous: 25% of trucks operate on 90 kWh, 40% on 600 kWh, and the remainder on intermediate sizes, yielding an average of 357 kWh—closely aligned with ElaadNL’s benchmark of 289.5 kWh/day. This heterogeneity reduces battery CAPEX by approximately 19% compared to a uniform-capacity baseline. Operating expenditures (OPEX) are dominated by ERS charging, while penalty costs for infeasible tours remain substantial, averaging €362 per unserved tour. These findings demonstrate that nationwide electrification is feasible under a layered strategy: ERS as the long-haul backbone, SCS as regional redundancy, and heterogeneous batteries as cost optimizers. The study advances the literature by moving from trip-level to tour-level modelling at unprecedented scale, explicitly quantifying infeasibility, and decomposing system costs into CAPEX, OPEX, and penalties. The results provide actionable insights for policymakers and industry stakeholders seeking cost-effective and operationally viable pathways for freight decarbonization in the Netherlands and beyond.

Over the past decade, consumer shopping habits have increasingly shifted towards online grocery purchases, creating a growing demand for efficient and scalable warehouse operations. As order volumes and complexity rise, the optimization of warehouse processes has become essential to meet requirements while controlling operational costs. This study addresses the Joint Order Batching Picker Routing Problem (JOBPRP) by developing an exact optimization approach and examining the interdependencies among key warehouse processes, such as batching, routing, and product allocation. Using a case study of Crisp B.V., an online grocery retailer, the proposed algorithm was implemented to optimize configurations across multiple temperature-controlled zones with varying operational characteristics, such as differing pick densities and operational constraints. The research shows that all warehouse processes are interconnected and the best performing configuration is depending on the operational characteristics of the warehouse. The optimization approach achieved a 39.14% reduction in weekly travel distance compared to Crisp’s current benchmark, highlighting its potential to significantly enhance travel distances. These findings highlight the significant impact of integrating order batching and picker routing on warehouse efficiency. The study not only demonstrates the critical correlation between warehouse processes but also provides actionable insights for optimizing order picking in high-density, large-scale warehouses

The adoption of new technologies on pipelaying vessels is closely linked to the demand for full automation of pipe handling operations. However, pipe handling systems are rarely addressed in research, and, to the best of our knowledge, no studies exist that describe comparable intralogistics systems with similar control structures and movement restrictions. As a result, there is no clear reference for how such automation can be realized. This study addresses this gap by developing a conceptual pipe routing system for the pipelaying vessel Solitaire, operated by Allseas, and implements it within a Discrete Event Simulation model of the pipe handling system. The model is used to evaluate multiple routing strategies under different pipe supply scenarios and to determine the most effective routing strategy for the case study. The results show that a heuristic approach to sequential decision-making, framed within a model-free Markov Decision Process, can automate pipelaying operations and lay the groundwork for future optimization through reinforcement learning.

Current geopolitical tensions mean that NATO countries must now make greater efforts to deter threats to their territories, having neglected this defence task for years. In case of a major conflict, brigades and their vehicles are ordered to immediately travel to the area of conflict. This vehicle deployment is researched and modelled in a Multi-Integer Linear Programme (MILP), with the objective to minimise the makespan of the deployment, e.g. the arrival time of the last convoy. In this deployment the strategic and operational movements are modelled, meaning the starting point is the Point of Embarkation (POE) and the end point is the Staging Area (SA) or Concentration Area (CA). The model is suited for deployments that use a double modal network, being road and rail transport. Two scenarios are established, distinguishable by their transport networks and vehicle types, and they are subjected to configurations in which parameters are varied. The research is conducted in cooperation with the Royal Netherlands Army (RNLA), and serves as a handle to obtain understanding on deployments when varying certain parameters. It is observed that the usage of the rail mode mainly should be motivated by vehicle suitability, rather than makespan oriented reasoning, as it hardly improves the makespan of the deployment. Furthermore, platooning is a promising technology to implement in the deployment, with the ability to decrease the makespan by at least 11%. The influence of traffic congestion halfway the deployment is highlighted, encompassing an increase in makespan of at least 7-15%. It is found that the convoy amount and the amount of vehicles does not influence the makespan by a lot (pm1.9%). Lastly, the difference between the transport networks of the two scenarios is exposed with the use of a robustness examination, where the makespan of the first scenario improves substantially more (11.7%) than the makespan of the second scenario (0.4%). This research can be expanded in several directions, including the incorporation of additional modalities and a more comprehensive investigation into the robustness of transport networks within these deployments.

Autonomous terminal tractors (ATTs) are a current development as a driver-less alternative to terminal tractors (TTs). This paper focuses on the integration of these ATTs in a rubbertired gantry (RTG)-based container terminal, and more specifically looking at how strategies for ATT-only intersections could influence the productivity of the terminal. For this study, a discrete-event simulation model of an ATT, ATT-controller and intersection management system (IMS) were designed. Within the IMS, four intersection strategies were developed: One-Vehicle-at-aTime First-Eligible-First-Serve (One-FEFS), Multiple-Vehicles-at-a-Time First-Eligible-First-Serve (Multi-FEFS), Quay-Crane Destination Priority (QC-Prio) and Priority for Delayed ATTs with QC Destination (Delay-Prio). These intersection strategies were tested in a peak-load and off-peak-load scenario, on a model of an RTG-based terminal, with configurations of varying fleet sizes. Here was found that the intersection strategies had a small influence on the QC productivity in the peak-load scenario, and a negligible influence in the off-peak-load scenario. Utilising either the Multi-FEFS or the QC-Prio strategy leaded to the highest terminal performance. In the simulation model, the ATT performed significantly worse than the existing non-autonomous TT, in every configuration.

With fluctuating market demands, the tugboat industry confronts the challenge of adapting its supply chain to meet customer customisation needs while managing low order predictability. This study examines the shift from a Make-to-Stock (MTS) to three alternative production configurations based on the Assemble-to-Order (ATO) and Make-to-Order (MTO) configuration, focusing on the tugboat industry’s need for flexibility in response to market changes and customer-specific requirements.

The core issue addressed is the trade-off between investment risk and customer satisfaction, as the proposed configurations increase delivery lead times. To quantify the costs associated with adapting delivery lead times, a Bill of Materials and Operations (BOMO) is utilised, combining the Bill of Materials (BOM) with the production sequence (Bill of Operations, BOO).

A mathematical algorithm is developed to calculate the financial effects of the configurations based on BOMO data. The model involves a three-step process: importing part data, merging BOM, BOO, supplier, and transport data into a BOMO dataset, and performing value analysis on the BOMO data to quantify risk exposure and financing costs over time.

The study’s findings indicate that while increasing delivery lead times, the MTO configuration significantly reduces the risk exposure and financing costs. The BOMO is a strategic tool for analysing material costs and delivery lead times, providing insights into the financial implications of different production strategies. The research concludes that the MTO configuration is viable for Damen’s tugboat production, balancing risk exposure, financing costs, and delivery lead times.

This research investigates several concepts for better powder handling at the infant formula factory of Nestlé in Nunspeet. By exploring the current state of the powder handling, the problem has been clearly defined. Several concepts have been developed using the analytic hierarchy process. A literature review has been done in order to determine how the concepts can be modelled in order to compare them. The literature review highlights that one of the concepts can be modelled as a new version of the Bin Packing Problem (BPP). The concepts are then modelled and compared by using KPIs. The research shows that storing one of the powders in silos and the other powders in a rented warehouse is the best solution for Nestlé.

In their Distribution Centre (DC) in Maasdam, FrieslandCampina (FC) uses a four-crew shift schedule to prepare all necessary orders for their clients, 24 hours of each Monday to Saturday. Their large automated warehouse is home to 10 000 pallet places, containing fresh dairy products. From here, orders are either prepared as full pallets, machine-picked layers or hand-picked "colli". In the last department especially, personnel cost is high relative to the throughput. Definitive picking deadlines are often ambiguous, posing challenges in job and personnel scheduling. The study goal is twofold. Firstly, to find out whether full knowledge of picking deadlines can contribute to a more efficient job, and so, shift schedule. Secondly, to offer insight for a trade-off between shift types to absorb workload. To reach this study goal, a Shift Minimisation Personnel Task Scheduling Problem (Krishnamoorthy et. al., 2012) and a Bin Packing Problem (Paquay et. al., 2014) were combined and tailored to fit the scheduling problem at FC's DC. In three weekly scenarios, the MILP model scheduled picking jobs in the least expensive shifts through a cost minimisation function. Two model configurations were used, one to prefer the shift between 09:00 and 17:00 (flex), and one to prefer either one of the 06:00-14:00 (morning) or the 14:00-22:00 (afternoon) shifts. Both model configurations inherently avoided the most expensive 22:00-06:00 (night) shift. Main findings include the possibility to absorb workload using the morning and afternoon shift and to avoid the night shift. Additionally, it was confirmed that insight in picking deadlines can contribute to an efficient personnel schedule a great deal.

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 paper investigates how two sequential sub-processes at a flower auction can be well aligned to efficiently execute the overall auction process. Existing literature mainly focuses on warehouses without perishable goods and warehouses where all orders are known before the outbound process is started. However, at a flower auction, the gathering of goods and distribution takes place while the auction is still ongoing. In addition, flowers are vulnerable goods that must be handled with care.
During a case study at Royal FloraHolland Naaldwijk, the current process of order picking and in-house delivery is investigated to find the main strengths and bottlenecks. This is done physically and with data. From this analysis,
it has been found that the main issues are the spread and share of waiting times in the in-house delivery process and the output of the order-picking process that is too low. To improve the overall process based on the found issues, a calculation model has been built in Python to test possible improvements. It has been found that implementing limited waiting times and other alterations to increase efficiency results in a more reliable and better predictable
process that can be executed with approximately the same number of work hours or slightly more than in the current situation.

Trade fairs bring together suppliers from specific industries or fields, offering them a valuable platform to showcase their products, gather information on competitors, and find potential partners. During construction, materials that are not yet in use are often stored in pathways. When multiple stands are under construction simultaneously, pathways can get increasingly congested, resulting in stands becoming inaccessible, safety risks and delays. Despite the importance of managing these issues, no scheduling method has been developed to address these challenges to date. To fill this gap, this research introduces a Mixed Integer Linear Programming (MILP) model designed to improve the construction scheduling process at trade fairs by incorporating the predicted impact of scheduled workspace availability on delays as a factor in scheduling decisions. A case study at RAI Amsterdam was performed to collect data and validate the model. The improved schedules proposed by the MILP model are estimated to reduce average aisle material storage by 3.1%, variance in material storage density by 38.9%, and workspace interferences by 18.0%. When accessibility constraints were relaxed, even greater gains were achieved, with reductions up to 10.7% in material storage and 64.6% in interferences. Simulation runs with varying input variables showed that the degree improvement varied by hall layout, stand density, and available construction time, with island layouts and additional setup time yielding the best results. The most recurring and clear scheduling strategy applied by the model was letting stands furthest away
from the accessible safety paths start first, followed sequentially by closer stands. Overall, the model provides a practical scheduling approach to reduce congestion and enhance safety, offering a useful tool for trade fair organizers.

The Automated Guided Vehicle (AGV) is the most commonly used piece of horizontal movement that uses electric power from an internal battery pack. With much focus on the charging strategy, current charge operation models lack the ability to model large scale terminal operations and to incorporate new types of horizontal movement. The objective of this research is to show the impact of recharge operations on terminal performances. A discrete event simulation model is based on the characteristics of APMT MVII to model the current impact. The results of numerical experiments show charging operations have a limited impact on the productivity of Lift-AGV. An exception is the change in battery type. Using a Li-ion battery lowers the need for recharging and increases total productivity. The findings show the importance of integrating modelling to accurately project terminal operations.

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.

Long truck queues and congestion around terminals is a common sight, however they come with many negative externalities for all the stakeholders involved. Truck Appointment System (TAS) is the most commonly used system to face these problems, but it still has some drawbacks and limitations. Consequently, in this research an extension of the typical TAS is proposed in order to improve its performance. The main components of this system are the use of truck dependent time-windows, the utilization of real-time information and the adaptive trucks rescheduling model. The duration of the arrival time-windows is longer than the actual service times, allowing overlap between time-windows. Thus, the actual service sequence might be different from the reserved one. To determine the actual loading sequence an Optimization model is developed, which is run periodically while utilizing real-time truck information. A chemical plant is used as a case study in this research. The performance of the proposed TAS is assessed with the use of a Simulation model. The outcomes of this research suggest that the a less strict TAS can significantly improve the system’s performance, especially trucks’ waiting times. Also, the system’s resilience against disruptions and the plant’s environmental footprint are improved, while queues are reduced.

This research addresses the critical role of the Yard Utilization Rate (YUR) in the performance of container terminals, focusing on Rubber-Tyred Gantry Crane (RTG) container terminals. Existing literature reveals a gap in studies specifically targeting high YURs in RTG container terminals. The primary research question is, ”How to reduce Quay Crane (QC) productivity loss in RTG container terminals with highly utilized storage yards?” A Root Cause Analysis (RCA) identifies key factors affecting the loading and unloading processes of QCs, with a focus on the root causes "Too few TTs deployed", "Congestion", and "YUR too high". Design alternatives are proposed to address these root causes, and three design alternatives are tested through simulation: varying the number of Terminal Trucks (TTs), sacrificing driving lanes for additional storage space, and implementing an alternative shuffle policy with a different bay distribution. Results show a significant influence of the number of TTs on QC productivity, with an optimum at 8 TTs per QC. Sacrificing driving lanes for extra storage space proves counterproductive due to increased congestion. However, the alternative shuffle policy Multi-bay with Sets Shuffle Policy with a Clustered Bay Distribution shows promise, offering a potential solution to mitigate QC productivity loss in highly utilized RTG container terminals, with an average increase of 0.4 boxes per hour. In conclusion, the study successfully addresses the identified knowledge gap, providing valuable insights and proposing a practical solution for reducing QC productivity loss in RTG terminals with highly utilized storage yards. The suggested approach of Clustered Bay Distribution with a Multi-bay with Sets Shuffle Policy proves effective in maintaining terminal performance during periods of an increased YUR.

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 worlds awareness of our emissions and the effect it has on our surroundings is increasing. This influences efforts to put new agreements into place to reduce emissions and the effects on global warming. Along with the Paris agreements, the International Maritime Organisation also came with measures to achieve these needed reductions of emissions for the maritime industry. The set goal for 2050 is a reduction of 50% of greenhouse gas emissions. To achieve this new fuels need to be used and research need to be done on these fuels.

In this thesis the workings of a liquid bulk terminal are analysed, with the use of ammonia as fuel for ships in the maritime industry. The current liquid bulk terminals need to change with the future increase in demand and use of ammonia. This thesis focusses on how this can take place. The transport and bunkering methods of ammonia need to be researched and used to formulate a model to investigate the various options.

It is opted to develop a Discrete Event Simulation model of a liquid bulk terminal. The bunkering operations and supply of ammonia within a terminal are modelled. Various bunkering and supply methods are modelled for different sizes of terminal and at various levels of supply and demand.

The model is implemented using python and a Discrete Event Simulation package Salabim. With the models simulations it is investigated how bunker and supply methods at the terminal perform at different levels of scale. This is to gain insights for the future transition to the less polluting bunker fuel ammonia. The model is verified and validated with the use of data from literature on similar terminals using LNG.

The simulation results show that using pipeline bunkering and supply options are the most suitable. This option gives the shortest time that a ship has to spend on average at the terminal. The use of pipeline bunkering is therefore more efficient and this increases with the increasing scale of size, supply and demand of ammonia at a liquid bulk terminal.

The costs of all the facilities needed for the different sizes of terminal and the various scenarios of methods are analysed. The costs show that for smaller scales, bunker vessels and train supply, are more cost efficient. For large scale supply, demand and terminal size, using pipeline supply and bunkering is the most cost efficient.

Recommendations for future research includes the use of hybrid terminals using multiple bunkering methods at the same time. Investigating the safety risks for each method of bunkering and supply. And to use the proposed model for a case study at a location to investigate the suitability of the bunkering and supply methods for such locations.

This study aims to investigate the effectiveness of a Truck Appointment System (TAS) in improving dock utilization and overall operational efficiency at Royal Floraholland Aalsmeer. The study focuses on the impact of TAS on dock turnaround times, truck wait times, and maximum throughput time, as well as its implications on cost. The research explores how a TAS could work, including the scheduling of appointments and truck assignments, and the effects of more distributed arrivals. The study analyzes the performance indicators; truck waiting time, maximum time between flower check-in time and storage, parking wait times, cost, and examines the impact of assignment systems on these indicators. To be able to evaluate these performances a Discrete Event Simulation model is made, and different scenarios are analyzed. The study shows that requiring reservations has a significant impact on spreading out the pattern of arrivals, with gains seen at about 70% reservation percentages. The biggest improvements are observed at the throughput times of trolleys to cooled storage, with maximum throughput times decreasing up to 18%. The results suggest that a TAS can improve overall operational efficiency and decrease variability in maximum and average throughput time, while not lowering dock times significantly.

Air transport has gone from a once in a lifetime experience for the extremely rich to a day to day method of transportation for everybody. With the increase in passenger movements the amount of baggage that goes through airports has increased as well. This could cause problems for airports if they have a limited capacity in one of the parts of the baggage journey. An important part is transfer baggage, if something goes wrong the transfer bag misses their connecting flight which is an expensive problems for the airlines and airports.

To cope with the increase in demand of the capacity there are several solutions. The solution that will be explored in this paper is to make a more efficient use of the capacity that is available by making a prioritization in transfer baggage based on transfer time. The process in called cold buffering and the aim is flatten the infeed peaks. In order to explore this potential solution the baggage journey was explored and part of journey was modelled in a simulation. From the model it followed that it is possible to obtain 28.4% of peak shaving by using the cold buffering.