Recently KLM has revealed the plan to downsize the full-freight cargo fleet in Schiphol Airport, for that reason it is important for the company and the airport to explore the consequences of moving the cargo transported by the full freighters into the bellies of the passenger flights. The consequences of this action in terms of capacity and requirements are still unknown for the stakeholders. The current study illustrates that once the freighters are phased out, the commercial traffic needs to adjust mainly their load factors in order to absorb the cargo that was previously transported by the full freighters. The current model is a version that includes the airside operation of the airport and also the vehicle movement which allows addressing the uncertainties of the operation as well as the limitations and potential problems of the phasing-out action.
The constant growth of air traffic, especially in Europe, is putting pressure on airports, which, in turn, are suffering congestion problems. The airspace surrounding airport, terminal manoeuvring area (TMA), is particularly congested, since it accommodates all the converging traffic to and from airports. Besides airspace, airport ground capacity is also facing congestion problems, as the inefficiencies coming from airspace operations are transferred to airport ground and vice versa. The main consequences of congestion at airport airspace and ground, is given by the amount of delay generated, which is, in turn, transferred to other airports within the network. Congestion problems affect also the workload of air traffic controllers that need to handle this big amount of traffic.This thesis deals with the optimization of the integrated airport operations, considering the airport from a holistic point of view, by including operations such as airspace and ground together. Unlike other studies in this field of research, this thesis contributes by supporting the decisions of air traffic controllers regarding aircraft sequencing and by mitigating congestion on the airport ground area. The airport ground operations and airspace operations can be tackled with two different levels of abstractions, macroscopic or microscopic, based on the time-frame for decision-making purposes. In this thesis, the airport operations are modeled at a macroscopic level.The problem is formulated as an optimization model by identifying an objective function that considers the amount of conflicts in the airspace and capacity overload on the airport ground; constraints given by regulations on separation minima between consecutive aircraft in the airspace and on the runway; decision variables related to aircraft entry time and entry speed in the airspace, landing runway and departing runway choice and pushback time. The optimization model is solved by implementing a sliding window approach and an adapted version of the metaheuristic simulated annealing. Uncertainty is included in the operations by developing a simulation model and by including stochastic variables that represent the most significant sources of uncertainty when considering operations at a macroscopic level, such as deviation from the entry time in the airspace, deviation in the average taxi time and deviation in the pushback time. In this thesis, optimization and simulation techniques are combined together by developing two methods that aim at improving the solution robustness and feasibility. The first method acts as a validation tool for the optimized solution, and it improves the robustness of solution by iteratively fine-tuning some of the optimization model input parameters. The second method embeds the optimization in a simulation environment by taking full advantage of the sliding window approach and creating a loop for a continuous improvement of the optimized solution at each window of the sliding window approach. Both methods prove to be effective by improving the performance, lowering the total amount of conflicts up to 23.33% for the first method and up to 11.2% for the second method, however, in contrast to the deterministic method, the two methods they are not able to achieve a conflict-free scenario due to the effect of uncertainty.In general, the research conducted in this thesis highlights that uncertainty is a factor that affects to a large extent the feasibility of optimized solution when applied to real-world instances, and it, moreover, confirms that using simulation together with optimization has the potentiality toivdeal with uncertainty. The framework developed can be potentially applied to similar problems and different optimization solving methods can be adapted to it.Keywords: Optimization, Simulation, Integrated airport operations, Uncertainty
MULTIFILE
The two-dimensional vehicle routing problem (2L-VRP) is a realistic extension of the classical vehicle routing problem where customers’ demands are composed by sets of non-stackable items. Examples of such problems can be found in many real-life applications, e.g. furniture or industrial machinery transportation. Often, these real-life instances have to deal with uncertainty in many aspects of the problem, such as variable traveling times due to traffic conditions or customers availability. We present a hybrid simheuristic algorithm that combines biased-randomized routing and packing heuristics within a multi-start framework. Monte Carlo simulation is used to deal with uncertainty at different stages of the search process. With the goal of minimizing total expected cost, we use this methodology to solve a set of stochastic instances of the 2L-VRP with unrestricted oriented loading. Our results show that accounting for systems variability during the algorithm search yields more robust solutions with lower expected costs.
The Netherlands is one of the most densely populated countries in Europe. Despite the excellent road network, The Netherlands is confronted with this density on a daily basis: the negative impact of traffic jams and incidents on travel times is growing by 38% the next 5 years. VIA NOVA will lay the necessary foundation for the next step of technological developments to overcome these negative impacts of congestion in future. This next step in technological developments is called Talking Traffic. Vehicles will communicate directly with the infrastructure and other road users and vice versa. The potential with respect to congestion reduction is big, because traffic can be managed more directly. To reach this potential, Talking Traffic relies to a large extent on (big)data already available in modern cars: data of sensors, navigation, etc. However, the problem is data usage in terms of quality and variety among car-brands. The partners stressed the fact that besides technical requirements: data deployment quality, code of practice and a guideline, research should also address business requirements. Without a clear view on quality variations and demands with respect to quality, the data cannot be used effectively. VIA NOVA researches the following issues, o quality and quantity of data from cars o needed quality and quantity of data from cars in Talking Traffic use cases o big data analysis tools to interpret large quantities of data o business models, privacy and security of data from cars The outcome enables users to judge whether data from cars can be useful to solve specific traffic related problems, which data is than to be used, which quality of data is needed and finally the quantity of the needed data. With this measure Talking Traffic can be deployed more effectively resulting in more reduction of congestion.
