This paper focuses on a new method to compute fitness function (ff) values in genetic algorithms for bus network optimization. In the proposed methodology, a genetic algorithm is used to generate iteratively new populations (sets of bus networks). Each member of the population is evaluated by computing a number of performance indicators obtained by the analysis of the assignment of the O/D demand associated to the considered networks. Thus, ff values are computed by means of a multicriteria analysis executed on the performance indicators so found. The goal is to design a heuristic algorithm that allows to achieve the best bus network satisfying both the transport demand and supply.

Genetic algorithms in bus network optimisation

Bielli M;Carotenuto P
2002

Abstract

This paper focuses on a new method to compute fitness function (ff) values in genetic algorithms for bus network optimization. In the proposed methodology, a genetic algorithm is used to generate iteratively new populations (sets of bus networks). Each member of the population is evaluated by computing a number of performance indicators obtained by the analysis of the assignment of the O/D demand associated to the considered networks. Thus, ff values are computed by means of a multicriteria analysis executed on the performance indicators so found. The goal is to design a heuristic algorithm that allows to achieve the best bus network satisfying both the transport demand and supply.
2002
Istituto di Analisi dei Sistemi ed Informatica ''Antonio Ruberti'' - IASI
Istituto Applicazioni del Calcolo ''Mauro Picone''
Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato - STIIMA (ex ITIA)
Genetic Algorithms
Network optimization
Transportation
Network design
Multicriteria analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/155183
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