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.

The adaptation of battery-powered terminal trucks (BTTs) in rubber tired gantry crane terminals (RTG) will affect terminal productivity and costs. This research tests the performance of different charging strategies and compares costs. This is done with the use of a discrete-event simulation model including the energy consumption and charging of BTTs. Five charging strategies are presented: Out of Operations (OOP), Centralised Fixed Threshold (CFT), Decentralised Fixed Threshold (DFT), Decentralised Pre-Emptive (DPE) and Decentralised Opportunity Charging (DOP). The scenarios were tested in a terminal operations model of an RTG terminal, with configurations varying in charge lane location and numbers. Based on productivityOOP and DOP performed similarly to a diesel-benchmark of the same fleet size. It was found that charge location has little effect on overall terminal performance when the charge duration is large. Pre-Emptive charging has a positive effect on terminal productivity through high charge lane utilisation and little time loss associated with waiting on available chargers. Due to lower energy and maintenance costs, all battery-powered alternatives are more cost-effective than diesel-powered alternatives.

Abstract :A model has been developed for the health assessment of a chlorine compressor that is capable of constructing Health Indicators (HI). This model is an LSTM Autoencoder, which works as follows: an LSTM-based encoder maps a multivariate input sequence to a fixed-dimensional vector representation. The decoder, another LSTM network, uses this vector representation to produce the target sequence. The loss between the reconstructed sequence and the input sequence forms the basis for the HI. The model is trained exclusively with healthy data. The LSTM-AE is designed to reconstruct the measurements independently of the operating conditions, enhancing the model’s robustness in varying operational contexts. Health assessment is conducted by using the constructed HI to classify the health state of the compressor. The first instance of the compressor being marked as unhealthy triggers the start of fault identification. The proposed method is tested on real-world data through a case study on chlorine compressors, resulting in an identification accuracy of 73% and a precision of 56% for the three considered failure mechanisms.

Accurate forecasting in Reverse Supply Chain (RSC) management is crucial for the semiconductor industry, particularly for companies like ASML, which must efficiently manage the return flow of defective machinery parts. This study addresses key gaps by developing and evaluating time series-based forecasting models tailored to ASML's RSC. Using a modified ABC-analysis, parts were categorized based on defect frequency and economic impact, focusing on the most critical components. The research applied and optimized models including SES, ARIMA, ARIMAX, and LSTM, using five years of historical defect data. The analysis showed that LSTM models excel in high-frequency (weekly) forecasts for parts with frequent early-life defects, achieving an average mMAPE of 26.32\%. ARIMAX models performed best for lower-frequency (monthly) data, particularly in sparsely represented classifications, with mMAPE as low as 4.31\% to 10.80\%. Despite a higher mMAPE of 85.42\% in one outlier, ARIMAX emerged as the most balanced model, offering a practical trade-off between accuracy and computational efficiency. Furthermore, the study highlights the computational efficiency of ARIMAX, which, although more demanding than SES, provided a favorable balance, with ARIMA and LSTM being more resource-intensive. These findings demonstrate ARIMAX's suitability for long-term forecasting and broader trend analysis, making it the preferred model for ASML's RSC. This research provides a robust, data-driven framework that enhances inventory management and capacity planning, making it possible to predict return flows with low error rates on a monthly basis, particularly in high-tech industries where many defective parts are returned. Future research should incorporate additional dynamic variables, explore hybrid models, and refine data splitting techniques to further improve predictive accuracy and support sustainable supply chain operations.

This thesis presents a COLREG-compliant collision avoidance and detection algorithm for the safe navigation of autonomous surface vessels. The method builds upon the Velocity Obstacle algorithm and implements the concept of the ship domain and the ship arena into the framework. The International Regulations for Preventing Collision at Sea – the COLREGs – are analyzed individually and incorporated in the designed collision avoidance algorithm. The COLREGs were used to determine the preferred passing side and optimized motion for every possible encounter situation with an obstacle vessel. The ship domain and arena help to perform proactive alterations when required, improving on the apparent shortcomings of the VO algorithm. This framework leads to a more proactive collision avoidance algorithm that guarantees COLREG compliance and safe navigation in mixed-traffic environments. The robustness and wide adaptability of the developed algorithm are validated in different encounter situations, ranging from vessel-to-vessel batch simulations to complex, multi-vessel scenarios.

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.

Purpose - With the short term need to reduce direct CO2 emissions of trucks in distribution processes, this paper aims to provide an easy to implement solution approach for distribution processes of new cars from holistic perspective. Scarce emphasizes is provided on short-term alternatives in truck-based distribution processes and approaches lacks applicable for large scale problems including split delivery function.
Design/methodology/approach - The distribution process and model methods are analyzed using a literature study and interviews with experts, resulting in the development of a solution approach. Combined with an extensive field research, a solution approach enables the performance evaluation of the current state, and the policy implications. Future designs are used to validate the solution approach by calculating performance differences in multiple relevant evaluation domains.
Findings - The analysis of the current state has identified critical bottlenecks, leading to the development of two promising policies. The application of a new and validated prioritization strategy and permitting more stops per truck has successfully yielded a significant reduction in CO2 emissions. The performance of the solution approach demonstrates high precision on a small scale and yields results comparable to actual practices on a larger scale, suggesting the approach's effectiveness and potential for future application.
Research limitations/implications - This research provides a new solution approach for evaluating direct CO2 emissions of model different designs of distribution processes. Despite its narrow scope, the transportation sector has a significant environmental footprint, and offers the potential for substantial reductions in emissions. From modeling perspective, further research is suggested in integrating split delivery function without using dummy variables.
Originality/value - This paper contributes by identifying critical gaps in the understanding and implementation of system-wide efficient car distribution processes from distribution hubs to car dealers. It not only addresses potential improvements, but also proved efficiency gains of the system with a new solution approach, using a new combination of a state-of-the-art meta-heuristic and a proven split delivery method applicable for large-scale problems.

Facing the critical challenge of reducing greenhouse gas (GHG) emissions in the maritime industry, this thesis explores the potential of smart control systems using Reinforcement Learning (RL) for autonomous sailing. Traditional controls for sailing fall short in navigating the complex, dynamic conditions of maritime environments. RL has shown to be effective for continuous control applications in these types of conditions, however, primarily in simulated environments. Therefore, this study aims to show the potential of RL for autonomous sailing control (ASC) by means of a small scale project. A fast-time simulation of an Optimist is used to train the sailing controls required to reach an upwind target. The controls are then transferred to a robotized Optimist in a real-world environment to test the transferability of the simulation trained controls. First, the reality gap, or modelling error, between the simulation and real-world environment is quantified to be able to assess the performance of the used techniques to bridge the existing gap. The sim-to-real techniques of Domain Randomization (DR) and the addition of observation noise (ON) are applied during the training process. To test the effectiveness of the trained RL controls, the best performing ones in the simulation are selected and tested in the real-world environment. The performance of the RL controlled Optimist is compared to state-of-the-art controls in robotic sailing. Their performances are measured and compared by means of success rate and a physics-based metric that calculates the efficiency of the sailboat to use the power of the wind to propel itself, called the energy ratio. The results show that the RL controls are highly successful in the sailing simulation, however, the transfer to the real-world remains a major challenge. DR does improve the sim-to-real transfer, resulting in an agent that is able to reach a 100% success rate throughout 12 runs in the real-world environment.

This research investigates the application of a digital twin in managing challenges of variability and complexity within systems by using a case study at KLM Engineering & Maintenance. Using the DMADV (define, measure, analyse, design and verify) methodology, the research evaluates the problem using a literature review, measures and analyses the current state of the Logistic Handling Area (LHA), designs a digital twin concept and verifies its value. The literature review highlights the variability in the Maintenance, Repair and Overhaul (MRO) industry, which enables the investigation of digital twin applications for dynamic coordination. The analysis phase reveals significant operational challenges arising from variability in inflows and processing times, enhanced by system complexity and integral coordination issues between departments. To address these challenges, a digital twin is designed that enables real-time monitoring of KPIs, testing of dynamic resource allocation and integral operational target setting. The value assessment shows that the digital twin can support process operators by managing variability through continuous monitoring and evaluation of resource allocation, ultimately achieving predictable and stable system performance in a complex and variable environment.

To support the deployment of future offshore facilities aimed at achieving net-zero greenhouse gas emissions by 2050, offshore logistics operators must adjust their fleet management and routing strategies to accommodate diverse facility types. Existing methods for determining optimal vessel fleet size and composition are primarily tailored to offshore wind farm activities, lacking integration across multiple offshore facility types. To address this gap, we propose a Mixed-Integer Linear Programming (MILP) approach to optimize vessel fleet configuration, leveraging traditional solution methods such as branch-and-cut with commercial solvers. This model presents a mixed fleet of crew transfer vessels, service operation vessels, and heavy lift vessels to meet the specific demands of future offshore operations. With a focus on hydrogen power plants and carbon capture and storage platforms in addition to offshore wind farms, it demonstrates how a strategically configured fleet can efficiently support the diverse activities involved in integrating these new facilities.

Wind energy is growing to be an essential part of the transition towards sustainable energy sources. To facilitate this, it is crucial that wind farm operators can offer competitive energy prices compared to fossil fuel sources; effective and efficient wind farm operations are therefore imperative. However, many full-scale wind farms deal with wake effects, e.g. the disturbed air that travels downstream of a turbine and potentially ends up in other neighbouring turbines. The result of this lower-velocity, higher-turbulence flow field is both decreased power production and increased fatigue loads. One proposed solution for this problem comes in the form of 'wake steering': the yawing (rotating) of upstream turbines' nacelles to facilitate a degree of control over the deflection of wakes. By doing so, the problematic disturbed flow fields can be strategically guided between downstream turbines to maximise collective power production. However, the yawing action itself brings some adverse effects: the upstream turbines, now no longer directly facing the wind, feel decreased power production and increased fatigue loads themselves. These fatigue effects, accumulating over time, can eventually cause increased maintenance costs and nullification of any revenue gains through power optimisation. The problem thus becomes a farm-wide collective revenue optimisation task. This thesis investigates how long-term revenue in wind farms can be maximised, considering both profits through power optimisation and maintenance costs through fatigue-induced component failures due to wake steering control. First, a realistic wind farm simulation environment is constructed, based on a Graph Neural Network (GNN) surrogate wind farm simulation model, to facilitate efficient reinforcement learning training. Next, the environment is used to train fully centralised reinforcement learning agents based on a GNN architecture, resulting in agents that can generalise across all wind conditions and unseen wind farm layouts. Ultimately, the results show that an 'informed' agent that considers all profits and costs involved manages to significantly reduce the cost of energy compared to 'greedy' (power optimisation only), 'risk-averse' (damage minimisation only) and 'baseline' (zero-yaw) policies, furthermore considerably maximising long-term wind farm revenue by as much as 20%. Altogether, this thesis shows promising results in using graph-based reinforcement learning to train maintenance-conditioned, inflow-agnostic, and layout-agnostic wake steering controllers for wind farm revenue optimisation.

With Schiphol Airport's flight numbers growing, working assets are essential to ensure on-time processes. The Passenger Boarding Bridge (PBB) is a critical asset in the airport's turnaround process. By ensuring that the asset is working properly, the operational processes can run efficiently. Currently, improving the reliability of the PBB when in use happens after the fault has occurred. With this maintenance strategy, the PBB data is not used to predict the future health state of the PBB. Literature shows that the PBB can be classified as a multi-component system. Research in the predictive maintenance strategy of multi-component systems is still in an early phase. Research until now is more theoretical than practical, and an investigation into applying theoretical knowledge in practice is needed. With the upcoming developments of Industry 4.0, a Cyber-Physical System (CPS) architecture is proposed for a multi-component system. This architecture has been applied to the PBB to develop and use a predictive maintenance strategy for this system. Based on the implementation of a simulation model, the output showed that the proposed CPS architecture enabled the development of a predictive maintenance strategy for the PBB. With this strategy, proactive maintenance is planned while the system's reliability is held on a preset level to ensure a working asset during in-time use.

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.

Inland waterway transport is a low CO2 emission alternative to road transport. A shift towards more inland waterway transport could also help reduce road congestion and noise pollution. Infrastructure bottlenecking, particularly at locks, is part of the reasons preventing this shift. Congestion is leading to delays. Locks can be physically improved, or the passage of the vessels through the locks can be optimized through scheduling. Recent work introduced a novel switching-max-plus-linear system approach to scheduling vessels passing through networks of waterways and locks, also introducing a novel routing component to the scheduling problem. Switching max-plus-linear systems are a convenient way to model scheduling systems using max-plus-linear algebra. The switching-max-plus-linear model only considered locks with a single chamber that can only process one vessel at a time. Additionally, it only considered four specific waterway network configurations, rather than any arbitrary network configuration. Real locks can have multiple chambers, and they can process multiple vessels at the same time if they are placed according to regulations in the two-dimensional space of the chamber. The scope of this report was then to build upon this switching max-plus-linear model by adding support for arbitrary network configurations, and multi-chamber, multi-vessel locks with proper twodimensional ship placement, to answer the main research question: How can multi-vessel, multichamber locks with ship placement be integrated into the SMPL IWT scheduling model? Three mathematical scheduling models formulated as SMPL systems were introduced, each subsequent model building on the previous one. The first introduced support for arbitrary network configurations. The second introduced support for multi-chamber locks and allowed vessels to pass through lock chambers at the same time, provided that their assigned one-dimensional sizes fit into the assigned one-dimensional capacity of the chamber. The third introduced support for twodimensional ship placement through a Tetris-like placement sequence also modeled as a switching max-plus-linear system. The models were translated to mixed integer linear programming models, and arrival time and arrival time offset objectives were added, so they could be used as the rules by which a scheduler would build and solve offline scheduling optimization models on a case-by-case basis. Auxiliary objectives to promote vessels slowing down, rather than waiting stationary in the waiting areas, were also added. The models were all found to be working as intended and implemented correctly through the use of a number of verification cases and tests. Complexity tests showed that the solution times for all three models already became larger than a practical limit of 15-20 minutes for simple scenarios with 10-15 vessels. A heuristic model that mimics how vessels are assigned to lockages in practice was built. Comparisons to the scheduling models on the verification cases showed negligible differences for the models in single-lock cases, but it indicated that the multi-vessel, multi-chamber models may outperform practice in multi-lock cases. Future research is recommended to focus on online optimization to account for disturbances, distributed optimization to reduce calculation times, and validation of the model’s performance with real data.

Purpose - With the ongoing rise of product returns in the fashion industry, exacerbating its GHG footprint when it should be lowered, this paper aims to provide a measurement tool for companies to improve the environmental impact of product returns while considering the economic costs. Companies do not have an understanding of the impact of these returns on their performances, resulting from a gap in research where an accurate measurement method lacks that includes all activities and return methods.
Design/methodology/approach - The returns process and the return methods were analyzed using a literature study and interviews with experts, resulting in a set of environmental and economic indicators. These indicators were aggregated in a Composite Indicator (CI) by rescaling, analytical hierarchy process, and multiplicative aggregation techniques. A benchmark for target performance was developed based on reduction targets of the European Commission, using rescaling, equal weighting, and multiplication and integrated into the CI. An uncertainty- and sensitivity analysis confirmed the model’s robustness. A case study of a fashion retailer verified the model. A scenario analysis based on the PESTLE framework was used to validate the CI and benchmark. A company’s performance measured was compared against the verified benchmark by calculating the performance gap.
Findings - The environmental and economic impact of the return methods for the base case was measured. The impact of the return methods was compared against the benchmark, which shows that home pick-ups have the smallest performance gap with the benchmark for this retailer. Instore returns have the largest gap with the benchmark, indicating worse company performance and lower compliance with the EC targets. The scenario analysis showed how companies could use this CI to compare return improvement strategies with each other and provided potential solutions for companies to improve their performance.
Research limitations/implications - This research provides a tool for fashion companies to measure, analyze, and improve the environmental impact of returns on their performances with EC reduction targets while considering their costs and providing transparency. Customers and governments are provided with transparent information on what- and how a company measures the environmental impact of returns. Governments can use the CI and benchmark proposed in this paper as a transparent tool for environmental reporting and compare the performances of companies sector-wide. For academics, this research provides multiple research opportunities.
Originality/value - This paper contributes by creating a new CI for company performance measurement for fashion companies on the environmental and economic impact of product returns. A new measurement method that assesses the environmental and economic impact of the return activities is developed and gives insight into the differences between the return methods. Also, a new benchmark expressing target performance is developed and incorporated in this CI based on reduction targets of the EC to combat climate change.

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.

Until recently, tenders in Europe were awarded to wind farm developers based on the highest auction prices or the lowest subsidized bids. The wind industry has suggested that non-price-related criteria should be considered for tenders, like plans to reduce greenhouse gas emissions. As a result of the sustainable tender criteria, greenhouse gas emissions are a relatively new KPI for offshore wind farm developers.

Studies have shown that the costs and wind farm availability are sensitive to the fleet composition and were commonly used as criteria in offshore wind fleet optimization models. Offshore wind greenhouse gas emissions were shown to be sensitive to the offshore wind fleet composition as well but thus far not used as criteria for fleet composition decision-making. This study aims to develop an offshore wind O&M multi-objective fleet optimization model that includes GHG emissions as the third criterion for the fleet composition. The model is rendered as a deterministic MIP problem. An epsilon constraint method-inspired approach is proposed to reformulate the multi-objective into a set of perturbed single-objective models, which can be solved using a commercial MIP solver.

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.

For high efficiency and flexibility, a fleet of Automated Guided Vehicles (AGVs) both homogeneous and heterogeneous are widely used automation products for material handling in warehouses and automated production lines. Given the layout capacity, the AGVs interact with each other, which provokes challenges in Driverless Transport Vehicle System (DTVS) traffic management in dynamic environments. One of the main challenges is how to avoid collision and deadlock between heterogeneous fleets of AGVs in a bidirectional layout. This research study proposes a deadlock detection and avoidance algorithm that follows the deadlock prediction and uses a dynamic rerouting strategy with Dijkstra to avoid deadlocks. The mission paths are checked in space and time for overlapping edges by four check conditions and cumulative weights and return a boolean value to avoid cyclic deadlocks. For the heterogeneous fleet of AGVs, a standard communication protocol VDA5050 is used to maintain a standard communication interface between vehicles and the traffic management module with cloud-based microservices for increased processing time and interoperability within the warehouse. This communication interface is used to communicate the novel deadlock control algorithm to a heterogeneous fleet of AGVs. The proposed algorithm not only improves the throughput by increasing vehicle operational time but also successfully avoids congestion and deadlocks with high traffic management efficiency in the logistic transportation system.

This research aim was to design a centralised model that redefines definitions, standardises maintenance by categorisation, and reduces waste by introducing new slot lengths, leading to an equilibrium between flights and maintenance. This equilibrium enhances the airline’s capacity to manage maintenance efficiently and ensures greater uptime. The model addresses the important aspects of airline planning, including maintenance, flight and operation planning. Lean Six Sigma theories serve as a framework to combine the different plannings. The key importance is that the model considers all the stakeholders instead of only one so that the equilibrium between excessive flying and excessive maintenance can be established. In striking such a balance, uptime is assumed to increase.