Gurobi Sensitivity Analysis

This repository demonstrates how to perform Sensitivity Analysis in Gurobi for Linear Programming (LP) models. The focus is on extracting and interpreting:

• Objective coefficient ranges (how much you can change profit/cost coefficients before the solution changes).
• Right-hand side (RHS) ranges (how much you can change resource limits before the shadow price/dual value changes).
• Dual values (Shadow prices) for constraints.
• Reduced costs for decision variables.

📊 Features

• General framework for performing Sensitivity Analysis on any LP model.
• Prints allowable increases and decreases for:
  • Objective function coefficients.
  • RHS of constraints.
• Computes and displays dual values (shadow prices).
• Computes reduced costs for variables.
• Fully compatible with Gurobi and Python.