Planning and reconfigurable control of a fleet of unmanned vehicles for taxi operations in airport environment

The optimization of airport operations has gained increasing interest by the aeronautical community, due to the substantial growth in the number of airport movements (landings and take-offs) experienced in the past decades all over the world. Forecasts have confirmed this trend also for the next decades. The result of the expansion of air traffic is an increasing congestion of airports, especially in taxiways and runways, leading to additional amount of fuel burnt by airplanes during taxi operations, causing additional pollution and costs for airlines. In order to reduce the impact of taxi operations, different solutions have been proposed in literature; the solution which this dissertation refers to uses autonomous electric vehicles to tow airplanes between parking lots and runways. Although several analyses have been proposed in literature, showing the feasibility and the effectiveness of this approach in reducing the environmental impact, at the beginning of the doctoral activity no solutions were proposed, on how to manage the fleet of unmanned vehicles inside the airport environment. Therefore, the research activity has focused on the development of algorithms able to provide pushback tractor (also referred as tugs) autopilots with conflict-free schedules. The main objective of the optimization algorithms is to minimize the tug energy consumption, while performing just-in-time runway operations: departing airplanes are delivered only when they can take-off and the taxi-in phase starts as soon as the aircraft clears the runway and connects to the tractor. Two models, one based on continuous time and one on discrete time evolution, were developed to simulate the taxi phases within the optimization scheme. A piecewise-linear model has also been proposed to evaluate the energy consumed by the tugs during the assigned missions. Furthermore, three optimization algorithms were developed: two hybrid versions of the particle swarm optimization and a tree search heuristic. The following functional requirements for the management algorithm were defined: the optimization model must be easily adapted to different airports with different layout (reconfigurability); the generated schedule must always be conflict-free; and the computational time required to process a time horizon of 1h must be less than 15min. In order to improve its performance, the particle swarm optimization was hybridized with a hill-climb meta-heuristic; a second hybridization was performed by means of the random variable search, an algorithm of the family of the variable neighborhood search. The neighborhood size for the random variable search was considered varying with inverse proportionality to the distance between the actual considered solution and the optimal one found so far. Finally, a tree search heuristic was developed to find the runway sequence, among all the possible sequences of take-offs and landings for a given flight schedule, which can be realized with a series of taxi trajectories that require minimum energy consumption. Given the taxi schedule generated by the aforementioned optimization algorithms a tug dispatch algorithm, assigns a vehicle to each mission. The three optimization schemes and the two mathematical models were tested on several test cases among three airports: the Turin-Caselle airport, the Milan-Malpensa airport, and the Amsterdam airport Schiphol. The cost required to perform the generated schedules using the autonomous tugs was compared to the cost required to perform the taxi using the aircraft engines. The proposed approach resulted always more convenient than the classical one

New approaches to airline recovery problems

Air traffic disruptions result in fight delays, cancellations, passenger misconnections, creating high costs to aviation stakeholders. This dissertation studies two directions in the area of airline disruption management – an area of significant focus in reducing airlines’ operating costs. These directions are: (i) a joint proactive and reactive approach to airline disruption management, and (ii) a dynamic aircraft and passenger recovery approach to evaluate the long-term effects of climate change on airline network recoverability. Our first direction proposes a joint proactive and reactive approach to airline disruption management, which optimizes recovery decisions in response to realized disruptions and in anticipation of future disruptions. Specifically, it forecasts future disruptions partially and probabilistically by estimating systemic delays at hub airports (and the uncertainty thereof) and ignoring other contingent disruption sources. It formulates a dynamic stochastic integer programming framework to minimize network-wide expected disruption recovery costs. Specifically, our Stochastic Reactive and Proactive Disruption Management (SRPDM) model combines a stochastic queuing model of airport congestion, a fight planning tool from Boeing/Jeppesen and an integer programming model of airline disruption recovery. We develop an online solution procedure based on look-ahead approximation and sample average approximation, which enables the model's implementation in short computational times. Experimental results show that leveraging partial and probabilistic estimates of future disruptions can reduce expected recovery costs by 1-2%, as compared to a baseline myopic approach that uses realized disruptions alone. These benefits are mainly driven by the deliberate introduction of departure holds to reduce expected fuel costs, fight cancellations and aircraft swaps. Our next direction studies the impact of climate change-imposed constraints on the recoverability of airline networks. We first use models that capture the modified payload-range curves for different aircraft types under multiple climate change scenarios, and the associated (reduced) aircraft capacities. We next construct a modeling and algorithmic framework that allows for simultaneous and integrated aircraft and passenger recovery that explicitly capture the above-mentioned capacity changes in aircraft at different times of day. Our computational results using the climate model on a worst-case, medium-case, and mild-case climate change scenarios project that daily total airline recovery costs increase on average, by 25% to 55.9% on average ; and by 10.6% to 156% over individual disrupted days. Aircraft-related costs are driven by a huge increase in aircraft swaps and cancelations; and passenger-related costs are driven by increases in disrupted passengers who need to be rebooked on the same or a different airline. Our work motivates the critical need for airlines to systematically incorporate climate change as a factor in the design of aircraft as well as in the design and operations of airline networks

The Vehicle Rescheduling Problem with Retiming

When a vehicle breaks down during operation in a public transportation system, the remaining vehicles can be rescheduled to minimize the impact of the breakdown. In this paper, we discuss the vehicle rescheduling problem with retiming (VRSPRT). The idea of retiming is that scheduling flexibility is increased, such that previously inevitable cancellations can be avoided. To incorporate delays, we expand the underlying recovery network with retiming possibilities. This leads to a problem formulation that can be solved using Lagrangian relaxation. As the network gets too large, we propose an iterative neighborhood exploration heuristic to solve the VRSPRT. This heuristic allows retiming for a subset of trips, and adds promising trips to this subset as the al

Multi-agent system for an airline operations control centre

Estágio realizado na TAP Portugal e orientado pelo Eng.º António CastroTese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Dynamic passenger recovery model for airline disruption management

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Mobile Telecommunication and Innovation at (MSc.MTI) at Strathmore UniversityAirline operations experience schedule disruptions every day. These schedule disruptions require intervention from the airline operations controllers through schedule recovery. In a hub and spoke airline network model, a disruption such as a flight cancellation can affect passenger itineraries in multiple fight legs, making it hard for airlines to re-accommodate disrupted passengers within a short time period. The current airline recovery solutions do not explicitly consider passenger recovery. This dissertation investigates the passenger recovery process by considering the challenges faced by passengers during a schedule disruption, the current solutions used to recover disrupted passengers and how a suitable solution can be designed, developed, tested and validated to ensure that it solves these challenges. Data was collected from existing records of flight schedules and passenger bookings. The data collected was used as input to an optimisation model for passenger recovery. Scrum Agile Development methodology was adopted as the software methodology for developing the solution. A proof of concept web application was developed to make passenger recovery easier and reduce operational cost and passenger delay time. An optimization model was developed based on IBM ILOG CPLEX optimiser to help solve disruptions faster. Testing was conducted by both the developer and a selected sample of airline industry users

Weighted Time-Band Approximation Model for Flight Operations Recovery considering Simplex Group Cycle Approaches in China

The time-band approximation model for flight operations recovery following disruption (Bard, Yu, Arguello, IIE Transactions, 33, 931–947, 2001) is constructed by partitioning the recovery period into time bands and by approximating the delay costs associated with the possible flight connections. However, for disruptions occurring in a hub-and-spoke network, a large number of possible flight connections are constructed throughout the entire flight schedule, so as to obtain the approximate optimal. In this paper, we show the application of the simplex group cycle approach to hub-and-spoke airlines in China, along with the related weighted threshold necessary for controlling the computation time and the flight disruption scope and depth. Subsequently, we present the weighted time-band approximation model for flight operations recovery, which incorporates the simplex group cycle approach. Simple numerical experiments using actual data from Air China show that the weighted time-band approximation model is feasible, and the results of stochastic experiments using actual data from Sichuan Airlines show that the flight disruption and computation time are controlled by the airline operations control center, which aims to achieve a balance between the flight disruption scope and depth, computation time, and recovery value

Optimising airline maintenance scheduling decisions

Airline maintenance scheduling (AMS) studies how plans or schedules are constructed to ensure that a fleet is efficiently maintained and that airline operational demands are met. Additionally, such schedules must take into consideration the different regulations airlines are subject to, while minimising maintenance costs. In this thesis, we study different formulations, solution methods, and modelling considerations, for the AMS and related problems to propose two main contributions. First, we present a new type of multi-objective mixed integer linear programming formulation which challenges traditional time discretisation. Employing the concept of time intervals, we efficiently model the airline maintenance scheduling problem with tail assignment considerations. With a focus on workshop resource allocation and individual aircraft flight operations, and the use of a custom iterative algorithm, we solve large and long-term real-world instances (16000 flights, 529 aircraft, 8 maintenance workshops) in reasonable computational time. Moreover, we provide evidence to suggest, that our framework provides near-optimal solutions, and that inter-airline cooperation is beneficial for workshops. Second, we propose a new hybrid solution procedure to solve the aircraft recovery problem. Here, we study how to re-schedule flights and re-assign aircraft to these, to resume airline operations after an unforeseen disruption. We do so while taking operational restrictions into account. Specifically, restrictions on aircraft, maintenance, crew duty, and passenger delay are accounted for. The flexibility of the approach allows for further operational restrictions to be easily introduced. The hybrid solution procedure involves the combination of column generation with learning-based hyperheuristics. The latter, adaptively selects exact or metaheuristic algorithms to generate columns. The five different algorithms implemented, two of which we developed, were collected and released as a Python package (Torres Sanchez, 2020). Findings suggest that the framework produces fast and insightful recovery solutions