4,994 research outputs found
Decision making under uncertainties for air traffic flow management
A goal of air traffic flow management is to alleviate projected demand-capacity imbalances at airports and in en route airspace through formulating and applying strategic Traffic Management Initiatives (TMIs). As a new tool in the Federal Aviation Administration\u27s NextGen portfolio, the Collaborative Trajectory Options Programs (CTOP) combines many components from its predecessors and brings two important new features: first, it can manage multiple constrained regions in an integrated way with a single program; second, it allows flight operators to submit a set of desired reroute options (called a Trajectory Options Set or TOS), which provides great flexibility and efficiency.
One of the major research questions in TMI optimization is how to determine the planned acceptance rates for airports or congested airspace regions (Flow Constrained Areas or FCA) to minimize system-wide costs. There are two important input characteristics that need to be considered in developing optimization models to set acceptance rates in a CTOP: first, uncertain airspace capacities, which result from imperfect weather forecast; second, uncertain demand, which results from flights being geographically redistributed after their TOS options are processed. Although there are other demand disturbances to consider, such as popup flights, flight cancellations, and flight substitutions, their effect on demand estimates at FCAs will likely be far less than that of rerouting from TOSs. Hence, to cope with capacity and demand uncertainties, a decision-making under uncertainty problem needs to be solved.
In this dissertation, three families of stochastic programming models are proposed. The first family of models, which are called aggregate stochastic models and are formulated as multi-commodity flow models, can optimally plan ground and air delay for groups of flights given filed route choice of each flight. The second family of models, which are called disaggregate stochastic models and directly control each individual flight, can give the theoretical lower bounds for the very general reroute, ground-, and air-holding problem with multiple congested airspace regions and multiple route options. The third family of models, called disaggregate-aggregate models, can be solved more efficiently compared with the second class of models, and can directly control the queue size at each congested region. Since we assume route choice is given or route can be optimized along with flight delay in a centralized manner, these three families of models, although can provide informative benchmarks, are not compatible with current CTOP software implementation and have not addressed the demand uncertainty problem. The simulation-based optimization model, which can use stochastic programming models as part of its heuristic, addresses the demand uncertainty issue by simulating CTOP TOS allocation in the optimization process, and can give good suboptimal solution to the practical CTOP rate planning problem.
Airline side research problems in CTOP are also briefly discussed in this dissertation. In particular, this work quantifies the route misassignment cost due to the current imperfect Relative Trajectory Cost (RTC) design.
The main contribution of this dissertation is that it gives the first algorithm that optimizes the CTOP rate under demand and capacity uncertainty and is compatible with the Collaborative Decision Making (CDM) CTOP framework. This work is not only important in providing much-needed decision support capabilities for effective application of CTOP, but also valuable for the general multiple constrained airspace resources multiple reroutes optimization problem and the design of future air traffic flow management program
MODELS AND SOLUTION ALGORITHMS FOR EQUITABLE RESOURCE ALLOCATION IN AIR TRAFFIC FLOW MANAGEMENT
Population growth and economic development lead to increasing demand for travel and pose mobility challenges on capacity-limited air traffic networks. The U.S. National Airspace System (NAS) has been operated near the capacity, and air traffic congestion is expected to remain as a top concern for the related system operators, passengers and airlines. This dissertation develops a number of model reformulations and efficient solution algorithms to address resource allocation problems in air traffic flow management, while explicitly accounting for equitable objectives in order to encourage further collaborations by different stakeholders.
This dissertation first develops a bi-criteria optimization model to offload excess demand from different competing airlines in the congested airspace when the predicted traffic demand is higher than available capacity. Computationally efficient network flow models with side constraints are developed and extensively tested using datasets obtained from the Enhanced Traffic Management System (ETMS) database (now known as the Traffic Flow Management System). Representative Pareto-optimal tradeoff frontiers are consequently generated to allow decision-makers to identify best-compromising solutions based on relative weights and systematical considerations of both efficiency and equity.
This dissertation further models and solves an integrated flight re-routing problem on an airspace network. Given a network of airspace sectors with a set of waypoint entries and a set of flights belonging to different air carriers, the optimization model aims to minimize the total flight travel time subject to a set of flight routing equity, operational and safety requirements. A time-dependent network flow programming formulation is proposed with stochastic sector capacities and rerouting equity for each air carrier as side constraints. A Lagrangian relaxation based method is used to dualize these constraints and decompose the original complex problem into a sequence of single flight rerouting/scheduling problems.
Finally, within a multi-objective utility maximization framework, the dissertation proposes several practically useful heuristic algorithms for the long-term airport slot assignment problem. Alternative models are constructed to decompose the complex model into a series of hourly assignment sub-problems. A new paired assignment heuristic algorithm is developed to adapt the round robin scheduling principle for improving fairness measures across different airlines. Computational results are presented to show the strength of each proposed modeling approach
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
Aircraft Trajectory Planning Considering Ensemble Forecasting of Thunderstorms
Mención Internacional en el título de doctorConvective weather poses a major threat that compromises the safe operation of
flights while inducing delay and cost. The aircraft trajectory planning problem under
thunderstorm evolution is addressed in this thesis, proposing two novel heuristic
approaches that incorporate uncertainties in the evolution of convective cells. In
this context, two additional challenges are faced. On the one hand, studies have
demonstrated that given the computational power available nowadays, the best
way to characterize weather uncertainties is through ensemble forecasting products,
hence compatibility with them is crucial. On the other hand, for the algorithms to be
used during a flight, they must be fast and deliver results in a few seconds.
As a first methodology, three variants of the Scenario-Based Rapidly-Exploring
Random Trees (SB-RRTs) are proposed. Each of them builds a tree to explore the
free airspace during an iterative and random process. The so-called SB-RRT, the
SB-RRT∗ and the Informed SB-RRT∗ find point-to-point safe trajectories by meeting
a user-defined safety threshold. Additionally, the last two techniques converge to
solutions of minimum flight length.
In a second instance, the Augmented Random Search (ARS) algorithm is used to
sample trajectories from a directed graph and deform them iteratively in the search
for an optimal path. The aim of such deformations is to adapt the initial graph to the
unsafe set and its possible changes. In the end, the ARS determines the population of
trajectories that, on average, minimizes a combination of flight time, time in storms,
and fuel consumption
Both methodologies are tested considering a dynamic model of an aircraft flying
between two waypoints at a constant flight level. Test scenarios consist of realistic
weather forecasts described by an ensemble of equiprobable members. Moreover,
the influence of relevant parameters, such as the maximum number of iterations,
safety margin (in SB-RRTs) or relative weights between objectives (in ARS) is analyzed.
Since both algorithms and their convergence processes are random, sensitivity
analyses are conducted to show that after enough iterations the results match.
Finally, through parallelization on graphical processing units, the required computational
times are reduced substantially to become compatible with near real-time
operation.
In either case, results show that the suggested approaches are able to avoid dangerous
and uncertain stormy regions, minimize objectives such as time of flight,
flown distance or fuel consumption and operate in less than 10 seconds.Los fenómenos convectivos representan una gran amenaza que compromete la seguridad
de los vuelos, a la vez que incrementa los retrasos y costes. En esta tesis
se aborda el problema de la planificación de vuelos bajo la influencia de tormentas,
proponiendo dos nuevos métodos heurísticos que incorporan incertidumbre en la
evolución de las células convectivas. En este contexto, se intentará dar respuesta a
dos desafíos adicionales. Por un lado, hay estudios que demuestran que, con los
recursos computacionales disponibles hoy en día, la mejor manera de caracterizar la
incertidumbre meteorológica es mediante productos de tipo “ensemble”. Por tanto,
la compatibilidad con ellos es crucial. Por otro lado, para poder emplear los algoritmos
durante el vuelo, deben de ser rápidos y obtener resultados en pocos segundos.
Como primera aproximación, se proponen tres variantes de los “Scenario-Based
Rapidly-Exploring Random Trees” (SB-RRTs). Cada uno de ellos crea un árbol que
explora el espacio seguro durante un proceso iterativo y aleatorio. Los denominados
SB-RRT, SB-RRT∗ e Informed SB-RRT∗ calculan trayectorias entre dos puntos
respetando un margen de seguridad impuesto por el usuario. Además, los dos últimos
métodos convergen en soluciones de mínima distancia de vuelo.
En segundo lugar, el algoritmo “Augmented Random Search” (ARS) se utiliza
para muestrear trajectorias de un grafo dirigido y deformarlas iterativamente en
busca del camino óptimo. El fin de tales deformaciones es adaptar el grafo inicial
a las zonas peligrosas y a los cambios que puedan sufrir. Finalmente, el ARS calcula
aquella población de trayectorias que, de media, minimiza una combinación
del tiempo de vuelo, el tiempo en zonas tormentosas y el consumo de combustible.
Ambas metodologías se testean considerando un modelo de avión volando punto
a punto a altitud constante. Los casos de prueba se basan en datos meteorológicos
realistas formados por un grupo de predicciones equiprobables. Además, se analiza
la influencia de los parámetros más importantes como el máximo número de iteraciones,
el margen de seguridad (en SB-RRTs) o los pesos relativos de cada objetivo
(en ARS). Como ambos algoritmos y sus procesos de convergencia son aleatorios, se
realizan análisis de sensibilidad para mostrar que, tras suficientes iteraciones, los resultados
coinciden. Por último, mediante técnicas de paralelización en procesadores
gráficos, se reducen enormemente los tiempos de cálculo, siendo compatibles con
una operación en tiempo casi-real.
En ambos casos los resultados muestran que los algoritmos son capaces de evitar
zonas inciertas de tormenta, minimizar objetivos como el tiempo de vuelo, la distancia
recorrida o el consumo de combustible, en menos de 10 segundos de ejecución.Programa de Doctorado en Ingeniería Aeroespacial por la Universidad Carlos III de MadridPresidente: Ernesto Staffetti Giammaria.- Secretario: Alfonso Valenzuela Romero.- Vocal: Valentin Polishchu
Coordinated and robust aviation network resource allocation
In the United States, flight operators may schedule flights to most airports at whatever time best achieves their objectives. However, during some time periods, both at airports and in the airspace, these freely-developed schedules may become infeasible because weather or other factors reduce capacity. A plan must then be implemented to mitigate this congestion safely, efficiently, and equitably. Current planning processes treat each congested resource independently, applying various rules to increase interoperation times sufficiently to match the reduced capacity. However, several resources are occasionally congested simultaneously, and ignoring possible dependencies may yield infeasible allocations for flights using multiple resources.
In this dissertation, this problem of developing coordinated flight-slot allocations for multiple congested resources is considered from several perspectives. First, a linear optimization model is developed. It is demonstrated that optimally minimizing flight arrival delays induces an increasing bias against flights using multiple resources. However, the resulting allocations reduce overall arrival delay, as compared to the infeasible independent allocations, and to current operational practice. The analytic properties of the model are used to develop a rule-based heuristic for allocating capacity that achieves comparable aggregate results. Alternatively, minimizing delay assigned at all resources is considered, and this objective is shown to mimic the flights' original schedule order.
Recognizing that minimizing arrival delays is attractive because of its tangible impact on system performance, variations to the original optimization model are proposed that constrain the worst-case performance of any individual user. Several different constraints and cost-based approaches are considered, all of which are successful to varying degrees in limiting inequities.
Finally, the model is reformulated to consider uncertainty in capacity. This adds considerable complexity to the formulation, and introduces practical difficulties in identifying joint probability distributions for the capacity outcomes at each resource. However, this new model is successful in developing more robust flight-slot allocations that enable quick responses to capacity variations.
Each of the optimization models and heuristics presented here are tested on a realistic case study. The problem studied and the approaches employed represent an important middle ground in air traffic flow management research between single resource models and comprehensive ones
Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties
This paper addresses aircraft delays, emphasizing their impact on safety and
financial losses. To mitigate these issues, an innovative machine learning
(ML)-enhanced landing scheduling methodology is proposed, aiming to improve
automation and safety. Analyzing flight arrival delay scenarios reveals strong
multimodal distributions and clusters in arrival flight time durations. A
multi-stage conditional ML predictor enhances separation time prediction based
on flight events. ML predictions are then integrated as safety constraints in a
time-constrained traveling salesman problem formulation, solved using
mixed-integer linear programming (MILP). Historical flight recordings and model
predictions address uncertainties between successive flights, ensuring
reliability. The proposed method is validated using real-world data from the
Atlanta Air Route Traffic Control Center (ARTCC ZTL). Case studies demonstrate
an average 17.2% reduction in total landing time compared to the
First-Come-First-Served (FCFS) rule. Unlike FCFS, the proposed methodology
considers uncertainties, instilling confidence in scheduling. The study
concludes with remarks and outlines future research directions
Informed scenario-based RRT* for aircraft trajectory planning under ensemble forecasting of thunderstorms
Thunderstorms represent a major hazard for flights, as they compromise the safety of both the
airframe and the passengers. To address trajectory planning under thunderstorms, three variants
of the scenario-based rapidly exploring random trees (SB-RRTs) are proposed. During an iterative
process, the so-called SB-RRT, the SB-RRT* and the Informed SB-RRT* find safe trajectories by
meeting a user-defined safety threshold. Additionally, the last two techniques converge to solutions
of minimum flight length. Through parallelization on graphical processing units the
required computational times are reduced substantially to become compatible with near real-time
operation. The proposed methods are tested considering a kinematic model of an aircraft flying
between two waypoints at constant flight level and airspeed; the test scenario is based on a
realistic weather forecast and assumed to be described by an ensemble of equally likely members.
Lastly, the influence of the number of scenarios, safety margin and iterations on the results is
analyzed. Results show that the SB-RRTs are able to find safe and, in two of the algorithms, closeto-
optimum solutions.This work has received funding from (1) the Spanish Government (Project RTI2018-098471-B-C32) and (2) the SESAR Joint
Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 783287
Development and demonstration of an on-board mission planner for helicopters
Mission management tasks can be distributed within a planning hierarchy, where each level of the hierarchy addresses a scope of action, and associated time scale or planning horizon, and requirements for plan generation response time. The current work is focused on the far-field planning subproblem, with a scope and planning horizon encompassing the entire mission and with a response time required to be about two minutes. The far-feld planning problem is posed as a constrained optimization problem and algorithms and structural organizations are proposed for the solution. Algorithms are implemented in a developmental environment, and performance is assessed with respect to optimality and feasibility for the intended application and in comparison with alternative algorithms. This is done for the three major components of far-field planning: goal planning, waypoint path planning, and timeline management. It appears feasible to meet performance requirements on a 10 Mips flyable processor (dedicated to far-field planning) using a heuristically-guided simulated annealing technique for the goal planner, a modified A* search for the waypoint path planner, and a speed scheduling technique developed for this project
Upravljanje putanjama vazduhoplova u kontroli letenja na pre-taktičkom i taktičkom nivou
Global air traffic demand is continuously increasing, and it is predicted
to be tripled by 2050. The need for increasing air traffic capacity motivates a
shift of ATM towards Trajectory Based Operations (TBOs). This implies the
possibility to design efficient congestion-free aircraft trajectories more in
advance (pre-tactical, strategic level) reducing controller’s workload on tactical
level. As consequence, controllers will be able to manage more flights.
Current flow management practices in air traffic management (ATM)
system shows that under the present system settings there are only timid
demand management actions taken prior to the day of operation such as: slot
allocation and strategic flow rerouting. But the choice of air route for a
particular flight is seen as a commercial decision to be taken by airlines, given
air traffic control constraints. This thesis investigates the potential of robust
trajectory planning (considered as an additional demand management action)
at pre-tactical level as a mean to alleviate the en-route congestion in airspace.
Robust trajectory planning (RTP) involves generation of congestion-free
trajectories with minimum operating cost taking into account uncertainty of
trajectory prediction and unforeseen event. Although planned cost could be
higher than of conventional models, adding robustness to schedules might
reduce cost of disruptions and hopefully lead to reductions in operating cost.
The most of existing trajectory planning models consider finding of conflict-free
trajectories without taking into account uncertainty of trajectory prediction. It is
shown in the thesis that in the case of traffic disturbances, it is better to have a
robust solution otherwise newly generated congestion problems would be hard
and costly to solve.
This thesis introduces a novel approach for route generation (3D
trajectory) based on homotopic feature of continuous functions. It is shown that
this approach is capable of generating a large number of route shapes with a
reasonable number of decision variables. Those shapes are then coupled with
time dimension in order to create trajectories (4D)...Globalna potražnja za vazdušnim saobraćajem u stalnom je porastu i
prognozira se da će broj letova biti utrostručen do 2050 godine. Potreba za
povećanjem kapaciteta sistema vazdušnog saobraćaja motivisala je promene u
sistemu upravljanja saobraćajnim tokovima u kome će u budućnosti centralnu
ulogu imati putanje vazduhoplova tzv. “trajectory-based” koncept. Takav
sistem omogućiće planiranje putanja vazduhoplova koje ne stvaraju zagušenja
u sistemu na pre-taktičkom nivou i time smanjiti radno opterećenje kontrolora
na taktičkom nivou. Kao posledica, kontrolor će moći da upravlja više letova
nego u današnjem sistemu.
Današnja praksa upravljanja saobraćajnim tokovima pokazuje da se mali
broj upravljačkih akcija primenjuje pre dana obavljanja letova npr.: alokacija
slotova poletanja i strateško upravljanje saobraćajnim tokovima. Međutim izbor
putanje kojom će se odviti let posmatra se kao komercijalna odluka aviokompanije
(uz poštovanje postavljenih ograničenja od strane kontrole letenja) i
stoga je ostavljen na izbor avio-kompaniji. Većina, do danas razvijenih, modela
upravljanja putanjama vazduhoplova ima za cilj generisanje bez-konfliktnih
putanja, ne uzimajući u obzir neizvesnost u poziciji vazduhoplova. U ovoj
doktorskoj disertaciji ispitivano je planiranje robustnih putanja vazduhoplova
(RTP) na pre-taktičkom nivou kao sredstvo ublažavanja zagušenja u
vazdušnom prostoru . Robustno upravljanje putanjama vazduhoplova
podrazumeva izbor putanja vazduhoplova sa minimalnim operativnim
troškovima koje ne izazivaju zagušenja u vazdušnom prostoru u uslovima
neizvesnosti buduđe pozicije vazduhoplova i nepredviđenih događaja. Iako
predviđeni (planirani) operativni troškovi robustnih putanja mogu u startu biti
veći od operativnih troškova bez-konfliktnih putanja, robusnost može uticati na
smanjenje troškove poremećaja putanja jer ne zahteva dodatnu promenu
putanja vazduhplova radi izbegavanja konfliktnih situacija na taktičkom nivou.
To na kraju može dovesti i do smanjenja stvarnih operativnih troškova. U tezi je
pokazano, da je u slučaju poremećaja saobraćaja bolje imati robustno rešenje
(putanje), jer novo-nastali problem zagušenosti vazdušnog prostora je teško i
skupo rešiti..
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