Try our new documentation site (beta).
sensitivity.py
#!/usr/bin/env python3.7
# Copyright 2023, Gurobi Optimization, LLC
# A simple sensitivity analysis example which reads a MIP model from a file
# and solves it. Then uses the scenario feature to analyze the impact
# w.r.t. the objective function of each binary variable if it is set to
# 1-X, where X is its value in the optimal solution.
#
# Usage:
# sensitivity.py <model filename>
#
import sys
import gurobipy as gp
from gurobipy import GRB
# Maximum number of scenarios to be considered
maxScenarios = 100
if len(sys.argv) < 2:
print('Usage: sensitivity.py filename')
sys.exit(0)
# Read model
model = gp.read(sys.argv[1])
if model.IsMIP == 0:
print('Model is not a MIP')
sys.exit(0)
# Solve model
model.optimize()
if model.Status != GRB.OPTIMAL:
print('Optimization ended with status %d' % model.Status)
sys.exit(0)
# Store the optimal solution
origObjVal = model.ObjVal
for v in model.getVars():
v._origX = v.X
scenarios = 0
# Count number of unfixed, binary variables in model. For each we create a
# scenario.
for v in model.getVars():
if (v.LB == 0.0 and v.UB == 1.0 and v.VType in (GRB.BINARY, GRB.INTEGER)):
scenarios += 1
if scenarios >= maxScenarios:
break
# Set the number of scenarios in the model
model.NumScenarios = scenarios
scenarios = 0
print('### construct multi-scenario model with %d scenarios' % scenarios)
# Create a (single) scenario model by iterating through unfixed binary
# variables in the model and create for each of these variables a scenario
# by fixing the variable to 1-X, where X is its value in the computed
# optimal solution
for v in model.getVars():
if (v.LB == 0.0 and v.UB == 1.0
and v.VType in (GRB.BINARY, GRB.INTEGER)
and scenarios < maxScenarios):
# Set ScenarioNumber parameter to select the corresponding scenario
# for adjustments
model.Params.ScenarioNumber = scenarios
# Set variable to 1-X, where X is its value in the optimal solution
if v._origX < 0.5:
v.ScenNLB = 1.0
else:
v.ScenNUB = 0.0
scenarios += 1
else:
# Add MIP start for all other variables using the optimal solution
# of the base model
v.Start = v._origX
# Solve multi-scenario model
model.optimize()
# In case we solved the scenario model to optimality capture the
# sensitivity information
if model.Status == GRB.OPTIMAL:
modelSense = model.ModelSense
scenarios = 0
# Capture sensitivity information from each scenario
for v in model.getVars():
if (v.LB == 0.0 and v.UB == 1.0 and v.VType in (GRB.BINARY, GRB.INTEGER)):
# Set scenario parameter to collect the objective value of the
# corresponding scenario
model.Params.ScenarioNumber = scenarios
# Collect objective value and bound for the scenario
scenarioObjVal = model.ScenNObjVal
scenarioObjBound = model.ScenNObjBound
# Check if we found a feasible solution for this scenario
if modelSense * scenarioObjVal >= GRB.INFINITY:
# Check if the scenario is infeasible
if modelSense * scenarioObjBound >= GRB.INFINITY:
print('Objective sensitivity for variable %s is infeasible' %
v.VarName)
else:
print('Objective sensitivity for variable %s is unknown (no solution available)' %
v.VarName)
else:
# Scenario is feasible and a solution is available
print('Objective sensitivity for variable %s is %g' %
(v.VarName, modelSense * (scenarioObjVal - origObjVal)))
scenarios += 1
if scenarios >= maxScenarios:
break
