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We discuss the variability in the performance of multiple runs of branch-and-cut Mixed Integer Linear Programming solvers, and we concentrate on the one deriving from the use of different optimal bases of the Linear Programming relaxations. We propose a new algorithm exploiting more than one of those bases and we show that different versions of the algorithm can be used to stabilize and improve the performance of the solver.doi:10.1007/s12532-015-0096-0 fatcat:jnrgly42rrhl5n3fvizgnpekta