The buildup phase at KLM Cargo requires cumbersome planning actions because of a slow data handover at the warehouse and an uncertain initial state of the buildup process at the beginning of the shift. This study presents a model to improve scheduling of future buildup shifts according to flight departure times of KLM. The most important objectives are to minimize the delays and equally distribute the workload. For this purpose, a MILP model has been created. The problem is modeled as a scheduling problem with time windows. Next to the branch-and-bound method, an adapted tabu search is implemented as an alternative optimization technique. Both methods show adequate performance in terms of computation time and model convergence. With the help of a real-time data architecture, scheduling can now be standardized, take less time, incorporate more detailed information and in addition, bottlenecks can be identified early.

In order to reduce aircraft emissions during on-ground operations, electric taxiing systems (ETS) have been intensively researched to take over or assist in part of the taxiing phase of a flight. One of these ETS is the TaxiBot, deployed by Smart Airport Systems (SAS). While a number of papers have been researching this vehicle routing problem (VRP) in order to minimise costs, fuel consumption or another metric, most research uses deterministic input data. However, sudden changes are inevitable, disrupting the resulting schedule. In this paper, we propose both a strategic model and a tactical model for airport surface movement, taking into account stochastic delays to the flight schedule. The subsequent tactical schedule can be produced with only little deviations from the strategic schedule. The tactical model can be generated in the order of seconds, making it useful for real-life traffic management decision support systems. We found that the tactical schedule does not worsen remarkably. Still 47.9% of the flights can be towed by a TaxiBot, which was 48.5% in the strategic schedule. Different case studies have been performed in order to determine the effect of e.g. the size of the flight schedule and the number of TaxiBots used, on the aircraft taxiing coverage and TaxiBot efficiency. With this, both busy and calm days, now and in the near future are assessed and the optimum number of TaxiBots necessary can be determined. An upper limit is reached at an asymptote starting from 34 TaxiBots, while a lower limit is dependent on a trade-off between flight coverage and spare TaxiBot capacity.

Ride-pooling is a key concept for the future of human mobility and vital in the roll-out of Mobility-as-a-Service (MaaS). Pooling allows individuals to travel at a reduced fare (when comparing to a private alternative) due to the reduction in operating costs as the usage of the vehicles is increased; essentially, the main intentions of pooling are to alleviate traffic congestion, reduce required operator fleet size, and to reduce vehicle hours travelled. Ride-pooling does have its drawbacks, travellers increase their in-vehicle travel times due to the detours induced by sharing. A way of minimising the time lost due to detouring is to incorporate pick-up and drop-off (PUDO) points that travellers would walk to and from when opting for ride-pooling. However, the knowledge on the extent of these benefits with respect to the service configurations used is still limited, therefore the objective of this thesis is to examine how pricing configurations and service settings affect the operator performance and level of service of ride-pooling with PUDO. We extend the utility formulation of an existing algorithm that matches trip requests to attractive shared rides where a route search algorithm assesses a PUDO configuration of a ride by computing and comparing the utility of the vehicle and the utilities of the travellers within the vehicle. The algorithm is applied to the context of Amsterdam and aims to further optimise selected pooled rides where experimentation consists of varying the door-to-door pooling discount, the PUDO discount, the service setting, and the demand level. The service setting sets weights on the utilities where we can favour the vehicle during the route search or treating the travellers and vehicle equally. Results show that increasing PUDO discount increases general attractiveness of the service allowing for more travellers to opt for ride-pooling with PUDO, however the largest differences in system-wide performance occurred when PUDO discount was significantly larger than door-to-door discount. Total vehicle hours could be reduced up to 2.2%, improve passenger utility by 2.8% but can suffer a loss of revenue up to 11.4%. The service setting was also able to control these performance indicators as favouring the vehicle provided the largest reductions in vehicle hours and lowest loss in revenue while treating the traveller and vehicle equally was able to provide the largest improvement in traveller utility. The former service setting induces longer walking times on travellers which is the cause of the greater reductions in vehicle travel time while the latter service setting is the opposite and the reason to why travellers find it more attractive. In essence, ride-pooling with PUDO is able to further reduce vehicle hours and improve traveller utility; pricing configurations and service settings can be helpful with scenarios where supply exceeds demand and vice versa. The use of the service setting showed that ride-pooling with PUDO can be made much more attractive to travellers by setting fair PUDO points to walk to by sacrificing vehicle travel time savings. Such a traveller orientated service setting could be useful when supply exceeds demand. A service provider such as Uber could utilise the insights obtained from this thesis when rolling out such a service in Amsterdam and plan for certain scenarios.

Since the first cargo flight in 1910 (Morrell [42]), air cargo has proven to be of great value to society. With over US $6 trillion worth of goods being transported through air cargo, accounting for around 35% of all global trade measured by value (Boeing [15]), it is impossible to deny its significance in today’s world. Air cargo provides us with a fast, reliable and safe method of transporting goods, making it attractive to industries where short shipping times are of the essence. Generally, there are two conventional ways of transporting air cargo – through the use of full-freighter aircraft and through the use of the belly space under the main cabin on passenger aircraft...

Several investigations have been undertaken to study the velocity and temperature fields associated with the thermal mixing of fluids, and resulting thermal striping in a T-junction. The T-junction thermal mixing and fatigue phenomenon is a major area of study for the purposes of safety, maintenance and operational life in the nuclear industry, in which fluid mixing occurs in cooling circuits for the nuclear reactor. The existing body of work on T-junctions mainly comprises of experimental references performed at high values of Reynolds numbers. However, these available experimental databases are not
sufficient to describe the involved physics in adequate detail, and, due to experimental limitations, accurate data on velocity and temperature fluctuations in regions close to the wall are not accessible. Computational Fluid Dynamics (CFD) can play an important role in predicting such complex flow features. However, predicting complex thermal fatigue phenomena is a challenge for the available momentum and heat flux turbulence models, which also require extensive validation.
It was realised that a comprehensive Direct Numerical Simulation (DNS) of a T-junction was required as a benchmark for validation purposes, but also to better understand the underlying physical phenomena of thermal mixing in the fluid and thermal fatigue in the solid walls. The aim of the thesis is to thus design such a reference DNS experiment of a thermal fatigue scenario calibrated using Reynolds-Averaged Navier-Stokes (RANS) simulations. The feasibility of scaling down the Reynolds number from experimental cases to a computationally-feasible range is investigated. The junction corner shape is also modified to a slightly rounded corner, ensuring that the underlying fundamental physical phenomena of turbulence and thermal mixing flow features were preserved. The pipe lengths of the model were calibrated to ensure there would be no interference of the upstream developing region on the thermal mixing at the junction, and the outlet boundary conditions. A sample proof-of-concept under-resolved DNS (UDNS) case, with high- and low-Prandtl number passive temperature scalars, with iso-temperature, iso-flux and mixed (Robin) wall boundary conditions, is simulated and presented. This proof-of-concept simulation contributes to the finalization of the fully-resolved DNS in computational grid size selection, transient characteristics, computational costs, and additionally, the implementation of the Robin boundary condition in the fully-resolved DNS.

Increased competitiveness with other transport systems and declining operation margins have motivated freight forwarders in the air cargo transport industry to look into horizontal collaboration. This thesis focuses on developing a fully integrated five phase auction based coopetition model, a form of horizontal collaboration where competition is preserved. A combinatorial auction is used to exchange transportation requests without having to reveal critical company information. Freight forwarders submit requests into an auction pool, where they are grouped into attractive bundles by a central planner and offered for auction. The request selection and bundling procedures, developed in this thesis, are based on the time windows of the request deliveries. A freight forwarder’s bid on each bundle is equal to the marginal profit of that bundle, which is obtained by solving two NP-hard routing problems with a simulated annealing and large neighbourhood search metaheuristic. A unique aspect of the auction, is that the dock capacity of the ground handlers is taken into account, which helps to alleviate truck congestion at the ground handlers. The potential of the auction based coopetition model is shown for an air cargo supply chain scenario. There is a clear increase in profitability for the collaborating freight forwarders, because the auction model decreases the transportation costs for the entire coalition. This cost reduction is achieved by an increase in transport efficiency, while the collaboration disadvantages as seen in literature are limited.

In this paper, we introduce the Air Cargo Allocation Plan Recovery Problem, where we embrace the perspective of a combination airline that relies on belly space to transport cargo. We present a recovery model that can reallocate bookings that are offloaded because of disruptions in the demand (overbooking) or in the supply (aircraft swap of cancellation) side. The model is based on a set-partitioning mixed integer linear program formulation with an itinerary generation pre-processing step whose goal is to limit the number of possible itineraries per booking and hence the computational effort. The problem is solved for three different cases from a European airline. The three cases are, a flight cancellation, an aircraft swap, and an aircraft swap with ULD configuration. The results show that the model is adaptable to fit the time available, the operational cost of the initial solution can be improved and the larger amount of the objective function value comes from revenue loss.

Cargo airlines' Revenue Management (RM) departments oversee the acceptance/rejection process of incoming bookings. The overarching goal is to accept as many bookings as possible, hence maximizing profit, while avoiding overbooking and offloading of already accepted shipments, to ensure customer satisfaction. This whole process is characterized by great uncertainty, arising from the fact that the exact shipment dimensions and the available aircraft capacity are not always known beforehand. The decision process is further complicated by the fact that loading of shipments is generally performed by experience, resulting to inefficient use of the available space. These factors pose challenges to the acceptance/rejection of an incoming booking request, that can lead to loss of potential revenue for the airline. A novel combined forecasting and stochastic packing model is presented, that tackles the aforementioned issues. The forecasting block of our model, instead of providing point estimates, generates the probability distribution of the predicted capacity and shipment dimensions through the use of the Monte Carlo (MC) dropout technique in Long Short Term Memory (LSTM) and Multi-Layer Perceptron (MLP) network respectively. The stochastic packing block falls into the general category of Knapsack Problems and is solved using the Extreme Point (EP) heuristic within a user-defined confidence level. It uses the generated distributions to select the optimal ULD configuration and palletization strategy, and makes a decision to accept or decline an incoming booking request accordingly. For evaluation purposes, four case studies with real booking data provided by our partner airline are carried out. The results support the validity of the model as well as the efficiency and robustness of the solution method followed.

The air cargo industry is a challenging environment due to the high competition between the stakeholders involved. This demands, as example, high efficiency from cargo airlines. Efficiency can be ensured by designing loading strategies that fully exploit the available cargo volume. Unknowns in the booking dimensions and flight information make this task challenging. We propose an optimisation and forecasting framework for ULD packing. Goal of the framework is to assist analysts with their decision-making, when assessing whether a new booking should be accepted or declined. Unknown booking dimensions and flight details are forecasted using an ensemble learning method. This information is used as input for a bin packing heuristic, which assesses if the booking fits in the aircraft. In addition, we propose a risk index that quantifies what are the chances the proposed loading configuration (per ULD) is not feasible due to a mismatch between predicted and real booking dimensions.

Congestion in the landside air cargo supply chain occurs for different concurring reasons. Lack of coordination between freight forwarders, as example, might create truck congestion on the ground handler side. Horizontal collaboration between forwarders can be introduced and modeled via mathematical programming to mitigate congestion. Resulting models, which are generally variations of Pickup and Delivery Problem with Time Windows (PDPTW), can be solved to optimality only for small-size instances, and the computation is generally time consuming. We therefore propose a simulated annealing (SA)-embedded adaptive large neighborhood search (ALNS) heuristic to address truck route planning in the landside air cargo supply chain. In this work, we allow the search to visit infeasible time-dependent solutions. Accordingly, the objective function minimizes the feasible solution, where total travel distance cost, total travel time cost and unassigned shipments cost, and the time-dependent violation costs. Computational results are reported for 10 instances that were also solved with a mathematical programming approach. Results shows that the meta-heuristic method performs equally or better than the mathematical model given a computational limit for the latter of 2 hours. In addition, the meta-heuristic method was able to find a feasible solution for those cases where the exact model failed to identify a feasible solution.