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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) # 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 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