Two artificial intelligence heuristics in solving multiple allocation hub maximal covering problem

Authors:
Ke-rui Weng;Chao Yang;Yun-feng Ma
Affiliations:
School of Management, Huazhong University of Science and Technology, Wuhan, P.R. China;School of Management, Huazhong University of Science and Technology, Wuhan, P.R. China;School of Management, Huazhong University of Science and Technology, Wuhan, P.R. China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

Citing 5
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Abstract

We consider the multiple allocation hub maximal covering problem (MAHMCP): considering a serviced O–D flow was required to reach the destination optionally passing through one or two hubs in a limited time, cost or distance, what is the optimal way to locate p hubs to maximize the serviced flows. By designing a new model for the MAHMCP, we provide two artificial intelligence heuristics based on tabu search and genetic algorithm respectively. Then, we present computational experiments on hub airports location of Chinese aerial freight flows between 82 cities in 2002 and AP data set. By the computational experiments, we find that both GA and TS work well for MAHMCP. We also conclude that genetic algorithm readily finds a better computational result for the MAHMCP, while the tabu search may have a better computational efficiency.