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volsay3

<table align="left"> <td> <a href="https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/contrib/volsay3.ipynb">Run in Google Colab</a> </td> <td> <a href="https://github.com/google/or-tools/blob/main/examples/contrib/volsay3.py">View source on GitHub</a> </td> </table>

First, you must install ortools package in this colab.

python

%pip install ortools

Volsay problem in Google or-tools.

From the OPL model volsay.mod Using arrays.

This model was created by Hakan Kjellerstrand ([email protected]) Also see my other Google CP Solver models: http://www.hakank.org/google_or_tools/

python

from ortools.linear_solver import pywraplp


def main(unused_argv):

  # Create the solver.

  # using GLPK
  # solver = pywraplp.Solver('CoinsGridGLPK',
  #                          pywraplp.Solver.GLPK_LINEAR_PROGRAMMING)

  # Using CLP
  solver = pywraplp.Solver.CreateSolver('CLP')
  if not solver:
    return

  # data
  num_products = 2

  products = ['Gas', 'Chloride']
  components = ['nitrogen', 'hydrogen', 'chlorine']

  demand = [[1, 3, 0], [1, 4, 1]]
  profit = [30, 40]
  stock = [50, 180, 40]

  # declare variables
  production = [
      solver.NumVar(0, 100000, 'production[%i]' % i)
      for i in range(num_products)
  ]

  #
  # constraints
  #
  for c in range(len(components)):
    solver.Add(
        solver.Sum([demand[p][c] * production[p]
                    for p in range(len(products))]) <= stock[c])

  # objective
  # Note: there is no support for solver.ScalProd in the LP/IP interface
  objective = solver.Maximize(
      solver.Sum([production[p] * profit[p] for p in range(num_products)]))

  print('NumConstraints:', solver.NumConstraints())
  print('NumVariables:', solver.NumVariables())
  print()

  #
  # solution and search
  #
  solver.Solve()

  print()
  print('objective = ', solver.Objective().Value())
  for i in range(num_products):
    print(products[i], '=', production[i].SolutionValue(), end=' ')
    print('ReducedCost = ', production[i].ReducedCost())

  print()
  print('walltime  :', solver.WallTime(), 'ms')
  print('iterations:', solver.Iterations())


main('Volsay')