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

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

Disruption Management in Airline Operations Control – An Intelligent Agent-Based Approach

Operations control is one of the most important areas for an airline company. Through operations control mechanisms an airline company monitors all the flights checking if they follow the schedule that was previously defined by other areas of the company. Unfortunately, some problems may arise during this stage (Clausen et al., 2005). Those problems can be related with crewmembers, aircrafts and passengers. The Airline Operations Control Centre (AOCC) includes teams of experts specialized in solving the above problems under the supervision of an operation control manager. Each team has a specific goal contributing to the common and general goal of having the airline operation running under as few problems as possible. The process of solving these kinds of problems is known as Disruption Management (Kohl et al., 2004) or Operations Recovery

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

Quantifying quality operational costs in a multi-agent system for airline operations recovery

When recovering from operational problems, the Airline Operations Control Centre (AOCC) usually tries to minimize direct operational costs while satisfying all the required rules. In this paper we present the implementation of a Distributed Multi-Agent System (MAS) representing the existing real-life roles in an AOCC. This MAS includes software agents that cooperate through a distributed problem solving approach, to find the best solution for each problem. We propose a general approach to quantify quality operational costs, so that passengers' satisfaction can also be considered in the final decision. We present a real case study to introduce our approach to quantify the quality operational costs and solve several real unexpected crew problems. We show that our MAS with quality costs is able to reduce flight delays and increase passenger satisfaction without increasing significantly the direct operational costs. A comparison with two other methods is presented. Copyright © 2009 Praise Worthy Prize S.r.l. - All rights reserved

“You are not my boss!”: Managing inter-organizational collaboration in German ground handling operations

While inter-organizational coordination among firms in networks has become a widespread phenomenon and the governance of inter-organizational networks has garnered considerable attention in the management literature, the repercussions of the network form for managing and organizing work remain a considerable gap in the literature. Building on Gittell’s concept of relational coordination, we explore the inter-organizational work collaboration in four German airports’ ground handling operations. By zooming-in on ramp agents’ boundary spanning work role, our comparative study illustrates whether and how a collaboration in inter-organizational work processes is brought about in practice. Our findings reveal the various practices ramp agents deploy in order to handle the tensions emerging from divergent organizational jurisdictions and the requirements for collaboration. We also illuminate how the field-level context influences inter-organizational collaboration by setting conditions such as workload and time restrictions in distributed service delivery

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

Evaluating Network Analysis and Agent Based Modeling for Investigating the Stability of Commercial Air Carrier Schedules

For a number of years, the United States Federal Government has been formulating the Next Generation Air Transportation System plans for National Airspace System improvement. These improvements attempt to address air transportation holistically, but often address individual improvements in one arena such as ground or in-flight equipment. In fact, air transportation system designers have had only limited success using traditional Operations Research and parametric modeling approaches in their analyses of innovative operations. They need a systemic methodology for modeling of safety-critical infrastructure that is comprehensive, objective, and sufficiently concrete, yet simple enough to be deployed with reasonable investment. The methodology must also be amenable to quantitative analysis so issues of system safety and stability can be rigorously addressed

A Multi-Agent Business Intelligence Framework for the Travel Sector

Business Intelligence in the travel sector includes dimensions such as market intelligence, customer relationship management, yield management, employee scheduling, over/ under booking, tour management, and security management. Each of these dimensions is elaborated on and put in an overarching framework to enable better business intelligence management for the travel sector, identifying both internal and external partners in an increasingly complex industry with ongoing customization of product / service offerings, detailed customer segmentation, and data integration requirements. A multi-agent business intelligence framework is used for the customer interface and customization, linked to a corporate business intelligence system displaying the dimensions above

Multi-agent plan based information gathering

The evolution of the Web has encouraged the development of new Information Gathering techniques. Artificial Intelligence techniques, such as Planning, have also been used for Information Gathering in order to go beyond merely retrieving Web data. Planning has been used traditionally to generate a sequence of actions that specify how information sources should be accessed. In this paper, planning is used mainly for integrating information found in heterogeneous sources. For instance, two different Web sources about flight and train travels, can be represented by two different planning operators, which will be subsequently combined and integrated by a single plan. We have found that a Multi-Agent framework is very appropriate to implement our technique. In order to evaluate our approach empirically, it has been applied to a tourism domain (MAPWEB-ETOURISM), whose purpose is to help a customer to plan his/her trips. In this domain, several specialized Web agents have been used to query travel Web sources, whose results are subsequently integrated by a planning agent to build complete travel solutions. Experimental results show that, by means of integration, more solutions can be found than by using single information sources or even travel meta-searchers. Also, (MAPWEB-ETOURISM) can find new types of solutions by integrating information gathered from heterogeneous Web sources (i.e. flights and trains.Publicad