# Copyright 2011 Hakan Kjellerstrand hakank@bonetmail.com
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Volsay problem in Google or-tools.
From the OPL model volsay.mod
Using arrays.
This model was created by Hakan Kjellerstrand (hakank@bonetmail.com)
Also see my other Google CP Solver models:
http://www.hakank.org/google_or_tools/
"""
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('CoinsGridCLP',
# pywraplp.Solver.CLP_LINEAR_PROGRAMMING)
# 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(),
print 'ReducedCost = ', production[i].ReducedCost()
print
print 'walltime :', solver.WallTime(), 'ms'
print 'iterations:', solver.Iterations()
if __name__ == '__main__':
main('Volsay')