pyomo_concrete_model_linear_optimization_with_glpk_solver.py

1import pyomo.environ as pyo
2
3# Create a concrete model
4model = pyo.ConcreteModel()
5
6# Define decision variables
7model.x = pyo.Var([1, 2], domain=pyo.NonNegativeReals)
8
9# Define objective function: min x1 + 2*x2
10model.obj = pyo.Objective(expr=model.x[1] + 2*model.x[2])
11
12# Define constraints
13model.con1 = pyo.Constraint(expr=3*model.x[1] + 4*model.x[2] >= 1)
14model.con2 = pyo.Constraint(expr=2*model.x[1] + 5*model.x[2] >= 2)
15
16# Solve the model using the glpk solver (requires glpk installed)
17opt = pyo.SolverFactory('glpk')
18results = opt.solve(model)
19
20# Print the results
21model.display()