# Copyright 2010-2017 Google
# 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.
from __future__ import print_function
from ortools.sat.python import cp_model


# Data.
cost = [[90, 76, 75, 70, 50, 74], [35, 85, 55, 65, 48,
                                   101], [125, 95, 90, 105, 59, 120],
        [45, 110, 95, 115, 104, 83], [60, 105, 80, 75, 59, 62], [
            45, 65, 110, 95, 47, 31
        ], [38, 51, 107, 41, 69, 99], [47, 85, 57, 71,
                                       92, 77], [39, 63, 97, 49, 118, 56],
        [47, 101, 71, 60, 88, 109], [17, 39, 103, 64, 61,
                                     92], [101, 45, 83, 59, 92, 27]]

group1 = [
    [0, 0, 1, 1],  # Workers 2, 3
    [0, 1, 0, 1],  # Workers 1, 3
    [0, 1, 1, 0],  # Workers 1, 2
    [1, 1, 0, 0],  # Workers 0, 1
    [1, 0, 1, 0]
]  # Workers 0, 2

group2 = [
    [0, 0, 1, 1],  # Workers 6, 7
    [0, 1, 0, 1],  # Workers 5, 7
    [0, 1, 1, 0],  # Workers 5, 6
    [1, 1, 0, 0],  # Workers 4, 5
    [1, 0, 0, 1]
]  # Workers 4, 7

group3 = [
    [0, 0, 1, 1],  # Workers 10, 11
    [0, 1, 0, 1],  # Workers 9, 11
    [0, 1, 1, 0],  # Workers 9, 10
    [1, 0, 1, 0],  # Workers 8, 10
    [1, 0, 0, 1]
]  # Workers 8, 11

sizes = [10, 7, 3, 12, 15, 4, 11, 5]
total_size_max = 15
num_workers = len(cost)
num_tasks = len(cost[1])
all_workers = range(num_workers)
all_tasks = range(num_tasks)

# Model.

model = cp_model.CpModel()
# Variables
total_cost = model.NewIntVar(0, 1000, 'total_cost')
x = [[model.NewBoolVar('x[%i,%i]' % (i, j))
      for j in all_tasks]
     for i in all_workers]
works = [model.NewBoolVar('works[%i]' % i) for i in all_workers]

# Constraints

# Link x and workers.
for i in range(num_workers):
  model.AddMaxEquality(works[i], x[i])

# Each task is assigned to at least one worker.
for j in all_tasks:
  model.Add(sum(x[i][j] for i in all_workers) >= 1)

# Total task size for each worker is at most total_size_max
for i in all_workers:
  model.Add(sum(sizes[j] * x[i][j] for j in all_tasks) <= total_size_max)

# Group constraints.
model.AddAllowedAssignments([works[0], works[1], works[2], works[3]], group1)
model.AddAllowedAssignments([works[4], works[5], works[6], works[7]], group2)
model.AddAllowedAssignments([works[8], works[9], works[10], works[11]],
                            group3)

# Total cost
model.Add(total_cost == sum(
    x[i][j] * cost[i][j] for j in all_tasks for i in all_workers))
model.Minimize(total_cost)

# Solve and output solution.
solver = cp_model.CpSolver()
status = solver.Solve(model)

if status == cp_model.OPTIMAL:
  print('Total cost = %i' % solver.ObjectiveValue())
  print()
  for i in all_workers:
    for j in all_tasks:
      if solver.Value(x[i][j]) == 1:
        print('Worker ', i, ' assigned to task ', j, '  Cost = ', cost[i][j])

  print()

print('Statistics')
print('  - conflicts : %i' % solver.NumConflicts())
print('  - branches  : %i' % solver.NumBranches())
print('  - wall time : %f ms' % solver.WallTime())