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