Pyomo：从 Python 代码访问解决方案

我有一个想要求解的线性整数程序。我安装了求解器 glpk （感谢这个答案）和pyomo。我写了这样的代码：

from pyomo.environ import *
from pyomo.opt import SolverFactory

a = 370
b = 420
c = 2

model             = ConcreteModel()
model.x           = Var([1,2], domain=NonNegativeIntegers)
model.Objective   = Objective(expr = a * model.x[1] + b * model.x[2], sense=minimize)
model.Constraint1 = Constraint(expr = model.x[1] + model.x[2] == c)
# ... more constraints

opt = SolverFactory('glpk')

results = opt.solve(model)

这会产生文件的解决方案results.yaml.

我有很多问题想要使用相同的模型但不同的来解决a, b, and c价值观。我想分配不同的值a, b, and c，求解模型，得到解model.x[1] and model.x[2]，并有一个列表a, b, c, model.x[1] and model.x[2]。我读文档但示例仅将解决方案写入文件，例如results.yaml.

有什么方法可以从代码中访问解决方案值吗？

Thanks,

以下是脚本的修改版本，说明了打印变量值的两种不同方式：(1) 通过显式引用每个变量；(2) 通过迭代模型中的所有变量。

# Pyomo v4.4.1
# Python 2.7
from pyomo.environ import *
from pyomo.opt import SolverFactory

a = 370
b = 420
c = 4

model             = ConcreteModel()
model.x           = Var([1,2], domain=Binary)
model.y           = Var([1,2], domain=Binary)
model.Objective   = Objective(expr = a * model.x[1] + b * model.x[2] + (a-b)*model.y[1] + (a+b)*model.y[2], sense=maximize)
model.Constraint1 = Constraint(expr = model.x[1] + model.x[2] + model.y[1] + model.y[2] <= c)

opt = SolverFactory('glpk')

results = opt.solve(model)

#
# Print values for each variable explicitly
#
print("Print values for each variable explicitly")
for i in model.x:
  print str(model.x[i]), model.x[i].value
for i in model.y:
  print str(model.y[i]), model.y[i].value
print("")

#
# Print values for all variables
#
print("Print values for all variables")
for v in model.component_data_objects(Var):
  print str(v), v.value

这是生成的输出：

Print values for each variable explicitly
x[1] 1.0
x[2] 1.0
y[1] 0.0
y[2] 1.0

Print values for all variables
x[1] 1.0
x[2] 1.0
y[1] 0.0
y[2] 1.0