Optimal Solution: The solution provides the values of the decision variables that yield the maximum value of the objective function. This represents the best possible outcome under the given constraints.

Objective Function Value: The value of the objective function at the optimal solution indicates the maximum achievable profit, revenue, or other measures being maximized. This tells you how well the objective has been met.

Sensitivity Analysis: This examines how changes in the coefficients of the objective function or the constraints affect the optimal solution. It helps understand the robustness of the solution and whether adjustments could improve outcomes.

Shadow Prices: If constraints are binding (i.e., they are met exactly), the shadow price indicates how much the objective function would improve if the constraint were relaxed by one unit. This provides insight into resource allocation.

Feasibility: Check whether the solution lies within the feasible region defined by the constraints. If the solution is feasible, it confirms that the decision variables comply with the limits imposed.

Interpretation in Context: Relate the optimal values of the decision variables back to the specific problem context. For instance, if the variables represent quantities of products, interpret what these quantities mean for production and sales.