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### sensitivity.py

#!/usr/bin/env python3.7

# Copyright 2021, 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)

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
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 scenarioObjVal >= modelSense * GRB.INFINITY:
# Check if the scenario is infeasible
if scenarioObjBound >= modelSense * 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


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