What does sensitivity analysis study in the context of linear programming?

Sensitivity analysis in linear programming primarily investigates how variations in the coefficients of an objective function or constraints influence the optimal solutions of the model. When a change occurs in these coefficients, whether through adjustments in resource availability, costs, or other key parameters, sensitivity analysis helps determine how these changes affect the solution's feasibility and optimality.

This process allows decision-makers to understand the robustness of their solution and how sensitive it is to changes in input data. For instance, by evaluating the impact of increasing or decreasing a resource cost, one can assess whether the current optimal solution remains viable or if adjustments are necessary. Sensitivity analysis thus provides critical insights into the operational implications of planned changes and aids in strategic decision-making by outlining the potential ramifications of uncertainties in the model.