# Tuning parameters
**URL:** https://discourse.julialang.org/t/tuning-parameters/108573
**Category:** Optimization (Mathematical)
**Tags:** gurobi
**Created:** [January 9, 2024, 4:44pm UTC](https://discourse.julialang.org/t/tuning-parameters/108573 "2024-01-09T16:44:48Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@angeloaliano1](https://discourse.julialang.org/u/angeloaliano1)
#### Post date: [January 9, 2024, 4:44pm UTC](https://discourse.julialang.org/t/tuning-parameters/108573/1 "2024-01-09T16:44:48Z")
I need to solve a hard MIP model using Gurobi. There is a tool to set the best parameters combination to solve this problem called “Parameter Tunning”. How can I find parameter tuning using Julia?
Here, I present a simple example:
```julia
Random.seed!(2022)
T=10
alpha=rand(1:10,T)
beta=rand(1:10,T)
d=rand(50:100,T)
M=sum(d)
w=0.9999
modelo = Model(Gurobi.Optimizer)
set_optimizer_attribute(modelo, "seconds", 60.0)
#
@variable(modelo, x[i in 1:T] >=0)
@variable(modelo, y[i in 1:T], Bin)
@variable(modelo, e[i in 1:T] >=0)
#
@objective(modelo, Min, w*sum(alpha[j]*e[j] for j in 1:T) + (1-w)*sum(beta[j]*y[j] for j in 1:T))
#
@constraint(modelo,x[1] - e[1] == d[1])
@constraint(modelo,[j in 2:T],x[j] - e[j] + e[j-1] == d[j])
@constraint(modelo,[j in 1:T],x[j] <= M*y[j])
#
optimize!(modelo)
#
z1 = sum(alpha[j]*value(e[j]) for j in 1:T)
z2 = sum(beta[j]*value(y[j]) for j in 1:T)
Z = [z1;z2]
```
---
### Author:  [@odow](https://discourse.julialang.org/u/odow)
#### Post date: [January 9, 2024, 8:44pm UTC](https://discourse.julialang.org/t/tuning-parameters/108573/2 "2024-01-09T20:44:45Z")
The easiest way is probably to write the model to file with
```julia
write_to_file(modelo, "benchmark.mps")
```
and then use:
> **[Command-Line Tuning - Gurobi Optimization](https://www.gurobi.com/documentation/current/refman/command_line_tuning.html)**
>
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