OMPR (Optimization Modeling Package) is a DSL to model and solve Mixed Integer Linear Programs. It is inspired by the excellent Jump project in Julia.

Here are some problems you could solve with this package:

- What is the cost minimal way to visit a set of clients and return home afterwards?
- What is the optimal conference time table subject to certain constraints (e.g. availability of a projector)?
- Sudokus

The Wikipedia article gives a good starting point if you would like to learn more about the topic.

I am always happy to get bug reports or feedback.

```
install.packages("ompr")
install.packages("ompr.roi")
```

To install the current development version use devtools:

```
remotes::install_github("dirkschumacher/ompr")
remotes::install_github("dirkschumacher/ompr.roi")
```

- ompr.roi - Bindings to ROI (GLPK, Symphony, CPLEX etc.)

```
suppressPackageStartupMessages(library(dplyr, quietly = TRUE))
suppressPackageStartupMessages(library(ROI))
library(ROI.plugin.glpk)
library(ompr)
library(ompr.roi)
result <- MIPModel() |>
add_variable(x, type = "integer") |>
add_variable(y, type = "continuous", lb = 0) |>
set_bounds(x, lb = 0) |>
set_objective(x + y, "max") |>
add_constraint(x + y <= 11.25) |>
solve_model(with_ROI(solver = "glpk"))
get_solution(result, x)
#> x
#> 11
get_solution(result, y)
#> y
#> 0.25
```

These functions currently form the public API. More detailed docs can be found in the package function docs or on the website

`MIPModel()`

create an empty mixed integer linear model (the old way)`add_variable()`

adds variables to a model`set_objective()`

sets the objective function of a model`set_bounds()`

sets bounds of variables`add_constraint()`

add constraints`solve_model()`

solves a model with a given solver`get_solution()`

returns the column solution (primal or dual) of a solved model for a given variable or group of variables`get_row_duals()`

returns the row duals of a solution (only if it is an LP)`get_column_duals()`

returns the column duals of a solution (only if it is an LP)

There are currently two backends. A backend is the function that initializes an empty model.

`MIPModel()`

is the standard MILP Model.`MILPModel()`

is another backend specifically optimized for linear models and is often faster than`MIPModel()`

. It has different semantics, as it is vectorized. Currently experimental and might be deprecated in the future.

Solvers are in different packages. `ompr.ROI`

uses the ROI package which
offers support for all kinds of solvers.

`with_ROI(solver = "glpk")`

solve the model with GLPK. Install`ROI.plugin.glpk`

`with_ROI(solver = "symphony")`

solve the model with Symphony. Install`ROI.plugin.symphony`

`with_ROI(solver = "cplex")`

solve the model with CPLEX. Install`ROI.plugin.cplex`

- … See the ROI package for more plugins.

Please take a look at the docs for bigger examples.

```
max_capacity <- 5
n <- 10
set.seed(1234)
weights <- runif(n, max = max_capacity)
MIPModel() |>
add_variable(x[i], i = 1:n, type = "binary") |>
set_objective(sum_over(weights[i] * x[i], i = 1:n), "max") |>
add_constraint(sum_over(weights[i] * x[i], i = 1:n) <= max_capacity) |>
solve_model(with_ROI(solver = "glpk")) |>
get_solution(x[i]) |>
filter(value > 0)
#> variable i value
#> 1 x 1 1
#> 2 x 6 1
#> 3 x 7 1
#> 4 x 8 1
```

An example of a more difficult model solved by GLPK

```
max_bins <- 10
bin_size <- 3
n <- 10
weights <- runif(n, max = bin_size)
MIPModel() |>
add_variable(y[i], i = 1:max_bins, type = "binary") |>
add_variable(x[i, j], i = 1:max_bins, j = 1:n, type = "binary") |>
set_objective(sum_over(y[i], i = 1:max_bins), "min") |>
add_constraint(sum_over(weights[j] * x[i, j], j = 1:n) <= y[i] * bin_size, i = 1:max_bins) |>
add_constraint(sum_over(x[i, j], i = 1:max_bins) == 1, j = 1:n) |>
solve_model(with_ROI(solver = "glpk", verbose = TRUE)) |>
get_solution(x[i, j]) |>
filter(value > 0) |>
arrange(i)
#> <SOLVER MSG> ----
#> GLPK Simplex Optimizer, v4.65
#> 20 rows, 110 columns, 210 non-zeros
#> 0: obj = 0.000000000e+00 inf = 1.000e+01 (10)
#> 29: obj = 4.546337429e+00 inf = 0.000e+00 (0)
#> * 34: obj = 4.546337429e+00 inf = 0.000e+00 (0)
#> OPTIMAL LP SOLUTION FOUND
#> GLPK Integer Optimizer, v4.65
#> 20 rows, 110 columns, 210 non-zeros
#> 110 integer variables, all of which are binary
#> Integer optimization begins...
#> Long-step dual simplex will be used
#> + 34: mip = not found yet >= -inf (1; 0)
#> + 62: >>>>> 5.000000000e+00 >= 5.000000000e+00 0.0% (13; 0)
#> + 62: mip = 5.000000000e+00 >= tree is empty 0.0% (0; 25)
#> INTEGER OPTIMAL SOLUTION FOUND
#> <!SOLVER MSG> ----
#> variable i j value
#> 1 x 1 2 1
#> 2 x 1 9 1
#> 3 x 1 10 1
#> 4 x 2 5 1
#> 5 x 2 7 1
#> 6 x 2 8 1
#> 7 x 3 6 1
#> 8 x 4 4 1
#> 9 x 10 1 1
#> 10 x 10 3 1
```

MIT

Please post an issue first before sending a PR.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.