Optimization of airport ground operations integrating genetic and dynamic flow management algorithms

Article type: Research Article

Authors: García, Jesús | Berlanga, Antonio | Molina, José M. | Casar, José R.

Affiliations: Dpto. Informatica, Univ. Carlos III de Madrid E-mails: {jgherrer@inf, aberlan@ia, molina@ia}.uc3m.es | Dpto. Senyales, Sistemas y Radiocomunicaciones, Univ. Politécnica de Madrid E-mail: jramon@grpss.ssr.upm.es

Abstract: This paper1 presents a new method for automatically finding the best routes and schedules for airport ground operations within a decision support system for tower controllers, a hard real-world application. It explores the potential advantages of hybridizing two complementary types of algorithmic approaches to find solutions as fast as possible: a genetic algorithm and a time-space dynamic flow management algorithm. An integrated system to combine the strengths of each algorithm and exploit their complementary nature has been analyzed. The proposed flow-management algorithm deterministically optimizes an over-simplified problem, while the genetic algorithm is able to search within a more realistic representation of the real problem, but success is not always guaranteed if the search space grows. The performance of this combination is illustrated using simulated samples of a real-world scenario: ground operations at Madrid Barajas International Airport.

Keywords: Network flow management, hybrid genetic algorithms, scheduling, routing, airport ground operations

Journal: AI Communications, vol. 18, no. 2, pp. 143-164, 2005