What is the process of incorporating multiple choice limitations into cp_model using ortools?

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To incorporate multiple choice limitations into the cp_model using ortools, you can follow these steps:

1. Define the decision variables and their domains.
2. Define the constraints for the multiple-choice limitations using the add_maxed_out_constraint function.
3. For each multiple-choice constraint, define a list of decision variables that are allowed to be chosen together.
4. Add the constraints to the model using the add_constraints function.

Here is an example code snippet:

from ortools.sat.python import cp_model

model = cp_model.CpModel()

# Define decision variables and domains
A = model.NewIntVar(0, 1, 'A')
B = model.NewIntVar(0, 1, 'B')
C = model.NewIntVar(0, 1, 'C')

# Define multiple-choice constraint
maxed_out = [(A, B), (B, C)]

# Add multiple-choice constraint to the model
model.add_maxed_out_constraint(maxed_out)

# Define objective function (optional)
model.Maximize(A + 2 * B + 3 * C)

# Create solver and solve model
solver = cp_model.CpSolver()
status = solver.Solve(model)

# Print results
if status == cp_model.OPTIMAL:
    print('A:', solver.Value(A))
    print('B:', solver.Value(B))
    print('C:', solver.Value(C))
    print('Optimal objective value:', solver.ObjectiveValue())
else:
    print('No solution found.')

In this example, the multiple-choice constraint limits the number of variables that can be set to 1 at the same time. Specifically, either A and B can be set to 1 together, or B and C can be set to 1 together, but not all three simultaneously. The objective function is set to maximize the sum of the values of the decision variables. The solver is then used to find the optimal solution to the model.