No method matching error

Specific Domains Optimization (Mathematical)
1

Hello everyone,

I’m trying to build a mathematical model to minimize the variance of 4 variables. However, when I try to compute the variance julia have returned the follow error:

ERROR: MethodError: no method matching /(::Base.Generator{Base.Iterators.ProductIterator{Tuple{UnitRange{Int64}, UnitRange{Int64}, UnitRange{Int64}}}, var"#354#361"{AffExpr, Array{VariableRef, 3}}}, ::Int64)

Closest candidates are:
/(::Union{Int128, Int16, Int32, Int64, Int8, UInt128, UInt16, UInt32, UInt64, UInt8}, ::Union{Int128, Int16, Int32, Int64, Int8, UInt128, UInt16, UInt32, UInt64, UInt8})
@ Base int.jl:97
/(::StridedArray{P}, ::Real) where P<:Dates.Period
@ Dates C:\Julia-1.9.1\share\julia\stdlib\v1.9\Dates\src\deprecated.jl:44
/(::Union{SparseArrays.AbstractCompressedVector{Tv, Ti}, SubArray{Tv, 1, <:SparseArrays.AbstractSparseMatrixCSC{Tv, Ti}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, false}, SubArray{Tv, 1, <:SparseArrays.AbstractSparseVector{Tv, Ti}, Tuple{Base.Slice{Base.OneTo{Int64}}}, false}} where {Tv, Ti}, ::Number)
@ SparseArrays C:\Julia-1.9.1\share\julia\stdlib\v1.9\SparseArrays\src\sparsevector.jl:1654
…
Can anyone help me, please?

===================
Code:

using JuMP, Gurobi
import DelimitedFiles

function solve()
#Parametros
members = 30 #Team Members
hours = 24 #Hours of the day
days = 7 #Days of the week
weeks = 4 #Weeks of the month
shifts = 13 #Shifts and Shifts Names
shifts_name = [“RRTWD_1”, “RRTWD_2”, “RRTWD_3”, “RRTWE_1”, “RRTWE_2”,
“MOCWD_1”, “MOCWD_2”, “MOCWD_3”, “MOCWE_1”, “MOCWE_2”,
“NSWD_1”, “NSWE_1”, “NSWE_2”]
resilienceScore = [ 2334.94, 4071.01, 2187.28, 465.52, 1294.25, 1804.09, 6783.05,
2656.37, 2142.94, 433.23, 1403.59, 93.33, 148.42, 174.89, 431.78,
212.36, 107.98, 433.23, 87.26, 240.4, 5785.67, 702.58, 589.05,
364.28, 825.32, 1078.85, 647.19, 1064.08, 1048.19, 483.3 ]
#Setup Model
model = Model(Gurobi.Optimizer)
set_optimizer_attribute(model, “TimeLimit”, 3600)
set_optimizer_attribute(model, “Presolve”, 1)
set_optimizer_attribute(model, “IntFeasTol”, 1e-6)
#set_optimizer_attribute(model, “LogFile”, “logGUROBI-“sheetname”.txt”)

#Decision variables
@variable(model, x[m = 1:members, s = 1:shifts, w = 1:weeks, d = 1:days], Bin)
@variable(model, avg_1[w = 1:weeks, d = 1:days, h = 1:hours] >= 0)
@variable(model, avg_2[w = 1:weeks, d = 1:days, h = 1:hours] >= 0)
@variable(model, avg_3[w = 1:weeks, d = 1:days, h = 1:hours] >= 0)
@variable(model, grand_avg[w = 1:weeks, d = 1:days, h = 1:hours] >= 0)

#Mean and Variance
@expression(model, mean_avg_1, sum(avg_1[w,d,h] for w = 1:4, d = 1:7, h = 1:24)/(4+7+24))
@expression(model, mean_avg_2, sum(avg_2[w,d,h] for w = 1:4, d = 1:7, h = 1:24)/(4+7+24))
@expression(model, mean_avg_3, sum(avg_3[w,d,h] for w = 1:4, d = 1:7, h = 1:24)/(4+7+24))
@expression(model, mean_grand_avg, sum(grand_avg[w,d,h] for w = 1:4, d = 1:7, h = 1:24)/(4+7+24))
@expression(model, variance_avg_1, ((sum((avg_1[w,d,h] - mean_avg_1)^2) for w = 1:4, d = 1:7, h = 1:24)/(4+7+23)))
@expression(model, variance_avg_2, ((sum((avg_2[w,d,h] - mean_avg_2)^2) for w = 1:4, d = 1:7, h = 1:24)/(4+7+23)))
@expression(model, variance_avg_3, ((sum((avg_3[w,d,h] - mean_avg_3)^2) for w = 1:4, d = 1:7, h = 1:24)/(4+7+23)))
@expression(model, variance_grand_avg, ((sum((grand_avg[w,d,h] - mean_grand_avg)^2) for w = 1:4, d = 1:7, h = 1:24)/(4+7+23)))

#Objective
@objective(model, Min, variance_avg_1 + variance_avg_2 + variance_avg_3 + variance_grand_avg)
                      

#Print Model
open("model.txt", "w") do f
    println(f, model)
end

#-----------------------------------------------------
println("solving mathematical model...")
optimize!(model)
status = termination_status(model)

end

Thank you

1 Like
2

Take a look at your brackets:

@expression(
    model,
    variance_avg_1, 
    (
        (
            sum(
                (avg_1[w,d,h] - mean_avg_1)^2
            ) for w = 1:4, d = 1:7, h = 1:24
        ) / (4+7+23)
    )
)

I’m guessing this isn’t what you want :smile:

@expression(
    model,
    variance_avg_1, 
    sum(
        (avg_1[w,d,h] - mean_avg_1)^2 for w = 1:4, d = 1:7, h = 1:24
    ) / (4+7+23)
)

You could simplify to

@expression(model, variance_avg_1, sum((avg_1 .- mean_avg_1).^2) / (4+7+23))

p.s., you can format your code with

```Julia
code
```

It might also help your debugging to try and find a minimal example that reproduces the error, which line is the problem, etc.

3

Lol, sorry for my mistake. Thank you very much for the answer.

Using dots before the operators is an easy way to involve all the elements of the array, isn’t it? I didn’t know this feature, thanks for the tip.

1 Like