Robust Linear Programming
Robust Linear Programming (RLP) extends classical linear programming to handle uncertainty in problem data — cost coefficients, constraint coefficients, or right-hand sides — by requiring solutions to remain feasible and near-optimal across all realizations of uncertain parameters within a defined uncertainty set. It replaces probabilistic assumptions with worst-case guarantees, making it practical when distributional knowledge is limited.
Kilderegistrering
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- Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. · DOI 10.1287/opre.1030.0065
- Ben-Tal, A., Nemirovski, A. (1999). Robust solutions of uncertain linear programs. Operations Research Letters, 25(1), 1–13. · DOI 10.1016/S0167-6377(99)00016-4
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