Restricted Robust Optimization for Maximization over Uniform Matroid with Interval Data Uncertainty

For the problem of selecting p items with uncertain (interval) objective function coefficients so as to maximize total profit (maximization over uniform matroid) we introduce the r-restricted robust deviation criterion or the r-robust solution for short. We seek solutions that minimize the r-restricted robust deviation. This new criterion increases the modeling power of the robust deviation (minmax regret) criterion by reducing the level of conservativeness of the robust solution. It is shown that r-restricted robust deviation solutions can be computed efficiently. Results of extensive computational experiments and comparisons with absolute robustness, robust deviation and restricted absolute robustness criteria are reported and discussed.