| Software | Key Feature | Free Access | |----------|-------------|--------------| | (Google) | Advanced optimization (routing, scheduling, flows) | Open source | | PuLP (Python) | Linear & integer programming | Open source | | OpenSolver (Excel add-in) | Solves up to 200 variables in free version | Free for Excel | | LibreOffice Calc + NLP Solver | Nonlinear programming | Completely free | | SCIP | Mixed-integer programming (very powerful) | Free for academic use | | R + ROI package | Full optimization framework | Open source | 🔧 Generated Feature: Sensitivity Analysis Dashboard If you were designing a useful feature for a free management scientist tool, here's a high-value function you could implement yourself using Python + OR-Tools: Feature: "One‑Click Sensitivity & Shadow Price Report" # 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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Would you like a complete Python script that implements the sensitivity dashboard feature above? | Software | Key Feature | Free Access
