Ruby-Cbc

This gem wraps the Coin-Or Cbc Mixed Integer Linear Programming Library. It uses the version 2.9.7 of Cbc.

Installation

Add this line to your application's Gemfile:

gem 'cbc'

And then execute:

$ bundle

Or install it yourself as:

$ gem install cbc

The gem includes a version of the Coin-Or Cbc library. If the system on which it is installed is not Linux 64bits, it downloads the library sources and recompiles them at installation.

It also works on Heroku.

Usage

# The same Brief Example as found in section 1.3 of 
# glpk-4.44/doc/glpk.pdf.
#
# maximize
#   z = 10 * x1 + 6 * x2 + 4 * x3
#
# subject to
#   p:      x1 +     x2 +     x3 <= 100
#   q: 10 * x1 + 4 * x2 + 5 * x3 <= 600
#   r:  2 * x1 + 2 * x2 + 6 * x3 <= 300
#
# where all variables are non-negative
#   x1 >= 0, x2 >= 0, x3 >= 0
#
m = Cbc::Model.new
x1, x2, x3 = m.int_var_array(3, 0..Cbc::INF)

m.maximize(10 * x1 + 6 * x2 + 4 * x3)

m.enforce(x1 + x2 + x3 <= 100)
m.enforce(10 * x1 + 4 * x2 + 5 * x3 <= 600)
m.enforce(2 * x1 + 2 * x2 + 6* x3 <= 300)

p = m.to_problem

p.solve

if ! p.proven_infeasible?
  puts "x1 = #{p.value_of(x1)}"
  puts "x2 = #{p.value_of(x2)}"
  puts "x3 = #{p.value_of(x3)}"
end

Modelling

Let's have a model :

model = Cbc::Model.new

The variables

3 variable kinds are available:

x = model.bin_var(name: "x")        # a binary variable (i.e. can take values 0 and 1)
y = model.int_var(L..U, name: "y")  # a integer variable, L <= y <= U
z = model.cont_var(L..U, name: "z") # a continuous variable, L <= z <= U

Name is optional and used only when displaying the model.

If you don't specify the range, the variables are free. You can also use Cbc::INF as the infinity bound.

Each one of these 3 kinds have also an array method that generate several variables. For instance to generate 3 positive integer variables named x, y and z :

x, y, z = model.int_var_array(3, 0..Cbc::INF, names: ["x", "y", "z"])

The constraints

You can enforce constraints:

model.enforce(x + y - z <= 10)

You are not restricted to usual linear programming rules when writing a constraint. Usually you would have to write x - y = 0 to express x = y. Ruby-Cbc allows you to put variables and constants on both sides of the comparison operator. You can write

model.enforce(x - y == 0)
model.enforce(x == y)
model.enforce(x + 2 == y + 2)
model.enforce(0 == x - y)

Linear constraints are usually of the form

a1 * x1 + a2 * x2 + ... + an * xn <= C
a1 * x1 + a2 * x2 + ... + an * xn >= C
a1 * x1 + a2 * x2 + ... + an * xn == C

With Ruby-Cbc you can write

2 * (2 + 5 * x) + 4 * 5 + 1 == 1 + 4 * 5

The (in)equation must still be a linear (in)equation, you cannot multiply two variables !

Objective

You can set the objective:

model.maximize(3 * x + 2 * y)
model.minimize(3 * x + 2 * y)

Displaying the model

the Model instances have a to_s method. You can then run

puts model

The model will be printed in LP format.

For instance:

Maximize
  + 10 x1 + 6 x2 + 4 x3

Subject To
  + x1 + x2 + x3 <= 100
  + 10 x1 + 4 x2 + 5 x3 <= 600
  + 2 x1 + 2 x2 + 6 x3 <= 300

Bounds
  0 <= x1 <= +inf
  0 <= x2 <= +inf
  0 <= x3 <= +inf

Generals
  x1
  x2
  x3

End

Solving

To solve the model, you need to first transform it to a problem.

problem = model.to_problem

You can define a time limit to the resolution

problem.set_time_limit(nb_seconds)

You can solve the Linear Problem

problem.solve

You can specify arguments that match the cbc command line

problem.solve(sec: 60) # equivalent to $ cbc -sec 60
problem.solve(log: 1)  # equivalent to $ cbc -log 1

For more examples of available options, if coinor-cbc is installed run

$ cbc

then type ?

Once problem.solve has finished you can query the status:

problem.proven_infeasible?  # will tell you if the problem has no solution
problem.proven_optimal?     # will tell you if the problem is solved optimally
problem.time_limit_reached? # Will tell you if the solve timed out

To have the different values, do

problem.objective_value # Will tell you the value of the best objective
problem.best_bound      # Will tell you the best known bound
                        # if the bound equals the objective value, the problem is optimally solved
problem.value_of(var)   # will tell you the computed value or a variable

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/gverger/ruby-cbc.