Allowable Range

Definition

The allowable range refers to the interval within which the coefficients of a linear programming model can vary without changing the optimal solution. This concept is crucial in understanding how robust the optimal solution is to changes in the parameters of the model, such as resource availability or costs, and it highlights the sensitivity of the solution to these changes.

5 Must Know Facts For Your Next Test

1. The allowable range is determined for each coefficient in the objective function and constraints, indicating how much these values can change before affecting the optimal solution.
2. If a coefficient is changed beyond its allowable range, it may result in a different optimal solution or even make the problem infeasible.
3. The allowable range is typically calculated during sensitivity analysis, which helps decision-makers understand the implications of uncertainty in their models.
4. A wider allowable range indicates that the optimal solution is more robust against variations in parameters, while a narrow range suggests greater sensitivity to changes.
5. Understanding the allowable range helps in making informed decisions about resource allocation and can guide managers in planning for variability in operational conditions.

Review Questions

• How does the allowable range relate to the stability of an optimal solution in linear programming?
  • The allowable range directly influences the stability of an optimal solution by indicating how much variation in coefficients can occur without changing that solution. If coefficients remain within this range, the optimal solution will hold. However, if any coefficient exceeds its allowable range, it could lead to a new optimal solution or potentially render the current solution infeasible, thus affecting decision-making.
• Discuss the significance of understanding the allowable range when conducting sensitivity analysis in linear programming.
  • Understanding the allowable range during sensitivity analysis is crucial because it allows decision-makers to identify how changes in parameters will impact their solutions. It helps them assess risk and robustness, ensuring that strategies remain effective despite fluctuations in coefficients. This insight guides managers on where they might need to adjust resources or strategies when facing uncertainty.
• Evaluate how variations in constraints might affect the allowable range and what implications this has for practical applications in optimization problems.
  • Variations in constraints can significantly affect the allowable range by either expanding or contracting it based on how they interact with the objective function. When constraints tighten, they may reduce the allowable range for certain coefficients, increasing sensitivity and potentially leading to different optimal solutions. This has practical implications in optimization problems, as it emphasizes the need for continuous monitoring of constraints and flexibility in resource management to adapt to changing conditions.

Related terms

Sensitivity Analysis: A method used to determine how different values of an independent variable impact a particular dependent variable under a given set of assumptions.

Objective Function: The mathematical expression that defines the goal of a linear programming problem, usually maximizing or minimizing a certain quantity.

Feasible Region: The set of all possible points that satisfy the constraints of a linear programming problem, representing all potential solutions.