In this paper, adjustable robust optimization with polyhedral uncertainty set is used to deal with uncertain demands in profit hub location problem. Hub location problem seeks to find best location for establishing hub facilities, as well as allocating demands nodes to them. In the proposed model, location decisions are taken in the first stage without revealing of uncertainty, while allocation decision is taken in the second stage in presence of uncertain demands. Column and constraint generation algorithm is used to solve proposed model more efficiency. The adjustable robust model and column and constraint generation algorithm analyzed using well-known AP data set with different level of conservatism. Computational experiments show the superiority of column and constraint generation algorithm comparison with Benders decomposition algorithm in the number of iterations. Also the number of hub facilities and objective function (profit) are decreased with increasing in uncertainty budget.

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