mip1.py#
#!/usr/bin/env python3.11
# Copyright 2025, Gurobi Optimization, LLC
# This example formulates and solves the following simple MIP model:
# maximize
# x + y + 2 z
# subject to
# x + 2 y + 3 z <= 4
# x + y >= 1
# x, y, z binary
import gurobipy as gp
from gurobipy import GRB
try:
# Create a new model
m = gp.Model("mip1")
# Create variables
x = m.addVar(vtype=GRB.BINARY, name="x")
y = m.addVar(vtype=GRB.BINARY, name="y")
z = m.addVar(vtype=GRB.BINARY, name="z")
# Set objective
m.setObjective(x + y + 2 * z, GRB.MAXIMIZE)
# Add constraint: x + 2 y + 3 z <= 4
m.addConstr(x + 2 * y + 3 * z <= 4, "c0")
# Add constraint: x + y >= 1
m.addConstr(x + y >= 1, "c1")
# Optimize model
m.optimize()
for v in m.getVars():
print(f"{v.VarName} {v.X:g}")
print(f"Obj: {m.ObjVal:g}")
except gp.GurobiError as e:
print(f"Error code {e.errno}: {e}")
except AttributeError:
print("Encountered an attribute error")