Column Generation is a technique for solving large-scale linear programs with many variables (columns) by generating only promising variables as needed. Based on Dantzig-Wolfe decomposition, it alternates between: (1) Restricted Master Problem (RMP) - LP with current subset of columns; (2) Pricing Problem - identifies new columns with negative reduced cost to add to RMP. The process continues until no improving columns exist (optimality). Often combined with branch-and-bound (branch-and-price) for integer programs. Applications: crew scheduling, vehicle routing with many routes, cutting stock problems, and airline scheduling. Effectiveness depends on efficiently solving the pricing problem, which often has special structure (shortest path, knapsack).