An optimization problem often has some uncertain data, and the optimum of a linear program can be very sensitive to small changes in the data. Such a problem can often be modified to a robust program, which is more stable to such changes. Various methods for this are compared, including requiring all versions of the data to be satisfied together (but they may be inconsistent), worst-case MAX–MIN model, and various models where deviations incur penalty costs. Existing methods require substantial computation. It is shown here that smaller computations often suffice; not all cases need be considered. Other penalty methods are suggested, using different norms. Moreover, perturbations of constraint coefficients can be represented by suitable perturbations of a requirement vector.
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Craven, B.D., Islam, S.M.N. Linear Programming with Uncertain Data: Some Extensions to Robust Optimization. J Optim Theory Appl 155, 673–679 (2012). https://doi.org/10.1007/s10957-012-0035-4