new_contents: #!/usr/bin/env python3
  # Copyright 2010-2021 Google LLC
  # 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.
  # [START program]
  """Solve assignment problem for given group of workers."""
  # [START import]
  from ortools.linear_solver import pywraplp
  # [END import]
  
  
  def main():
      # Data
      # [START data]
      costs = [
          [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],
      ]
      # [END data]
  
      # Allowed groups of workers:
      # [START allowed_groups]
      group1 = [  # Subgroups of workers 0 - 3
          [2, 3],
          [1, 3],
          [1, 2],
          [0, 1],
          [0, 2],
      ]
  
      group2 = [  # Subgroups of workers 4 - 7
          [6, 7],
          [5, 7],
          [5, 6],
          [4, 5],
          [4, 7],
      ]
  
      group3 = [  # Subgroups of workers 8 - 11
          [10, 11],
          [9, 11],
          [9, 10],
          [8, 10],
          [8, 11],
      ]
  
      allowed_groups = []
      for workers_g1 in group1:
          for workers_g2 in group2:
              for workers_g3 in group3:
                  allowed_groups.append(workers_g1 + workers_g2 + workers_g3)
      # [END allowed_groups]
  
      # [START solves]
      min_val = 1e6
      total_time = 0
      for group in allowed_groups:
          res = assignment(costs, group)
          solver_tmp = res[0]
          x_tmp = res[1]
          if solver_tmp.Objective().Value() < min_val:
              min_val = solver_tmp.Objective().Value()
              min_group = group
              min_solver = solver_tmp
              min_x = x_tmp
          total_time += solver_tmp.WallTime()
      # [END solves]
  
      # Print best solution.
      # [START print_solution]
      if min_val < 1e6:
          print(f'Total cost = {min_solver.Objective().Value()}\n')
          num_tasks = len(costs[0])
          for worker in min_group:
              for task in range(num_tasks):
                  if min_x[worker, task].solution_value() > 0.5:
                      print(f'Worker {worker} assigned to task {task}.' +
                            f' Cost = {costs[worker][task]}')
      else:
          print('No solution found.')
      print(f'Time = {total_time} ms')
      # [END print_solution]
  
  
  def assignment(costs, group):
      """Solve the assignment problem for one allowed group combinaison."""
      num_tasks = len(costs[1])
      # Solver
      # [START solver]
      # Create the mip solver with the SCIP backend.
      solver = pywraplp.Solver.CreateSolver('SCIP')
      # [END solver]
  
      # Variables
      # [START variables]
      # x[worker, task] is an array of 0-1 variables, which will be 1
      # if the worker is assigned to the task.
      x = {}
      for worker in group:
          for task in range(num_tasks):
              x[worker, task] = solver.BoolVar(f'x[{worker},{task}]')
      # [END variables]
  
      # Constraints
      # [START constraints]
      # The total size of the tasks each worker takes on is at most total_size_max.
      for worker in group:
          solver.Add(
              solver.Sum([x[worker, task] for task in range(num_tasks)]) <= 1)
  
      # Each task is assigned to exactly one worker.
      for task in range(num_tasks):
          solver.Add(solver.Sum([x[worker, task] for worker in group]) == 1)
      # [END constraints]
  
      # Objective
      # [START objective]
      objective_terms = []
      for worker in group:
          for task in range(num_tasks):
              objective_terms.append(costs[worker][task] * x[worker, task])
      solver.Minimize(solver.Sum(objective_terms))
      # [END objective]
  
      # Solve
      # [START solve]
      status = solver.Solve()
      # [END solve]
  
      return [status, solver, x]
  
  
  if __name__ == '__main__':
      main()
  # [END program]
  
subject: Add assignment_groups_mip.py