Recovering from airline operational problems with a multi-agent system: a case study

The Airline Operations Control Centre (AOCC) tries to solve unexpected problems during the airline operation. Problems with aircraft, crewmembers and passengers are common and very hard to solve due to the several variables involved. This paper presents the implementation of a real-world multi-agent system for operations recovery in an airline company. The analysis and design of the system was done following a GAIA based methodology. We present the system specification as well as the implementation using JADE. A case study is included, where we present how the system solved a real problem

Minimizing airport peaks problem by improving airline operations performance through an agent based system

Airports are important infra-structures for the air transportationbusiness. One of the major operational constraints is the peak of passengers inspecific periods of time. Airline companies take into consideration the airportcapacity when building the airline schedule and, because of that, the executionof the airline operational plan can contribute to improve or avoid airport peakproblems. The Airline Operations Control Center (AOCC) tries to solveunexpected problems that might occur during the airline operation. Problemsrelated to aircrafts, crewmembers and passengers are common and the actionstowards the solution of these problems are usually known as operationsrecovery. In this paper we propose a way of measuring the AOCC performancethat takes into consideration the relation that exists between airline scheduleand airport peaks. The implementation of a Distributed Multi-Agent System(MAS) representing the existing roles in an AOCC, is presented. We show thatthe MAS contributes to minimize airport peaks without increasing theoperational costs of the airlines

Designing a multi-agent system for monitoring and operations recovery for an airline operations control centre

A operação de uma companhia área raramente acontece como planeado. São comuns os problemas relacionados com os aviões, com as tripulações e com os passageiros. As acções que têm como objectivo resolver estes problemas são conhecidas como Gestão das Irregularidades Operacionais. O Centro de Controlo Operacional da Companhia Aérea (CCO) tenta resolver estes problemas com o mínimo de impacto na operação, com o mínimo custo e, ao mesmo tempo, satisfazendo todas as regras de segurança requeridas. Normalmente, cada problema é tratado separadamente e algumas ferramentas têm sido propostas para ajudar no processo de tomada de decisão pelos coordenadores destes centros de controlo. Observamos o CCO da TAP Portugal, a maior companhia aérea Portuguesa, e, destas observações, várias hipóteses foram identificadas e algumas experimentadas. Acreditamos, e esta é uma das nossas principais hipóteses, que o paradigma do Sistema Multi-Agente (SMA) é mais adequado para representar a organização hierárquica de vários níveis e as várias funções (roles) existentes no CCO. Nesta tese, propomos o desenho e a implementação parcial de um SMA Distribuído que represente as várias funções existentes no CCO. Admitimos a hipótese de que, tirando partido do facto de que cada base operacional tem recursos específicos (quer aviões quer tripulantes) e juntando informações que digam respeito aos custos envolvidos (por exemplo, informação sobre vencimentos dos tripulantes, custos dos hotéis, entre outros), as soluções para os problemas detectados serão encontradas mais rapidamente e serão menos caras. Também admitimos a hipótese de que se utilizarmos agentes de software especializados que implementam diferentes soluções (heurísticas e outras soluções baseadas em modelos de investigação operacional e algoritmos de inteligência artificial) aplicadas ao mesmo problema, a robustez do sistema irá aumentar. Finalmente, acreditamos que a inclusão de um mecanismo de aprendizagem, que aprenda com a utilização anterior dos tripulantes, irá aumentar a qualidade das soluções. Estendendo esse mecanismo de forma a aprender o perfil de cada tripulante e aplicando esse conhecimento na geração de planeamentos (escalas) futuros, a gestão deste recurso tão caro será muito mais eficiente e o nível de satisfação de cada tripulante irá aumentar. Apresentamos um caso de estudo real, obtido no CCO da TAP, onde um problema relacionado com tripulantes é resolvido usando o SMA proposto. Apresentamos resultados computacionais, usando uma operação real da companhia aérea, incluindo a comparação com uma solução para o mesmo problema encontrada pelo operador humano do CCO. Mostramos que, mesmo para problemas simples e quando comparado com soluções encontradas por operadores humanos, no caso específico desta companhia aérea, é possível encontrar soluções válidas, em menos tempo e com menos custos.Nesta tese também mostramos como completamos a metodologia GAIA de forma a melhor analisar e desenhar o SMA proposto para o CCO. Para além de mostrarmos o rationale que está por trás da análise, desenho e implementação do nosso sistema, também mostramos como mapeamos as abstracções usadas no desenho orientado a agentes para código específico em JADE. As vantagens da utilização de uma análise de requisitos orientada a objectivos e a sua influência nas fases seguintes da análise e do desenho, também são apresentadas. Finalmente, propomos diagramas UML 2.0 para representação de vários deliverables da GAIA, tais como, estrutura organizacional, modelos de funções (role) e de interacções e modelos de agentes e de serviços.An airline schedule seldom 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 or disruption management. The Airline Operations Control Center (AOCC) tries to solve these problems with the minimum impact in the airline schedule, with the minimum cost and, at the same time, satisfying all the required safety rules. Usually, each problem is treated separately and some tools have been proposed to help in the decision making process of the airline coordinators. We have observed the AOCC of TAP Portugal, the major Portuguese airline, and, from those observations, several hypotheses have been identified and some of them experimented. We believe, and that is one of our main hypothesis, that the Multi-Agent System (MAS) paradigm is more adequate to represent the multi-level hierarchy organization and the several roles that are played in an AOCC. In this thesis we propose the design and partial implementation of a Distributed MAS representing the existing roles in an AOCC. We hypothesize that if we take advantage of the fact that each operational base has specific resources (both crew and aircrafts) and that if we include information regarding costs involved (for example, crew payroll information and hotels costs, among others), the solutions to the detected problems will be faster to find and less expensive. We also hypothesize that if we use specialized software agents that implement different solutions (heuristic and other solutions based in operations research models and artificial intelligence algorithms), to the same problem, the robustness of the system will increase. Finally, we believe that the inclusion of some kind of learning mechanism that learns from previous utilization of crew members will improve the solutions quality. Extending that learning mechanism to learn each crew member profile, and applying that knowledge for generating future schedules, the management of that expensive resource will be much more efficient and the level of satisfaction of each crew member will increase. We also present a real case study taken from TAP Portugal AOCC, where a crew recovery problem is solved using the MAS. 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 Airline Operations Control Center. We show that, even for simple problems, and when comparing with solutions found by human operators in the case of this airline company, it is possible to find valid solutions, in less time and with a smaller cost. In this thesis we also show how we complement the GAIA methodology in order to better analyze and design the proposed MAS for the AOCC. Besides showing the rationale behind the analysis, design and implementation of our system, we also present how we mapped the abstractions used in agent-oriented design to specific constructs in JADE. The advantages of using a goal-oriented early requirements analysis and its influence on subsequent phases of analysis and design are also presented. Finally, we also propose UML 2.0 diagrams at several different levels for representation of GAIA deliverables, like organizational structure, role and interaction model, agent and service model

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

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

Using specialized agents in a distributed MAS to solve airline operations problems: a case study

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 has several specialized software agents that implement different algorithms, competing to find the best solution for each problem. We present a real case study where a crew recovery problem is solved We show that it is possible to find valid solutions, in less time and with a smaller cost

Mapping service components to EJB business objects

The emerging trends for e-business engineering revolve around specialisation and cooperation. Successful companies focus on their core competencies and rely on a network of business partners for the support services required to compose a comprehensive offer for their customers. Modularity is crucial for a flexible e-business infrastructure, but related requirements seldom reflect on the design and operational models of business information systems. Software components are widely used for the implementation of e-business applications, with proven benefits in terms of system development and maintenance. We propose a service-oriented componentisation of e-business systems as a way to close the gap with the business models they support. Blurring the distinction between external services and internal capabilities, we propose a homogeneous model for the definition of e-business applications components and present a process-based technique for component modelling. We finally present an Enterprise Java Beans extension that implements the model