Towards an autonomous and intelligent airline operations control

Studies have estimated that irregular operations (flights affected by a disruption) can cost between 2% and 3% of the airline annual revenue and that a better recovery process could result in cost reductions of at least 20%. Even for small airlines this can represent millions of Euros. In this paper we propose a multi-agent system (MAS) whose members represent the roles, functionalities and competences existing in a typical Airline Operations Control Centre (AOCC), the airline entity responsible for managing the impact of irregular events on planned operations. This multiagent based system produces intelligent solutions in the sense that its outcomes are the result of an autonomous reaction and adaption to changes in the environment, solving partial problems simultaneously. We tested our MAS using real data from TAP Portuguese airline company and experimentally compared our system with solutions found by the human operators on TAP Portugal AOCC. A comparison was also made with a more traditional sequential approach that is the typical method followed by AOCCs when solving disruptions. Results from those comparisons show that it is possible to reduce costs and have a better integrated solution with the proposed system

Complexity challenges in ATM

After more than 4 years of activity, the ComplexWorld Network, together with the projects and PhDs covered under the SESAR long-term research umbrella, have developed sound research material contributing to progress beyond the state of the art in fields such as resilience, uncertainty, multi-agent systems, metrics and data science. The achievements made by the ComplexWorld stakeholders have also led to the identification of new challenges that need to be addressed in the future. In order to pave the way for complexity science research in Air Traffic Management (ATM) in the coming years, ComplexWorld requested external assessments on how the challenges have been covered and where there are existing gaps. For that purpose, ComplexWorld, with the support of EUROCONTROL, established an expert panel to review selected documentation developed by the network and provide their assessment on their topic of expertise

Evolutionary Computation methods applied to Operational Control Centers

Durante a execução de um plano operacional, existe a possibilidade do mesmo sofrer rupturas causadas por eventos não esperados. As rupturas afetam pelo menos três dimensðes sobre as quais as companhias aéreas e os centros de controlo operacional devem ter em conta, nomeadamente os passageiros, a tripulação e os aviðes. Normalmente, um ruptura é um estado durante o qual uma operação que esteja a ser executada é afetada por um desvio (que é grande o suficiente para causar uma mudança) do plano original e, por vezes, levando a que o plano não seja execuível. Exem- plos de eventos que podem causar rupturas são condiçðes meteorológicas, ameaças ou ataques terroristas e avarias nos aviðes.Disruption Management, pode então ser definido como o processo que começa após detectar o desvio do plano original. Depois da ruptura, o plano é mudado e nunca mais vai estar tão perto da solução ótima quanto estava antes da ruptura, sendo que pode mesmo vir a ser impossível a continuação do plano. De qualquer maneira existe a necessidade de rever o plano e de minimizar o impacto causado pela ruptura.O MASDIMA é uma grande ajuda para os Centros de Controlo Operacionais das companhias aéreas encontrarem soluçðes para rupturas durante a execução de um plano operacional, e para melhorar quer o tempo de computação quer a qualidade das soluçðes, existe a necessidade de melhorar o sistema. Isto traduz-se em minimizar o impacto quer a ao nvel dos custos quer ao nvel dos atrasos.Para lidar com este problema serão implementados três agentes, sendo que cada um representa um algoritmo evolutivo diferente (Particle Swarm Optimisation, Ant Colony Optimisation e Ge- netic Algorithms) e estarão relacionados com a dimensão avião relativa ao problema. Estes três agentes serão implementados num Sistema Multi-Agente chamado de MASDIMA que represen- tará o Centro de Controlo Operacional.During the execution of an operational plan, there is the likelihood of this plan being affected by some disruptions caused by unexpected events. The disruptions affect at least three dimensions that airline companies and the operational control centers must take into account which are pas- senger, crew and aircraft. Usually, a disruption is a state during which the current operation being executed is affected by a deviation (which is large enough to cause a change) from the original plan, and sometimes unfortunately it leads to an unfeasible plan. Examples of events that might cause disruptions are bad weather, threats or terrorist attacks and aircraft malfunctions.Disruption Management, can be defined as the process that starts after the deviation from the original plan is detected. After the disruption, the plan is changed and it will no longer be as close as it was from an optimal plan or it can even turn into an unfeasible plan. Either way there is a need to review the plan and try to minimize the impact caused by the disruption.MASDIMA is useful to help Airline Operation Control Centers finding a solution to disrup- tions during an operational plan, and in order to improve both computing time and the quality of solutions, there is a need to improve the system. This will translate in minimising the impact both in terms of costs or delays.To deal with that problem three agents will be implemented that will reflect, each one, different evolutionary computation algorithms (Particle Swarm Optimisation, Ant Colony Optimisation and Genetic Algorithms) and are related to the aircraft dimension of the problem. These agents will be implemented on a Multi-Agent System named MASDIMA that represents an Operation Control Center

Network-wide assessment of ATM mechanisms using an agent-based model

This paper presents results from the SESAR ER3 Domino project. Three mechanisms are assessed at the ECAC-wide level: 4D trajectory adjustments (a combination of actively waiting for connecting passengers and dynamic cost indexing), flight prioritisation (enabling ATFM slot swapping at arrival regulations), and flight arrival coordination (where flights are sequenced in extended arrival managers based on an advanced cost-driven optimisation). Classical and new metrics, designed to capture network effects, are used to analyse the results of a micro-level agent-based model. A scenario with congestion at three hubs is used to assess the 4D trajectory adjustment and the flight prioritisation mechanisms. Two different scopes for the extended arrival manager are modelled to analyse the impact of the flight arrival coordination mechanism. Results show that the 4D trajectory adjustments mechanism succeeds in reducing costs and delays for connecting passengers. A trade-off between the interests of the airlines in reducing costs and those of non-connecting passengers emerges, although passengers benefit overall from the mechanism. Flight prioritisation is found to have no significant effects at the network level, as it is applied to a small number of flights. Advanced flight arrival coordination, as implemented, increases delays and costs in the system. The arrival manager optimises the arrival sequence of all flights within its scope but does not consider flight uncertainties, thus leading to sub-optimal actions.Comment: 20 pages, 6 figures, Journal of Air Transport Managemen

Solving airline operations problems using specialized agents in a distributed multi-agent system

An airline schedule very rarely operates as planned. Problems related with aircrafts, crew members and passengers are common and the actions towards the solution of these problems are usually known as operations recovery. The Airline Operations Control Center (AOCC) tries to solve these problems with the minimum cost and satisfying all the required rules. In this paper we present the implementation of a Distributed Multi-Agent System (MAS) representing the existing roles in an AOCC, This MAS deals with several operational bases and for each type of operation problems it has several specialized software agents that implement different algorithms (heuristic, AI, OR, etc.), competing to find the best solution for each problem. We present a real case study taken from an AOCC where a crew recovery problem is solved. Computational results using a real airline schedule are presented, including a comparison with a solution for the same problem found by the human operators in the AOCC. We show that, even in simple problems and when comparing with solutions found by human operators, it is possible to find valid solutions, in less time and with a smaller cost

Engage D3.5 Opportunities for innovative ATM research (interim report)

This document reports on the topics and academic disciplines of past Exploratory Research projects, notably SESAR Workpackage E (long-term and innovative research) and SESAR Exploratory Research (ER) with a view of tracing the evolution of research as well as opportunities for future research. This analysis is complemented with relevant activities in Engage, such as the Engage thematic challenges

Aircraft Maintenance Routing Problem – A Literature Survey

The airline industry has shown significant growth in the last decade according to some indicators such as annual average growth in global air traffic passenger demand and growth rate in the global air transport fleet. This inevitable progress makes the airline industry challenging and forces airline companies to produce a range of solutions that increase consumer loyalty to the brand. These solutions to reduce the high costs encountered in airline operations, prevent delays in planned departure times, improve service quality, or reduce environmental impacts can be diversified according to the need. Although one can refer to past surveys, it is not sufficient to cover the rich literature of airline scheduling, especially for the last decade. This study aims to fill this gap by reviewing the airline operations related papers published between 2009 and 2019, and focus on the ones especially in the aircraft maintenance routing area which seems a promising branch

Application of ICT as a Key Element for Airport Safety and Security Operations

Airport risk management is a demanding task as several different areas have to be monitored including outer edges, car parks, terminals, and other passenger facilities. Information and communication technologies (ICT) are key elements for airport operation safety and security. One of the advantages of ICT based systems is they can react better and faster in real time and perform certain tasks at airports. This paper aims to present a safety overview of ICT and multi-agent systems (MAS) usage in the implementation of various airport operations. This paper aims to present a safety overview of ICT and MAS systems usage in the implementation of various airport operations. This paper summarizes a multi-agent concept that highlights their applications at airports such as passenger transfer, baggage management, aircraft handling, and field service through a detailed and extensive literature review on related topics. Much of the information on processes within the airport, processes in air traffic, and the processes of operators, i.e. airlines, is the result of monitoring work on a software development project for individual airports that serves to manage all processes in airports. The analysis led to the conclusion that safety and security in airports can be additionally improved by greater use of ICT as well as greater use of MAS, which ultimately contributes to the optimization of the airport