Why my mutable parameter does not update in Pyomo?

Question

I am trying to update my mutable parameter Nc in my Abstract model the initial value is 4 I constructed the instance then change instance.Nc to 5 and solve it but it is still 4 (initial value) , can any body help ?

from pyomo.environ import *
import random 

model = AbstractModel()
model.i = RangeSet(40)
model.j = Set(initialize=model.i)
model.x = Var(model.i,model.j, initialize=0,within=Binary)
model.y = Var(model.i, within=Binary)

model.Nc=Param(initialize=5,mutable=True)
def Ninit(model,i):
    return random.randint(0,1)

model.N=Param(model.i,initialize=Ninit,mutable=True)

def Dinit(model,i,j):
    return random.random()
model.D=Param(model.i,model.j,initialize=Dinit,mutable=True)
def rule_C1(model,i,j):
        return model.x[i,j]<=model.N[i]*model.y[j] 
model.C1   = Constraint(model.i,model.j,rule=rule_C1)
def rule_C2(model):
        return sum(model.y[i] for i in model.i )==model.Nc
model.C2   = Constraint(rule=rule_C2)
def rule_C3(model,i):
        return sum(model.x[i,j] for j in model.j)==model.N[i]

model.C3   = Constraint(model.i,rule=rule_C3)

def rule_OF(model):
    return sum( model.x[i,j]*model.D[i,j] for i in model.i for j in model.j )
model.obj = Objective(rule=rule_OF, sense=minimize)
opt = SolverFactory('glpk')

#model.NC=4
instance = model.create_instance()
instance.NC=4
results = opt.solve(instance) # solves and updates instance
print('NC= ',value(instance.Nc))
print('OF= ',value(instance.obj))

Answer 1

Before creating the instance, you should provide the value for model.Nc (not model.NC).

It should look like:

opt = SolverFactory('glpk')

model.Nc=4 #correct this variable Nc

instance = model.create_instance()
results = opt.solve(instance) # solves and updates instance
print('NC= ',value(instance.Nc))
print('OF= ',value(instance.obj))

Answer 2

This is because instance.NC=4 has a wrong spelling for Nc

In order to fix this, the correct spelling of Nc should be used (not NC)