# Copyright 2011 Hakan Kjellerstrand hakank@gmail.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@gmail.com) Also see my other Google CP Solver models: http://www.hakank.org/google_or_tools/ """ from __future__ import print_function 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 Gas = 0 Chloride = 1 products = ['Gas', 'Chloride'] # declare variables production = [ solver.NumVar(0, 100000, 'production[%i]' % i) for i in range(num_products) ] # # constraints # solver.Add(production[Gas] + production[Chloride] <= 50) solver.Add(3 * production[Gas] + 4 * production[Chloride] <= 180) # objective objective = solver.Maximize(40 * production[Gas] + 50 * production[Chloride]) print('NumConstraints:', solver.NumConstraints()) # # 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()) if __name__ == '__main__': main('Volsay')