# Python pseudo-code – real implementation using ortools def sensitivity_analysis(model, variable_bounds, constraint_rhs): """ Generates: 1. Shadow prices for each constraint 2. Allowable increase/decrease for RHS values 3. Objective coefficient sensitivity ranges """ for i, constraint in enumerate(model.constraints): shadow_price = constraint.dual_value() print(f"Constraint i: Shadow price = shadow_price:.2f") print(f" RHS can change by ±calculate_rhs_range(constraint)") for var in model.variables: print(f"Variable var.name():") print(f" Reduced cost = var.reduced_cost()") print(f" Obj coefficient range: [var.min_obj_coef, var.max_obj_coef]")
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The software is legally available for educational purposes. It is often hosted by university servers or provided via textbook companion archives. Step-by-Step Download Instructions # Python pseudo-code – real implementation using ortools
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