A distributed multi-agent system to solve airline operations problems

• Autor(a) principal: António Castro
• Data de Publicação: 2007
• Outros Autores: Eugénio Oliveira
• Tipo de documento: Livro
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://repositorio-aberto.up.pt/handle/10216/345
• topic: Inteligência artificial, Ciências da computação e da informação; Artificial intelligence, Computer and information sciences

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 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. In this paper we present the implementation of a Distributed Multi-Agent System (MAS) that represents the several roles that exist in an AOCC. This MAS deals with several operational bases and for each type of operation problems it has several specialized software agents that implements heuristic solutions and other solutions based in operations research mathematic models and artificial intelligence algorithms. These specialized agents compete 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 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 in 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.