A dynamic programming approach for the aircraft landing problem with aircraft classes

The capacity of a runway system represents a bottleneck at many international airports. The current practice at airports is to land approaching aircraft on a first-come, first-served basis. An active rescheduling of aircraft landing times increases runway capacity or reduces delays. The problem of finding an optimal schedule for aircraft landings is referred to as the “aircraft landing problem”. The objective is to minimize the total delay of aircraft landings or the respective cost. The necessary separation time between two operations must be met. Due to the complexity of this scheduling problem, recent research has been focused on developing heuristic solution approaches. This article presents a new algorithm that is able to create optimal landing schedules on multiple independent runways. Our numerical experiments show that problems with up to 100 aircraft can be optimally solved within seconds instead of hours that are needed to solve these problems with standard optimization tools

An Optimistic Planning Approach for the Aircraft Landing Problem

International audienceThe Aircraft Landing Problem consists in sequencing aircraft on the available runways and scheduling their landing times taking into consideration several operational constraints, in order to increase the runway capacity and/or to reduce delays.In this work we propose a new Mixed Integer Programming (MIP) model for sequencing and scheduling aircraft landings on a single or multiple independent runways incorporating safety constraints by means of separation between aircraft at runways threshold. Due to the NP-hardness of the problem, solving directly the MIP model for large realistic instances yields redhibitory computation times. Therefore, we introduce a novel heuristic search methodology based on Optimistic Planning that significantly improve the FCFS (First-Come First-Served) solution, and provides good-quality solutions inreasonable computational time. The solution approach is then tested on medium and large realistic instances generated from real-world traffic on Paris-Orly airport to show the benefit of our approach

Modelling and Solving Decentralized and Dynamic Aircraft Landing Scheduling Problems

Aircraft landing problem (ALP) is considered as a scheduling problem where aircrafts are sequenced and allocated with appropriate time slots. In this thesis ALP problem is investigated where several constraints such as aircraft’s landing time windows, minimum separation time and position shifting constraints are taken into consideration. Existing approaches such as optimized solution based methods and heuristic methods to tackle different aspects of the problem are reviewed, and a static mathematical model is studied. The mathematical model is solved and verified using random data generated from simulation. The data are generated based on Pierre Elliott Trudeau International Airport (YUL) in Montreal, Quebec, Canada as well as from relevant data base library. AnyLogictm software was used to simulate aircraft landing operations in a runway environment. An agent based simulation was designed to include the dynamic event of aircrafts arrivals to the runway system. In the agent based system, an iterative bidding framework is used to generate flight landing schedule in a decentralized environment. In the decentralized environment, we consider each flight as a self-interest agent competing with other flights to get the most appropriate landing time. The efficiency of the decentralized approach is also studied. The results of the decentralized approach are compared with the centralized ALP solution. The results show that the agent based solution approach is able to generate reasonable landing comparing to optimal aircraft landing schedule from the centralized ALP model

Optimización estática de la secuencia de aterrizajes en entornos con varias pistas

Este proyecto se ocupa de la resolución del problema de secuenciar los aterrizajes de un conjunto de aviones en un aeropuerto (ALP) considerando un entorno estático. En los aeropuertos es habitual que el gestor aeroportuario se enfrente a problemas de capacidad que se agravan en determinadas épocas del año, días concretos o rangos horarios. Esta circunstancia se manifiesta en forma de retrasos generalizados, ya que, cuando en un aeropuerto congestionado es necesario modificar el horario de un vuelo, reajustar la planificación hace que muchos otros se vean afectados. Aumentar la capacidad de los aeropuertos (pistas de aterrizajes y puertas de embarque) se ha convertido en un factor limitante a la hora de hacer frente a la creciente demanda de vuelos en la actualidad. Uno de los principales factores que determinan el rendimiento y eficiencia de las pistas de aterrizaje son los criterios de separación requeridos entre los aterrizajes y despegues de los aviones. Debido a su complejidad, es muy difícil encontrar la solución óptima al problema en la mayoría de los casos. El colapso no afecta solamente a la infraestructura del aeropuerto, sino también en los espacios para estacionar coches, en las zonas reservadas para taxis, en la necesidad de contar con conexiones ferroviarias y, sobre todo, en que las terminales cuenten con suficientes slots para que las aeronaves puedan operar. Sin embargo, otro problema y de mayor relevancia es la saturación de las rutas. En puntos del planeta como el Atlántico Norte o el Sudeste Asiático se viven auténticas autopistas de aviones, cuyo número crece año a año y puede ocasionar retrasos y cancelaciones. Para obtener soluciones al problema presentado, se utilizan los algoritmos de recocido simulado (SA) y búsqueda en entornos variables (VNS). Para comparar el desempeño de ambas técnicas se resuelven cinco problemas de dimensiones 10, 15, 20, 30 y 50 aviones, respectivamenteThis project deals with solving the problem of sequencing the landings of a set of airplanes at an airport (ALP) considering a static environment. It is usual for the airport manager to face capacity problems that worsen at certain times of the year, specific days or time ranges. This circumstance manifests itself in the form of generalized delays, since when a congested airport needs to modify the schedule of a flight, readjusting the planning causes many others to be affected. Increasing the capacity of airports (runways and boarding gates) has become a limiting factor when it comes to coping with the growing demand for flights today. One of the main factors that determine the performance and efficiency of runways are the separation criteria required between aircraft landings and takeoffs. Due to its complexity, it is very difficult to find the optimal solution to the problem in most cases. The collapse does not only affect the infrastructure of the airport, but also in spaces for parking cars, in areas reserved for taxis, in the need to have rail connections and, above all, in which the terminals have enough slots so that the aircraft can operate. However, another problem and of greater relevance is the saturation of the routes. In areas of the planet such as the North Atlantic or Southeast Asia real motorways of airplanes live, whose number grows every year and can cause delays and cancellations. To obtain solutions to the presented problem, the Simulated Annealing (SA) and Variable Neighbourhood Search (VNS) algorithms are used. To compare the performance of both techniques, five problems of dimensions 10, 15, 20, 30 and 50 aircraft, respectively, are solved.Universidad de Sevilla. Máster en Ingeniería Industria

A Hybrid Tabu/Scatter Search Algorithm for Simulation-Based Optimization of Multi-Objective Runway Operations Scheduling

As air traffic continues to increase, air traffic flow management is becoming more challenging to effectively and efficiently utilize airport capacity without compromising safety, environmental and economic requirements. Since runways are often the primary limiting factor in airport capacity, runway operations scheduling emerge as an important problem to be solved to alleviate flight delays and air traffic congestion while reducing unnecessary fuel consumption and negative environmental impacts. However, even a moderately sized real-life runway operations scheduling problem tends to be too complex to be solved by analytical methods, where all mathematical models for this problem belong to the complexity class of NP-Hard in a strong sense due to combinatorial nature of the problem. Therefore, it is only possible to solve practical runway operations scheduling problem by making a large number of simplifications and assumptions in a deterministic context. As a result, most analytical models proposed in the literature suffer from too much abstraction, avoid uncertainties and, in turn, have little applicability in practice. On the other hand, simulation-based methods have the capability to characterize complex and stochastic real-life runway operations in detail, and to cope with several constraints and stakeholders’ preferences, which are commonly considered as important factors in practice. This dissertation proposes a simulation-based optimization (SbO) approach for multi-objective runway operations scheduling problem. The SbO approach utilizes a discrete-event simulation model for accounting for uncertain conditions, and an optimization component for finding the best known Pareto set of solutions. This approach explicitly considers uncertainty to decrease the real operational cost of the runway operations as well as fairness among aircraft as part of the optimization process. Due to the problem’s large, complex and unstructured search space, a hybrid Tabu/Scatter Search algorithm is developed to find solutions by using an elitist strategy to preserve non-dominated solutions, a dynamic update mechanism to produce high-quality solutions and a rebuilding strategy to promote solution diversity. The proposed algorithm is applied to bi-objective (i.e., maximizing runway utilization and fairness) runway operations schedule optimization as the optimization component of the SbO framework, where the developed simulation model acts as an external function evaluator. To the best of our knowledge, this is the first SbO approach that explicitly considers uncertainties in the development of schedules for runway operations as well as considers fairness as a secondary objective. In addition, computational experiments are conducted using real-life datasets for a major US airport to demonstrate that the proposed approach is effective and computationally tractable in a practical sense. In the experimental design, statistical design of experiments method is employed to analyze the impacts of parameters on the simulation as well as on the optimization component’s performance, and to identify the appropriate parameter levels. The results show that the implementation of the proposed SbO approach provides operational benefits when compared to First-Come-First-Served (FCFS) and deterministic approaches without compromising schedule fairness. It is also shown that proposed algorithm is capable of generating a set of solutions that represent the inherent trade-offs between the objectives that are considered. The proposed decision-making algorithm might be used as part of decision support tools to aid air traffic controllers in solving the real-life runway operations scheduling problem