irt-service/app/models/objective_function.py

62 lines
2.2 KiB
Python

from __future__ import annotations
from pydantic import BaseModel
from typing import Dict, List, AnyStr
from pulp import lpSum
from models.targets.tif_target import TifTarget
from models.targets.tcc_target import TccTarget
from models.problem import Problem
class ObjectiveFunction(BaseModel):
# minimizing tif/tcc target value is only option currently
# as we add more we can build this out to be more dynamic
# likely with models representing each objective function type
tif_targets: List[TifTarget]
tcc_targets: List[TccTarget]
target_variance_percentage: int = 10
objective: AnyStr = "minimize"
weight: Dict = {'tif': 1, 'tcc': 1}
def for_problem(self, problem_handler: Problem) -> None:
problem_handler.problem += lpSum([
bundle.count * problem_handler.solver_bundles_var[bundle.id]
for bundle in problem_handler.bundles
] + [
problem_handler.solver_items_var[item.id]
for item in problem_handler.items
])
def increment_targets_drift(self,
limit: float or bool,
all: bool = False,
amount: float = 0.1,
targets: list[TifTarget|TccTarget] = []) -> bool:
if all:
for target in self.tif_targets:
target.drift = round(target.drift + amount, 2)
for target in self.tcc_targets:
target.drift = round(target.drift + amount, 2)
else:
for target in targets:
target.drift = round(target.drift + amount, 2)
return amount
def update_targets_drift(self, amount: float = 0.0):
for target in self.tif_targets:
target.drift = round(amount, 2)
for target in self.tcc_targets:
target.drift = round(amount, 2)
def minimum_drift(self) -> float:
minimum_drift = 0.0
for target in self.all_targets():
if target.drift < minimum_drift:
minimum_drift = target.drift
return minimum_drift
def all_targets(self) -> list[TifTarget|TccTarget]:
return self.tif_targets + self.tcc_targets