• Optimization of touristic dist...

Treffer: Optimization of touristic distribution netwoorks using genetic algorithms

Title:
Optimization of touristic distribution netwoorks using genetic algorithms
Authors:
Medina, Josep R., Yepes, Victor
Publication Year:
2003
Collection:
Universitat Autònoma de Barcelona: Dipòsit Digital de Documents de la UAB
Subject Terms:
Distribution networks, Vehicle routing problem, Tourism demand, Air transportation, Genetic algorithms, Edge mapped recombination operator
Document Type:
Fachzeitschrift article in journal/newspaper
File Description:
application/pdf
Language:
English
ISSN:
20138830
Relation:
SORT : statistics and operations research transactions; Vol. 27, Núm. 1 (January-June 2003), p. 95-112; https://ddd.uab.cat/record/93735; urn:oai:ddd.uab.cat:93735; urn:articleid:20138830v27n1p95; urn:oai:raco.cat:article/28850
Availability:
https://ddd.uab.cat/record/93735
Rights:
open access ; Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. ; https://creativecommons.org/licenses/by-nc-nd/3.0/
Accession Number:
edsbas.3AB20B5F
Database:
BASE

Weitere Informationen

The eight basic elements to design genetic algorithms (GA) are described and applied to solve a low demand distribution problem of passengers for a hub airport in Alicante and 30 touristic destinations in Northern Africa and Western Europe. The flexibility of GA and the possibility of creating mutually beneficial feed-back processes with human intelligence to solve complex problems as well as the difficulties in detecting erroneous codes embedded in the software are described. A new three-parent edge mapped recombination operator is used to solve the capacitated vehicle routing problem required for estimating associated costs with touristic distribution networks of low demand. GA proved to be very flexible especially in changing business environments and to solve decision-making problems involving ambiguous and sometimes contradictory constraints.