DocumentCode :
2332095
Title :
An efficient genetic algorithm for the uncapacitated single allocation hub location problem
Author :
Naeem, Mohammad ; Ombuki-Berman, Beatrice
Author_Institution :
Dept. of Comput. Sci., Brock Univ., St. Catharines, ON, Canada
fYear :
2010
fDate :
18-23 July 2010
Firstpage :
1
Lastpage :
8
Abstract :
Hub location problem is a NP-hard problem that frequently arises in the design of transportation and distribution systems, postal delivery networks, and airline passenger flow. We propose a simple but effective genetic algorithm (GA) for the uncapacitated single allocation hub location problem (USAHLP). Our main contribution is two new simple chromosome encoding schemes based on indirect representation and two crossover operators. We performed an empirical study to evaluate the effectiveness of the proposed GA using well-known benchmark problems from the Civil Aeronautics Board (CAB) and Australian Post (AP) data sets. The GA found all best-known solutions for the 80 CAB problems and introduced new solutions for the larger problem instances for AP data. The proposed GA can easily be extended to other variants of location problems arising in network design planning in transportation and distributed systems.
Keywords :
distribution strategy; genetic algorithms; transportation; travel industry; Australian post data sets; CAB problems; NP-hard problem; airline passenger flow; benchmark problems; chromosome encoding schemes; civil aeronautics board; distribution system; genetic algorithm; network design planning; postal delivery networks; transportation system; uncapacitated single allocation hub location problem; Artificial neural networks; Benchmark testing; Biological cells; Encoding; Resource management; Simulated annealing; Transportation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2010 IEEE Congress on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6909-3
Type :
conf
DOI :
10.1109/CEC.2010.5586382
Filename :
5586382
Link To Document :
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