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First, you must install ortools package in this colab.
%pip install ortools
Encodes a convex piecewise linear function.
from ortools.sat.python import cp_model
class VarArraySolutionPrinter(cp_model.CpSolverSolutionCallback):
"""Print intermediate solutions."""
def __init__(self, variables: list[cp_model.IntVar]):
cp_model.CpSolverSolutionCallback.__init__(self)
self.__variables = variables
def on_solution_callback(self) -> None:
for v in self.__variables:
print(f"{v}={self.value(v)}", end=" ")
print()
def earliness_tardiness_cost_sample_sat():
"""Encode the piecewise linear expression."""
earliness_date = 5 # ed.
earliness_cost = 8
lateness_date = 15 # ld.
lateness_cost = 12
# Model.
model = cp_model.CpModel()
# Declare our primary variable.
x = model.new_int_var(0, 20, "x")
# Create the expression variable and implement the piecewise linear function.
#
# \ /
# \______/
# ed ld
#
large_constant = 1000
expr = model.new_int_var(0, large_constant, "expr")
# First segment.
s1 = model.new_int_var(-large_constant, large_constant, "s1")
model.add(s1 == earliness_cost * (earliness_date - x))
# Second segment.
s2 = 0
# Third segment.
s3 = model.new_int_var(-large_constant, large_constant, "s3")
model.add(s3 == lateness_cost * (x - lateness_date))
# Link together expr and x through s1, s2, and s3.
model.add_max_equality(expr, [s1, s2, s3])
# Search for x values in increasing order.
model.add_decision_strategy([x], cp_model.CHOOSE_FIRST, cp_model.SELECT_MIN_VALUE)
# Create a solver and solve with a fixed search.
solver = cp_model.CpSolver()
# Force the solver to follow the decision strategy exactly.
solver.parameters.search_branching = cp_model.FIXED_SEARCH
# Enumerate all solutions.
solver.parameters.enumerate_all_solutions = True
# Search and print out all solutions.
solution_printer = VarArraySolutionPrinter([x, expr])
solver.solve(model, solution_printer)
earliness_tardiness_cost_sample_sat()