To incorporate multiple choice limitations into the cp_model using ortools, you can follow these steps:
add_maxed_out_constraint
function.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.
Asked: 2022-06-07 11:00:00 +0000
Seen: 11 times
Last updated: Jul 01 '21