For large-scale linear programming (LP) models, several adjustments can be made to improve efficiency and manageability. First, problem simplification involves eliminating unnecessary variables or constraints to reduce complexity. Second, aggregation groups similar variables or constraints into aggregate forms, which helps decrease the size of the model. Third, decomposition breaks the problem into smaller, more manageable subproblems, often using methods like Benders decomposition. Additionally, using efficient algorithms, such as the simplex method or interior-point methods, can enhance computational efficiency. Employing a sparse matrix representation saves memory and speeds up calculations by only accounting for non-zero elements. Finally, conducting sensitivity analysis helps prioritize which variables or constraints to focus on, aiding in efficient adjustments.