How to use abs function in a objective function of JuMP + Julia - optimization

Question

I would like to solve a simple linear optimization problem with JuMP and Julia.
This is my code:
using JuMP
using Mosek
model = Model(solver=MosekSolver())
#variable(model, 2.5 <= z1 <= 5.0)
#variable(model, -1.0 <= z2 <= 1.0)
#objective(model, Min, abs(z1+5.0) + abs(z2-3.0))
status = solve(model)
println("Objective value: ", getobjectivevalue(model))
println("z1:",getvalue(z1))
println("z2:",getvalue(z2))
However, I got this error message.
> ERROR: LoadError: MethodError: no method matching
> abs(::JuMP.GenericAffExpr{Float64,JuMP.Variable}) Closest candidates
> are: abs(!Matched::Bool) at bool.jl:77 abs(!Matched::Float16) at
> float.jl:512 abs(!Matched::Float32) at float.jl:513
How can I use abs function in the JuMP code?

Answer 1

My problem is solved by #rickhg12hs's commnet.
If I use #NLobjective instead of #objective, It works.
This is the final code.
using JuMP
using Mosek
model = Model(solver=MosekSolver())
#variable(model, 2.5 <= z1 <= 5.0)
#variable(model, -1.0 <= z2 <= 1.0)
#NLobjective(model, Min, abs(z1+5.0) + abs(z2-3.0))
status = solve(model)
println("Objective value: ", getobjectivevalue(model))
println("z1:",getvalue(z1))
println("z2:",getvalue(z2))

Answer 2

I did it on a diffrent way
AvgOperationtime = [1 2]#[2.0 2.0 2.0 3.3333333333333335 2.5 2.0 2.0 2.5 2.5 2.0 2.0]
Operationsnumberremovecounter = [1 0;1 1]#[1.0 1.0 1.0 1.0 -0.0 1.0 1.0 1.0 -0.0 1.0 1.0; 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0; 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0; 1.0 1.0 1.0 -0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0; 1.0 1.0 1.0 -0.0 1.0 1.0 1.0 -0.0 1.0 1.0 1.0]
Modelnumber = 2
Operationsnumber = 2
Basecaseworkload = 2
y = 0.1
Highestnumber = 999
Solver = GLPK.Optimizer
#Operationtime[1,1 X;0,9 2]
m = Model(with_optimizer(Solver));
#variable(m, Operationtime[1:Modelnumber,1:Operationsnumber]>=0);
#variable(m, Absoluttime[1:Modelnumber,1:Operationsnumber]>=0);
#variable(m, Absolutchoice[1:Modelnumber,1:Operationsnumber,1:2], Bin);
#objective(m, Max, sum(Absoluttime[M,O]*Operationsnumberremovecounter[M,O] for M=1:Modelnumber,O=1:Operationsnumber))
#How much Time can differ
#constraint(m, BorderOperationtime1[M=1:Modelnumber,O=1:Operationsnumber], AvgOperationtime[O]*(1-y) <= Operationtime[M,O]);
#constraint(m, BorderOperationtime2[M=1:Modelnumber,O=1:Operationsnumber], AvgOperationtime[O]*(1+y) >= Operationtime[M,O]);
#Workload
#constraint(m, Worklimit[O=1:Operationsnumber], sum(Operationtime[M,O]*Operationsnumberremovecounter[M,O] for M=1:Modelnumber) == Basecaseworkload);
#Absolut
#constraint(m, Absolutchoice1[M=1:Modelnumber,O=1:Operationsnumber], sum(Absolutchoice[M,O,X] for X=1:2) == 1);
#constraint(m, Absoluttime1[M=1:Modelnumber,O=1:Operationsnumber], Absoluttime[M,O] <= Operationtime[M,O]-AvgOperationtime[O]+Absolutchoice[M,O,1]*Highestnumber);
#constraint(m, Absoluttime2[M=1:Modelnumber,O=1:Operationsnumber], Absoluttime[M,O] <= AvgOperationtime[O]-Operationtime[M,O]+Absolutchoice[M,O,2]*Highestnumber);
optimize!(m);
println("Termination status: ", JuMP.termination_status(m));
println("Primal status: ", JuMP.primal_status(m));