Two Artificial Intelligence Heuristics in Solving Multiple Allocation Hub Maximal Covering Problem
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.
KeywordsGenetic Algorithm Tabu Search Tabu List Binary Tournament Selection Airline Network
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