# Objective function error

I need to reproduce that model.

``````using JuMP, Cbc
Model1 = Model(with_optimizer(Cbc.Optimizer))

a = 2
t = 2
w = 2

A = 1:a
T = 1:t
W = 1:w

CR = [11, 11]

@variable(Model1,r[A,T], lower_bound=0)
@variable(Model1,o[A,T], lower_bound=0)

@objective(Model1,Min,sum((CR[w]*(r[a,t]+o[a,t])) for w in W, a in A, t in T))
``````

But when I try to compile the objective function it adds up the values and gives me the following result:

``````22 r[1,1] + 22 o[1,1] + 22 r[1,2] + 22 o[1,2] + 22 r[2,1] + 22 o[2,1] + 22 r[2,2] + 22 o[2,2]
``````

When should you submit

``````11 r[1,1] + 11 o[1,1] + ....
``````

What should I do?

JuMP will build the model that you formulate. If it builds something that you are not expecting, that means there is an error in your code.

For example, you might be missing `0.5 * sum( ... )`. Or it might be some other error, like you are not meant to sum over `W`.

Your objective function has a `for w in W`, this means for each pair of values `a` and `t` can assume (and, consequently, for each variable you have), you will have a loop with `w == 1` and then another with `w == 2`, so what you do is: `11 r[1,1] + 11 o[1,1] ... 11 r[2,2] + 11 o[2,2]` and then again `11 r[1,1] + 11 o[1,1] ... 11 r[2,2] + 11 o[2,2]`. JuMP simplifies this to the completely equivalent expression `22 r[1,1] + 22 o[1,1] ... 22 r[2,2] + 22 o[2,2]` (a single time). There is no bug in JuMP. Your code is doing exactly what is written it should do. There is no difference between two instances of `11 r[1, 1]` and one instance of `22 r[1, 1]`.