Meal delivery platforms operate in dynamic environments, facing complex challenges including fluctuating customer demand, deciding where and which restaurants to offer, and efficient rider allocation. This thesis introduces the Restaurant Selection and Rider Dimensioning Problem (RSRDP) by proposing an integrated optimization model that jointly determines the optimal restaurant assortments and rider allocations to maximize the platform's expected profitability, while maintaining high service quality standards.

A nested logit model captures customer purchasing behavior on the platform to model how customers choose a restaurant to order from, where customers initially choose a cuisine type and subsequently select a specific restaurant within that type. Rider operations are modeled through a spatial-temporal network, enabling optimized management of rider flows across urban zones. We formulate the problem as a Mixed-Integer Linear Program (MILP) and research advanced solution methods including Benders decomposition, column generation, branch-and-price, and heuristics. Finally, we propose our novel Iterative Assortment Generation (IAG) algorithm to solve the problem, ensuring computational feasibility for large-scale scenarios.

Computational experiments based on simulated urban delivery data illustrate that integrating restaurant selection with rider dimensioning consistently increases profitability and maintains high service quality compared to traditional methods that separate these decisions. Furthermore, we explore the impact of two distinct rider compensation policies: commission-based (payment per delivery) and fixed employment (hourly wages). Our findings reveal significant trade-offs between these compensation structures. Commission-based compensation offers greater operational flexibility, adaptability to fluctuating demands, and cost-effectiveness; however, it also leads to increased rider relocations and potentially reduced rider satisfaction due to uncertainty in workload. Conversely, fixed employment provides stable rider availability, promoting better workforce management, but potentially incurs higher operational costs and decreased responsiveness to short-term demand fluctuations.

Based on these insights, we recommend meal delivery platforms adopt integrated optimization models as part of their tactical planning to effectively balance service quality and profitability. Platforms should carefully evaluate their rider compensation policies, recognizing that the ideal choice may depend on the specific context of their operational environment, such as demand variability, competitive landscape, and workforce preferences.

Future research could build on this study by investigating dynamic compensation schemes that adapt in real-time to observed demand conditions. Additionally, incorporating hybrid compensation schemes, combining salaried and commission-based riders, and utilizing the strengths of both policies, provides a promising research direction. Developing multi-objective models that balance profitability against service quality measures, such as timely deliveries or rider satisfaction, would further guide operational decisions and customer and rider retention. Furthermore, future research could improve the way service districts are designed. Instead of using fixed boundaries, districts could be adjusted dynamically and tailored more precisely to specific customer segments and changing demand patterns. This approach would mean that restaurant offerings can also change throughout the day to closely match customers' preferences, potentially increasing overall profitability. Furthermore, modeling couriers individually, rather than as aggregated flows, allows more precise scheduling that better accounts for realistic travel times, shift durations, and rider constraints. Although this approach increases modeling complexity and computational demands, it offers more accurate operational insights, leading to better-informed management decisions. Finally, employing heuristic-driven column-generation methods and comparing solution approaches systematically can improve scalability and accuracy.

This research presents a dynamic scheduling model designed to reduce extreme departure time deviations for freight train requests and distribute deviations more evenly across the planning horizon, as an improvement over the current first-come, first-served method. By integrating deterministic demand forecasting and congestion-based penalties into a dynamic scheduling algorithm, the model evaluates overall customer satisfaction after allocating all requests. A sensitivity analysis is conducted to identify the optimal balance between minimizing forecasted congestion and reducing current departure time deviations. The approach aims to achieve a scheduling strategy that minimizes extreme departure time deviations without disproportionately impacting overall time deviations. Results demonstrate the model's ability to reduce some extreme deviations in low-capacity network scenarios and distribute the deviations more evenly throughout the planning horizon, with minimal impact on overall time deviations. In high-capacity networks, improvements are negligible. The findings highlight the potential for targeted benefits under constrained conditions while underscoring the need for further refinement to ensure consistent and meaningful performance across diverse scenarios.

This work presents a comprehensive framework to assist on-demand meal delivery platforms with the decision-making of delivery model choice and the planning of the micro-logistics centers within a mixed operational model. We consider two prevalent delivery models: (a) the owner-operator model operating with independent contractors (IC); and (b) the company-vehicle model with micro-logistics centers (CV). These centers serve as the place to accommodate company-owned vehicles. Assuming platforms have operated with independent contractors for some time, we aim to determine the necessity of micro-logistics centers, indicating whether to maintain the IC model or adopt a hybrid approach, and optimize the planning of necessary centers. We propose a mixed integer optimisation problem to minimize the total costs while considering the convenience for couriers. It combines strategic decisions for locating micro-logistics centers considering the dimensions of the centers (number, locations and vehicle stock) with operational considerations (the impact of couriers' distribution and shifts on repositioning company vehicles). For the CV model, two operational policies are considered: fixed coverage with return-to-origin requirement, and time-variant coverage with global redistribution considering the spatial-temporal variation of demand. Our findings suggest that diverse market conditions and operational approaches can lead to different strategies. We also applied the model for the city of Amsterdam, and it reveals that multiple centers are needed and the platform may invest in courier convenience by choosing center locations with great accessibility.

This study addresses the operation limitations of the current practice in managing shared micro-delivery resources (MDRs) for on-demand meal delivery platforms. The current static hub approach requires long courier non-service riding time for MDR pickups and returns, and the multi-static-hub configuration needs hiring a truck to rebalance the hub inventories, which is pollutant and can cause extra burden to the road traffic. In this research, by making use of the well-developed infrastructures in canal-rich cities and the latest advancements in electric vessel technology, we propose an innovative Mobile-Hub strategy that utilizes electric vessels as mobile hubs (MHs) for MDRs. We formulate the problem as mixed-integer linear programming and name it the Capacitated Mobile Facility Location Problem with Multiple Demands (CMFLP-MDs). The objective of our model is to minimize the total system cost including capital investment and operational cost, while satisfying the demands for MDR pickups and returns of the couriers. Our results show that the Mobile-Hub strategy significantly outperforms the current static central hub approach. The proposed system helps maintain lower and more stable MH inventory levels while providing superior service, which demonstrates the benefits of the Mobile-Hub strategy.

To aid the efforts of electrifying public bus systems and to reduce the impact public transit has on emissions in cities, it is necessary for planners to be well informed when making the decision to electrify the transport system. The methodological approach shows that there is value in considering the development of a new transport system over simply electrifying an existing transport network, as there is a trade-off between investment characteristics and passenger satisfaction. Significant cost savings could be realized by jointly considering line planning, infrastructure location, vehicle, battery and fleet size. This has been investigated by developing a decision making model that selects a lineplan seeking to maximize passenger satisfaction, whilst taking into account, vehicle scheduling, battery capacity and charging station location. The model demonstrated that there is a clear trade-off between these aspects and that a multitude of pareto optimal solutions that can be obtained, depending on weighting that decision-makers assign to the objectives. The simplicity of turning the objective of this methodology into a revenue-maximization function, should make this model a suitable starting point for more serious modelling of investment decision in the future.

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.

This thesis explores the optimization of charging station location within a one-way electric car-sharing system, addressing the inherent challenges of demand uncertainty. We introduce a novel deep learning-based stochastic programming framework (LMSP Framework) to tackle this issue. This framework integrates two key components:
1. A deep learning model (LSTM-MLP-MDN), composed of Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and Mixture Density Network (MDN) architectures, which predicts the probability distribution of traffic demand using historical data.
2. A two-stage stochastic programming model, designed to strategically optimize the locations of charging stations and the initial number of vehicles at each station under demand uncertainty.
The primary goal of this framework is to improve the profitability and operational efficiency of the car-sharing system by optimizing both the location and capacity of charging stations, effectively solving the Charging Station Location Problem (CSLP).
We validate the effectiveness, adaptability, and feasibility of our framework through a comprehensive case study in Manhattan, utilizing historical traffic data to ensure the reliability of the deployment plan. Our results demonstrate that integrating deep learning techniques with stochastic programming significantly enhances both the accuracy of demand forecasting and the consistency of resource allocation in the optimization process for charging station locations. Specifically, the LMSP Framework achieves higher operational efficiency in the short term, as evidenced by superior metrics like Demand Satisfaction Ratio (DSR) and Charging Station Utilization Rate (CSU) compared to traditional methods. This ensures more balanced and efficient resource allocation across different demand scenarios. However, traditional approaches tend to perform better in short-term financial indicators, such as profit and Return on Investment (ROI), as they employ more aggressive resource allocation strategies based on higher demand forecasts. Despite this, the LMSP Framework's focus on operational efficiency positions it as a more viable option for long-term profitability and user satisfaction, offering a sustainable solution for urban mobility.
Furthermore, our analysis provides valuable recommendations for future charging station deployments. These findings have important implications for the planning and operation of electric car-sharing systems, potentially contributing to more sustainable and efficient urban mobility solutions for all stakeholders involved.

In the contemporary global environment, supply chains are increasingly vulnerable to disruptions, with environmental and supplier disturbances posing frequent challenges. This research aimed to understand the influence of a systematic supplier selection approach, combined with an effective risk mitigation strategy, on supply chain performance amidst supply-side disruption risks. We identified a significant gap in current supply chain management: the lack of a structured supplier selection methodology, which can lead to inefficiencies and performance declines. To address this, our study employed the best worst method (BWM) for systematic supplier assessment and ranking. Simultaneously, a discrete-event simulation model on the Simio platform was utilized to emulate supply chain dynamics, factoring in various suppliers and potential disruptions.
Our findings, contextualized within the polymer industry, highlighted Material Quality, Supply Reliability, Price, and Lead Time as the most critical criteria for supplier selection. The BWM rankings correlated strongly with actual supply chain outcomes, emphasizing its effectiveness. In terms of risk mitigation, flexibility-oriented sourcing strategies were superior to redundancy-oriented ones, demonstrating better management of supply-side disruptions and reduced safety stock levels.
These results have both societal and scientific implications. Societally, a methodical supplier selection process ensures resilient supply chains, essential for sectors like healthcare and food, and promotes global collaboration and innovation through diversified supplier sourcing. Scientifically, our research validates the BWM's utility for supplier selection, challenging conventional beliefs linking higher costs to superior risk mitigation. A key insight was the growing importance of flexibility in modern supply chain risk management.
For the polymer sector, we recommend a BWM-based structured supplier selection process. By partnering with top-ranked suppliers and diversifying geographically, businesses can reduce safety stock levels. Our simulation model provides a practical tool for decision-makers, facilitating scenario testing to identify optimal strategies.
However, our study's limitations must be acknowledged. Tailored primarily for the polymer industry, the specific assumptions underpinning our simulation may not be universally relevant. Thus, while our insights are valuable, their applicability might be limited by data constraints and the polymer sector's distinct attributes.
In conclusion, a methodical supplier selection, paired with a flexibility-oriented sourcing approach, optimizes supply chain performance in the face of supply-side disruptions. Enhancing these elements can guide businesses in developing agile supply chains, ensuring cost-effectiveness and improved performance.

The pivotal sector of transportation has shown signs of a surge in demand. The European Union projects a 42% increase in passenger transport from 2019 until 2050. Policymakers and stakeholders must collaborate to address the increasing transportation demand while considering environmental, societal, and economic benefits Despite efforts to mitigate emissions, the transportation sector has not achieved substantial reductions. The emergence of Hyperloop technology presents a disruptive solution that could address this transportation challenge in Europe. It has been pursued as a viable alternative to air travel, rail, and traditional forms of transportation due to its affordability, sustainability, and rapid speeds of up to 1200 km/h. Even though Hyperloop is a promising alternative in the transportation sector, the technology is still largely in development. There are multi-dimensional considerations in understanding whether the Hyperloop will become a mainstream transport option for passengers and whether the conflicting objectives will result in an efficient Hyperloop network. A knowledge gap was identified with a lack of studies to explore the relationship between the network design objectives and the network design itself.
In order to identify the impacts of the Hyperloop network design in the global transportation sector, a literature review was conducted on the transformative potential of the Hyperloop. Key strengths were identified as a reduction in travel times and low operational emissions. On the other hand, the high capital resources required and the uncertainty around the safety of technology were the main points of criticism. In order to analyze the potential demand for Hyperloop and model the modal shift, a Multi- Nominal Logit was employed where a utility function was formulated for the total benefit passengers receive upon completing a trip. The key attributes for the utility function were selected as travel time, travel costs, number of transfers, and safety perception, in alignment with previous studies on the subjects. A utility-based probabilistic mode choice was determined for the available demand. A multi-objective optimization problem was formulated for the facility-location network design of Hyperloop.
The decision variables of the model were formulated as the decision to open a Hyperloop hub at a location and the decision to build infrastructure between the selected Hyperloop hubs. The model output is an alternate network optimized for four different objective functions. These objectives are determined to be (1) Utility Maximization, (2) Probability of Purchase Maximization, (3) Emission Minimization, and (4) Revenue Maximization as these factors were determined to be key performance indicators in a prospective Hyperloop network. The model aims to provide the decision-makers with an overview of the trade-offs involved with varying objective criteria considered in the network generation.
A case study was created to test the model within Europe. The main aim of the case study is to assess the economic and environmental impacts of the Hyperloop system and provide recommendations to policymakers regarding the conception of the Hyperloop network within the European Union. The case study employs the NUTS classification and excludes European countries where the demand data is incomplete and focuses on countries within the TEN-T network. Furthermore, three categories of experimental scenarios were set up to assess the sensitivity of the model to parameter values. The categories are (1) pricing strategy scenarios, (2) safety perception scenarios and (3) policy intervention scenarios. The findings reveal significant disparities in network characteristics based on different objective criteria. The Utility Maximization objective focuses on maximizing trip utility, leading to a network design with direct links between hubs, resulting in compact networks and lower infrastructure costs. However, Spain and Italy have lower priority in this design. On the other hand, the other three objectives (probability of purchase maximization, emission minimization, and revenue maximization) yield networks with a minimum-spanning tree pattern. These networks outperform the utility maximization network in terms of attracting passengers, reducing emissions, and economic performance. To maximize societal benefits, it is recommended to prioritize the remaining three objectives. The study finds that Hyperloop becomes more competitive for longer-distance trips. Experimentation with ticket prices, safety perception, and policy interventions demonstrates their influence on modal share, revenue stability, and carbon emissions. Higher ticket prices discourage Hyperloop usage, safety perception plays a crucial role, and policies discouraging short-haul flights result in higher Hyperloop modal share and lower emissions. These findings highlight the importance of considering ticket prices, safety perception, and strategic policies to promote sustainable transportation and reduce carbon emissions through a modal shift to Hyperloop.
Future research opportunities include expanding the utility function to incorporate additional attributes affecting mode choices, exploring modal shifts from other modes to Hyperloop, relaxing assumptions about geographical obstacles and hub locations, integrating strategic and tactical planning, and validating the model with a broader range of origin-destination pairs. Computational performance can be enhanced using meta-heuristics to compare different heuristics for network outputs and efficiency.

This research lays forward a framework where supplier segmentation, supplier collaboration models, and supplier development strategies are integrated, specifically for remanufacturing. As remanufacturing is gaining popularity this pioneering research may serve as a first step towards more research for
companies that are willing to drive remanufacturing across the whole supply chain. This research compares two proven supplier segmentation methods and links it with supplier new collaboration and existing supplier development strategies. As a result of this study, an integrated framework is proposed. For future research, the framework can be applied to other cases or the framework could be extended.

Efficient distribution is crucial in many industries today, especially in retail, where customer demand grows rapidly. The rise of online shopping and automation has led to a shift towards using Central Distribution Centers (CDCs). Retailers are part of this trend, which requires them to rethink how they handle their distribution. This study focuses on two main challenges: first, changing the distribution system to adapt to new CDCs, and second, dealing with the computational complexities when optimizing the distribution on a large scale. The aim is to design a large distribution system for the allocation of stores and the routing of vehicles. In this research, we introduce new elements to the mathematical model, which are mostly focused on the routing of vehicles. We also improve the computation by implementing a start solution, adjusting the parameters of the Gurobi optimization tool and including a callback function which bounds the run time. This model will determine the routing decisions for demand from several DCs to the stores and find the optimal vehicle routes. Through a case study at a company in the Netherlands, this paper provides insights into the changing world of distribution systems. It offers practical solutions for businesses trying to make their distribution processes better. The analysis revealed that consolidating demand at CDCs can lead to significant cost improvements while altering transport costs between CDCs and RDCs and adjusting order volumes can influence delivery strategies. Despite the absence of direct relationships between routing decisions, distances, and volumes, a logistic regression model provides guidance.

The maritime industry is preparing for a future where human presence is no longer required on board of ships. This will revolutionize the global execution of maritime operations and consequentially introduce unprecedented challenges to the corresponding logistics. This paper presents a non-standard facility location problem (FLP) that arises in the maritime survey industry. The goal is to determine a number of uncapacitated facilities and assign a heterogeneous fleet of both remotely operated vessels and traditional vessels to the located facilities in order to serve the inspection demand of offshore infrastructures such as oil platforms and wind parks. The facilities serve as sites where vessels can refuel and accommodate crew changes. Both current traditional vessels and future uncrewed surface vessels (USVs) are considered in the heterogeneous fleet that respectively combines the distinctive routing behavior of centroid-like hubs and drone-like \textit{one-to-one} trips simultaneously. A mixed-integer linear programming (MILP) model is constructed to determine the optimal harbor locations and the associated vessel fleet size \& mix to perform the inspection demand of North-Sea assets over a multi-period time interval. The simulation results are reported for a real case study commissioned by geo-data company Fugro. The results of the model suggest that the effective establishment of facility locations and corresponding fleet allocation can reduce the total costs and environmental footprint of the survey operations in the North-Sea. This research provides a first piece of reflection regarding MIP problems for remote and autonomous operations in the maritime industry. The research pointed out that the complexity of USV operations in combination with traditional vessels is difficult to capture within an acceptably sparse facility location problem. Finally, this study identifies the stochasticity of inspection operations to be the most promising future contribution to automation research in the maritime industry.

With the increasing demand for public transport systems worldwide and also a lot of these systems running at their maximum capacity, there is a strong need for finding ways for these systems to operate in a more efficient way. In recent transportation research there is an increasing attention for operational conditions and the impact of passengervehicles interaction on the timetables of urban rail networks. Passengervehicle interaction can have a strong impact on the operational conditions of an urban rail line as a large part of the dwell time of a vehicle can be explained by the number of boarding and alighting passengers.

Aircraft LRU (Line Replaceable Unit) MROs (Maintenance, Repair Organization) struggle to meet service levels. To stay competitive, literature describes two strategies; increasing contracted fleet size and not reaching projected repair turn around times. Besides these two strategies, literature does not describe the use of triple echelon order fulfillment for global contracts but it is recommended Sprong (2019) Munsters (2019). This research aims to find relations between the service level and the two strategies from the perspective of expansion rules and operations. A discrete time-step simulation is proposed because the systems component availability changes over time and because of the stochastic behavior of repair time and component failure. The found relations are firstly that send from base operational procedures and secondly that mbk stock influence the theoretic repair time performance. The second relation is that when adding more fleet units (apply a bigger pool), the current performance will deteriorate.

The aim of this thesis is to investigate how the collection of industrial waste can be managed with different types of sub-contraction. Literature review was conducted in order to obtain knowledge about how this type of problems can be modelled along with the types of sub-contraction and how the logistical performance could be assessed. Interviews were performed to get a better understanding of the decision making for this type of problems. Thereafter, two mathematical models were formulated in order to model the decisions at tactical and operational level. In order to get more insights the developed models were implemented with real-life data. Following, further testings were performed to determine the efficiency of the developed models and to obtain managerial insights. A key conclusion was that among the three types of sub-contraction that were examined, the results obtained from the route based and the tour based type give the most realistic solutions. Overall, the results indicate that the introduction of sub-contraction in waste collection brings considerable cost-savings.

Urban transshipment network is a transshipment solution for last-mile distribution in dense metropolitan regions. In this network, a set of micro hubs located in the urban areas are used for deconsolidating shipments and transferring them from high capacity vehicles to light freight vehicles that eventually deliver to customer locations. Due to the shorter distances between the micro hubs and final customers, these networks can be combined with light electric freight vehicles with limited driving ranges to deliver shipments from micro hubs networks. Thus, logistics service providers can potentially adopt these networks for complying with new low emission zone policies that restrict diesel cargo vehicles to enter dense urban regions. However, before deciding to adopt these new networks over existing conventional ones, it is imperative to assess the performance of these networks towards achieving overall business objectives. Although optimization models can be used for the evaluation process, the existing models that can be used for urban transshipment networks are computationally complex, causing their limited application to real-life contexts. Thus, this research presents a new continuous approximation-based model in the form of a support system for LSP to evaluate the performance of urban transshipment networks in a simpler aggregate manner. The application of the support system to a synthetic case study revealed that the model was able to provide a feasible solution that was adequate for strategic evaluation and performance of urban transshipment networks was contingent on the density of customer points in LEZ

Accurately forecasting charging demand is a major factor in energy providers’ ability to meet said demand. A number of methods have been proposed in the literature. This master thesis aims to provide a framework for energy providers to forecast charging demand due to increased EV adoption. Specifically, this research examines the effects that rental EV adoption has on charging demand at Icelandic energy provider ON Power’s fast charging stations. Further, it establishes how increased technological developments and infrastructure spending can be balanced to avoid unnecessary costs, and if ON Power’s charging infrastructure is prepared to meet the electricity demand that a fully electrified rental car fleet would bring. To answer these questions, an agent-based modeling framework was constructed to simulate charging demand in ON Power’s network. The model simulates all tourists that use rental cars in driving the Icelandic ring road as if the entire fleet consisted of EVs. A case study is conducted where the Icelandic ring road is split into nine zones, and six scenarios that each reflect different technological developments are simulated using the modeling framework. The research findings indicate a significant change in charging demand. Firstly, rental EVs affect seasonal charging demand with more than half of the demand occurring in the summer months of June-August. Secondly, geographical charging demand shifts from mostly being in the greater capital area, to mostly being outside of it. Thirdly, a sufficient charging infrastructure at Keflavík Airport is a major factor in the electrification of the Icelandic rental car fleet. The research findings further indicate that with the right strategy, it is well within reach for ON Power to handle the rental EV charging demand. A strategy is proposed for ON power to avoid unnecessary spending while still meeting charging demand, which considers current demand, future demand, and how new charging station locations should be determined.

Today, Oil and gas industry is fulfilling the world’s energy need, but while doing so, it is also facing challenges due to volatile crude oil price and global warming concerns. This puts emphasis on cost optimization to increase the profit margin and avoid any cost leakages. The logistics and distribution domain always remained important for managers interested to reduce cost and add value to its product and services. Many a times, due to imbalance between the production capacity and inventory storage capacity, the logistics supervisors are compelled to rent 3rd party offsite warehouse which puts extra financial burden on the company. Hence, there is a need to align the production, inventory and distribution operations to maximize the profitability of the manufacturing facility. In this work, a conceptual framework has been prepared to bring resilience in the transport planning of ABC Refinery and PP Polymer Plant. The main research question in this research is as follows: “How to design a conceptual model for logistics and distribution system to make it more resilient against real time disturbances?”. To answer this, System design methodology has been adopted. In this approach, functional analysis has been performed to developed alternative solutions to bring resilience in the transport planning which would align the production capacity and inventory, thereby offsetting the impact of any external disturbance. In this work, unstructured interviews, desk research followed by survey among truck drivers has been conducted to identify the factors affecting the ABC transportation. Later, Bayesian network has been used to analyze the present operations and to predict the performance in worst case of road congestion. To answer the main research question, literature review has been performed to design a Decision support system for dynamic slot allocation of Truck loading in ABC. The result of this work shows that the Multi Agents based Decision support system can improvise the Logistics and Distribution system of ABC by bringing more resilience in the Transport planning against any unforeseen events. Descriptive evaluation has been performed to assess the performance of this solution strategy over attributes identified in the system analysis. In the end, this work provides recommendation of using Operations research in the slot definition and slot allocation process for new shipment order.

Aircraft component maintenance, repair, and overhaul (MRO) outsourcing is a rather new phenomenon. Nowadays, component support solutions that are flexible with predictable cost are pursued. In other words, the time to restore and cost of MRO are aimed to be minimised. This research project is collaboration with one of the worlds leaders in aircraft MRO: KLM Engineering & Maintenance. Notable is the unbalanced allocation of jobs to shops due to obsolete contractual agreements, whereas the flexibility to deviate slightly from these agreements is available. Several task to shop assignment policies are developed and evaluated using discrete event simulation. The policies are an extension of the classic assignment problem and are aspired to reduce either the time to restore a component, the cost of MRO, and seek for a solution that includes both attributes. The result of the deployed experimental design is a non-dominating set of solutions where the trade-off between cost and time is clearly recognised. A policy where both cost and time are considered, with emphasis on time, results in a reduction of both performance indicators. However, other policies perform considerably better in terms of either cost or time. The established Pareto front provides a proper basis for further optimisation and depicts the choices for the MRO provider clearly.

The ever increasing need of developing efficient solutions for the transport of cargo, able to reduce transport costs while complying to green policies has promoted the use of multimodal transport during the last decades. Multimodal
transport consists of the use of at least two modes of transportation for the transport of cargo from origin to destination. Governments and transport authorities (network planners) seek to make the optimal investment decisions when choosing where to allocate their budgets. To accomplish this they need to predict the response of the formulated policies on the cargo flow distributions over a multimodal network. Cargo flows over a multimodal network are in turn dependent on the behavior of water and land transport companies (network users), aiming in minimizing their own total costs of transportation. This research has developed a Decision Support Tool that is able to model cargo flows over a multimodal network, and predict the changes of cargo flows as a response to network planners policies. The research has used the freight transport network of Brazil as a testing ground for the model, and focused on improvements on the railway infrastructure. The results of the model suggest that the establishment of new railway connections and higher railway operating speeds can reduce the total costs and environmental footprint of transportation over a multi modal network. Finally, according to the model assumptions, it was concluded network users have a preference towards faster railway services.