Robust Optimization addresses optimization problems with uncertain parameters by seeking solutions that perform well across all possible parameter realizations within an uncertainty set. Unlike stochastic programming which assumes probability distributions, robust optimization uses deterministic uncertainty sets (boxes, ellipsoids, polyhedra). For uncertain constraints a(ξ)ᵀx ≤ b(ξ), the robust counterpart ensures feasibility for all ξ in the uncertainty set. Adjustable robust optimization allows some variables to adapt to uncertainty realizations (here-and-now vs. wait-and-see decisions). Advantages: distribution-free, computationally tractable (often reformulated as deterministic problems), and provides worst-case guarantees. Applications: portfolio optimization, supply chain design, and energy systems planning.