Robust optimization: Difference between revisions

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'''Robust optimization'''. A term given to an approach to deal with uncertainty, similar to the recourse model of [[stochastic programming]], except that feasibility for all possible realizations (called scenarios) is replaced by a [[penalty function]] in the objective. As such, the approach integrates [[goal programming]] with a scenario-based description of problem data. To illustrate, consider the LP:

:Min <math> cx + dy: Ax=b, Bx + Cy = e, x, y >=\le 0,</math>

where d, B, C and e are random variables with possible realizations {(d(s), B(s), C(s), e(s): s in {1,...,N}} (N = number of scenarios). The robust optimization model for this LP is: