OMPR (Optimization Modelling 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:
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.
To install the current development version use devtools:
devtools::install_github("dirkschumacher/ompr")
devtools::install_github("dirkschumacher/ompr.roi")
Package | Description | Build Linux | Build Windows | Test coverage |
---|---|---|---|---|
ompr.roi | Bindings to ROI (GLPK, Symphony, CPLEX etc.) |
library(dplyr)
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)
get_solution(result, y)
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 modeladd_variable()
adds variables to a modelset_objective()
sets the objective function of a modelset_bounds()
sets bounds of variablesadd_constraint()
add constraintssolve_model()
solves a model with a given solverget_solution()
returns the solution of a solved model for a given variable or group of variablesSolvers 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
Please take a look at the docs for bigger examples.
library(dplyr)
library(ROI)
library(ROI.plugin.glpk)
library(ompr)
library(ompr.roi)
max_capacity <- 5
n <- 10
weights <- runif(n, max = max_capacity)
MIPModel() %>%
add_variable(x[i], i = 1:n, type = "binary") %>%
set_objective(sum_expr(weights[i] * x[i], i = 1:n), "max") %>%
add_constraint(sum_expr(weights[i] * x[i], i = 1:n) <= max_capacity) %>%
solve_model(with_ROI(solver = "glpk")) %>%
get_solution(x[i]) %>%
filter(value > 0)
An example of a more difficult model solved by symphony.
library(dplyr)
library(ROI)
library(ROI.plugin.symphony)
library(ompr)
library(ompr.roi)
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_expr(y[i], i = 1:max_bins), "min") %>%
add_constraint(sum_expr(weights[j] * x[i, j], j = 1:n) <= y[i] * bin_size, i = 1:max_bins) %>%
add_constraint(sum_expr(x[i, j], i = 1:max_bins) == 1, j = 1:n) %>%
solve_model(with_ROI(solver = "symphony", verbosity = 1)) %>%
get_solution(x[i, j]) %>%
filter(value > 0) %>%
arrange(i)
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.