How to dynamically set variable names in Pyomo?

Question

I am looking to set variables in my Pyomo model by using a loop, so that they can be created automatically. However, each variable also contains bounds.

I was hoping that it can loop through a dictionary to assign the bounds to each variable.

Current code

coeffs = [120, 600, 800, 200]
y_target = 165120

rangeStore = []

for index in range(4):
    # Define model and variables
    model = ConcreteModel()
    model.b = Var(bounds=(0,15))
    model.c = Var(bounds=(15,60))
    model.d = Var(bounds=(60,150))
    model.e = Var(bounds=(150,300))
    model_x = [model.b,model.c, model.d, model.e]

    # Model Constraint
    def weight_rule(model):
        return sum(coeffs[i] * model_x[i] for i in range(len(coeffs))) == y_target 
    model.weight = Constraint( rule=weight_rule )

    # Model Objective
    obj = model_x[index]
    model.objective = Objective(expr=obj, sense=minimize)

    # Solve the model
    solver = SolverFactory('glpk')
    solver.solve(model)
    
    # Store value
    rangeStore.append([value(model_x[i]) for i in range(len(coeffs))])

Part I want to be created in loop

    model.b = Var(bounds=(0,15))
    model.c = Var(bounds=(15,60))
    model.d = Var(bounds=(60,150))
    model.e = Var(bounds=(150,300))

Answer 1

coeffs = [120, 600, 800, 200]
y_target = 165120

rangeStore = []

bound_dic = {0:[0,15],1:[15,60],2:[60,150],3:[150,300]}
model = ConcreteModel()
model.ind = set(bound_dic.keys())

def b_rule(model, i):
    return (bound_dic[i][0], bound_dic[i][1])

model.x = Var(model.ind, bounds = b_rule)

model.weight = Constraint(expr = sum(coeffs[i] * model.x[i] for i in range(len(coeffs))) == y_target)

# Model Objective
obj = model.x[index]
model.objective = Objective(expr=obj, sense=minimize)

# Solve the model
solver = SolverFactory('glpk')
solver.solve(model)

# Store value
rangeStore.append([value(model.x[i]) for i in range(len(coeffs))])