We present an adaptation of the tri-level Operator-Attacker-Defender (OAD) model for designing resilient supply chains, addressing disruptions such as supplier failures, production shortfalls, and disabled transportation links. These disruptions impact the supply chain's performance regarding its ability to meet customer demand. Originally proposed by Alderson et al. in 2011, the OAD model integrates three levels of decision-making: the operator optimizing system performance, the attacker seeking to disrupt it, and strategic system design decisions to mitigate disruptions. Our conceptual model is based on a multi-commodity flow network with a defined performance metric, formulating the tri-level optimization problem accordingly. Due to its complexity, we employ a decomposition-based solution strategy. Extensive computational experiments demonstrate the model's tractability across various supply chain scenarios. A real-world case study on a global pharmaceutical supply chain illustrates the OAD model's effectiveness against climate-related disruption. With its ability to help explore trade-offs between operational efficiency, costs, and resiliency, this model contributes to Resilient Supply Chain Design (RSCD) by providing a deterministic framework for designing supply chains capable of withstanding disruptions.

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.

Urban areas have been facing the challenge of increased demand for urban transportation due to the expansion of opportunities for activities and the growth of e-commerce. These areas constantly face congestion, leading to more emissions and noise/air pollution. These circumstances have led to a strong interest in sustainability in mobility and logistics in urban areas. In particular, integrating mobility and logistics in a multimodal transport network instead of conventional transportation in which mobility and logistics are handled separately has caught interest. It is expected that the utilisation of water transit as a major means of urban transportation is important for the realisation of a sustainable multimodal urban transport network where abundant waterways are available, and combining mobility and logistics in the urban ferry system has the potential to provide more efficient urban transportation. To investigate the potential of such a system, a dynamic centralised fleet management model that optimises the operation of waterborne vessels is developed in this thesis, considering an electric waterborne vessel system for heterogeneous on-demand service that serves stochastic passenger travel and parcel delivery requests. The model dynamically optimises the operation of vessels by applying a rolling horizon and updates the operation plan every time a new request is inserted. Two solving algorithms, the exact method and the insertion heuristic, are proposed. The computational experiments are conducted by taking the city of Fredrikstad in Norway as the case location to evaluate the solving capability of the proposed solving algorithms and to assess the efficiency and service level of the transport system by comparing the performance with the conventional fixed purpose vessels under different demand scenarios. The demand scenarios are generated using a stochastic approach, which applies a non-homogeneous Poisson process for passenger requests and a probabilistic approach for parcel requests. The results suggest that the proposed insertion heuristic is capable of being applied to this model by providing good solutions in a significantly shorter computational time. Also, combining mobility and logistics results in higher efficiency and service levels for all demand scenarios than those of the fixed purpose vessels. To conclude, a model that determines the operation of electric waterborne vessels for on-demand heterogeneous services considering the stochasticity of the demand is developed in this thesis. The results show the capability of the proposed model to dynamically optimise the operation and the benefits of combining mobility and logistics in vessels for heterogeneous on-demand service.

With the ambition of policy makers to encourage a modal shift to rail, an increase in the demand for running trains can be expected. Capacity wise, this increase in demand could be facilitated by applying moving-block signalling. If railway traffic increases however, so does the difficulty of managing it. Rescheduling systems are currently being developed to help traffic managers with this task. An increase in traffic does however increase the computation time needed for solving the conflict resolution optimization problem tackled by these systems. This could pose a problem since traffic management is a task performed in real-time. An often proposed technique to reduce computation time for conflict resolution is decomposing the problem into multiple coordinated sub-problems. Until now, no research has been performed combining moving-block signalling with decomposition of the conflict resolution problem. This research addresses this gap by developing and testing a distributed moving-block conflict resolution model. The effect of the model on computation time and solution quality in comparison to a centralized model is investigated through a case study of the rail network of the Dutch province Noord-Brabant. The results show a clear improvement in computation time for the distributed model while the solution quality improves or remains the same in the majority of tested scenarios.

Over the past decade, there have been a number of researches regarding road cycling safety such as the prediction of bike-involved collision severity, identification of influencing factors and assessment of cycling safety levels. Many data-driven models have been employed in these studies, among which the Logistic regression is most commonly used. However, as a parametric model, Logistic regression assumes linear relations of variables making it impossible to capture the complexity of the real world. Therefore, this research will use a non-parametric model called CART (Classification and Regression Tree) that has no assumption of variable distribution and is highly explainable and apply it to objective data including bike-involved collision data and road feature data to rate cycling safety. The model is then applied to an urban area in London, UK. The roads in this area are present in different colors indicating the safety levels.

Cycling has gained popularity worldwide, but little is known about the choice behaviour of people searching for a bicycle parking spot in the city centre. Formal parking spaces provided in the city centre are currently often underutilised, whilst many people use fly parking, which causes nuisance. This thesis aims to identify the most effective communication strategy to reduce the utilisation of fly parking and whether this differs for distinct user groups. Traffic signs were chosen as a means to present the different communication strategies. Information on choice behaviour was gathered with the use of a stated preference survey. Discrete choice modelling was used to analyse this data. The results show communication prohibiting the use of fly parking work very effectively in reducing fly parking. Interactions of socio-demographics and communication strategies were also found, indicating that belonging to a certain user group affects the effectiveness of the different communication strategies. What should be noted is that the effect of the interactions is marginal compared to the effect of the communication strategy itself. Meaning that the effectiveness of the communication strategy itself is most important. The research also tested how well traffic signs performed as a means of delivering the communication. It was concluded that solely showing traffic signs without providing any prior or other information is not very effective.

In the Netherlands, online groceries are becoming increasingly popular, as are the challenges grocery companies face in meeting customers' rising demand for smaller and cheaper time slots while maintaining thin profit margins due to a highly competitive market. Customer choice modelling will be used to identify customers' behaviour and control the trade-offs between customer attractiveness and profitability with demand management. As there are parametric and non-parametric models, including Machine Learning, to identify the behaviour, they will be compared to define which model represents customer behaviour best. Based on the F1-score, it is evaluated that the Multinomial logit (MNL) and Neural Network (NN) models outperform the created Benchmark model, derived from assumptions from the data, to ensure added value in predicting performance. Since it is found that the advanced predictive models can provide a better understanding of customer behaviour and identify critical customer characteristics, it will be tested whether these models enhance the simulation closer to reality or produce similar results to the Benchmark model, and if so, whether they can be used to optimise the offer strategy. In essence, does the learned behaviour impact the routes and number of offered slots, and can it be utilised to optimise the provided set of offers? A simulation tool is conducted to determine this, which uses real-selected customer time slots to assess any differences from reality and collect key performance indicators (KPIs) for evaluation. The results of the simulations show that incorporating advanced choice models in the simulations adds value and brings the outcomes closer to reality. As a result, a first attempt is made with two test scenarios to determine whether the advanced choice models can be used to optimise the offer strategy. Promising outcomes are found by analysing the results. However, further research is needed to assess the exact impact of the strategies. In future research, it is recommended to determine the influence of using more historical data and data from other companies in combination with other choice models. Also, it is recommended to consider different strategies for optimising the offer set based on incentives or other features.

Electric vehicles (EVs) take advantage of reducing fossil-based environmental pollution and developing a more sustainable logistics network. Compared with internal combustion engine vehicles (ICEVs), EVs have new technical characteristics like limited battery capacity and long charging time at charging stations. With the diffusion of EVs, vehicle routing problems (VRPs) of EVs draw transportation service providers’ attention, which extends VRPs with intra-route charging considerations. In real-life practice, the punctuality of preplanned
vehicle routing may be affected by uncertain travel time caused by traffic congestion, which derives undesirable penalty costs for violating time windows. Besides, long intra-route charging time at charging stations presents an even greater challenge to trade-off between completing tours with enough electricity and providing delivery service on time. This assignment aims to investigate the impact of travel time uncertainty and electric vehicle characteristics on planning fleet size, vehicle routing, and charging schedules. A pickup and delivery
problem of electric vehicles is studied in this assignment, which considered flexible fleet size, partial charging policy, soft time windows and uncertain travel time. The problem is formulated as a two-stage stochastic linear programming model. The fleet size, vehicle routing, and charging decisions are determined in the first stage. After the realization of travel times, the second stage determines specific charging times at charging stations. The objective is to minimize the total operational cost, which consists of travel costs of vehicle usage and charging,
and the expected penalty cost of earliness, delay and overtime. The sample average approximation method is applied to model the stochastic programming to the deterministic equivalent and the Gurobi Optimizer is used to solve this mixed-integer programming. A computational experiment is conducted based on a small data set with one depot, 9 pickup and delivery requests, 5 charging stations at service vertices and 3 available electric trucks. The travel times along the delivery tour are assumed to follow Gamma distribution. To investigate the trade-off between travel costs and penalty costs, 12 instances are conducted in the experiment by tuning parameters of time windows, the uncertainty of travel time, and the importance of punctuality. The experiment results showed an increasing vehicle fleet size could improve the level of service but also derive more vehicle
usage costs. Besides, the intra-route charging operations impact vehicles’ departure times at each charging station, which subsequently impacts service start time at customer vertices. Longer intra-route charging time has the potential to enhance punctuality in instances with tight time windows or congested traffic conditions. Moreover, different operator inclination leads to different fleet size and charging time preference. Instances attaching more importance to punctuality have a larger fleet size and longer intra-route charging time.

Efficient repositioning strategies for idle vehicles in ride-sourcing systems help reduce passengers' waiting time and drivers' operational costs, which help platforms attract more passengers and drivers. In this paper, we propose a decentralized repositioning strategy for drivers, in which drivers make individual decisions on where they reposition themselves, based on their own experiences. In comparison to an existing centralized repositioning strategy, in which drivers comply with reposition instructions provided by the platform, we examine the effects of the decentralized strategy on service rate, passengers' waiting time and drivers' net income. We compare the reposition strategies under different supply and demand levels and different demand spatial distribution dispersion rates. We also explore the influence of platform information on drivers' decision-making process.
We found that decentralized repositioning strategies have better performance in reducing waiting time, while the centralized strategy is better at increasing driver income and service rate. We also found that when platform information is accessible, the system has the best performance when 20% to 60% proportion of drivers utilize platform information when making decisions.

The simultaneous rapidly increasing demand for home delivery of goods and on-demand expectancy of customers over the past years leaves a tough challenge for the logistical branch. They have to keep up with this increasing demand and simultaneously they are obliged to satisfy consumer service level demands to preserve their customers. On the other hand, as economic goals drive these businesses, they are prompted to operate cost-effectively. As a result, the fleet, deployed to execute the last-mile delivery, should meet both the requirement of cost-efficiency as well as the requirement for meeting consumer service level demands. This raises the question of how to efficiently design a fleet for last-mile on-demand logistics. For a fleet to be able to operate cost-efficiently, the fleet design decisions are required to take both fixed and variable costs into account. As such, the fleet design decisions need to include the consideration of the size of the fleet as well as the distance the vehicles travel on daily basis. Therefore, the goal of this thesis is to develop a novel method for fleet design for last-mile on-demand logistics. This work contributes by being the first to investigate methods for doing fleet design specifically for last-mile on-demand logistics considering multiple depots and variable pick-up locations. The purpose of the method is to determine the operational plans of the individual vehicles, the number of vehicles needed throughout a certain time period, the pick-up locations for all orders and the total distance travelled by the full fleet of vehicles. The proposed method builds upon established fleet design methods for ride-sharing taxi problems. The optimization method is adapted for last-mile on-demand logistics, yielding the required number of vehicles and their individual operational plans. The input of the system is a set of trips, which represent a path of a single vehicle to deliver one or multiple orders from a depot. Connecting two trips, which is called chaining, has the benefit of reducing the number of vehicles used, as chained trips are served by a single vehicle. Additionally, from multiple available depots where orders can be picked up, the method determines the best depot per order. This part of the method is called depot re-assignment. Furthermore, the fleet design problem is modelled as a multi-objective optimisation problem to find the trade-off between fleet size and the total distance the vehicles travel. Three different modelled datasets, each containing 10.000 order requests in the city centre of Amsterdam, are used to prove the value of the given method. A comparison between the method with and without depot re-assignment is made, to prove the value of the given addition of depot re-assignment. It is proven that depot re-assignment is valuable as it decreases or retains the fleet size for all test cases. The experiments conducted show that a significant decrease of the required fleet size can be established by a minor increase in total travelled distance. Furthermore, the optimal trade-off between the fleet size and the total distance travelled can be determined for a specific operation with the knowledge of operational costs for that operation.