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"""Scenario engine — perturb instances for stress testing."""
from __future__ import annotations
import copy
import random
from optos.constants import SCENARIO_TYPES
from optos.engine import OptimizationEngine
from optos.models import ProblemInstance, ScenarioResult
class ScenarioEngine:
def __init__(self, time_limit_sec: float = 12.0) -> None:
self.engine = OptimizationEngine(time_limit_sec)
self.rng = random.Random(42)
def apply_scenario(
self,
instance: ProblemInstance,
scenario_type: str,
seed: int = 42,
) -> tuple[ProblemInstance, dict]:
rng = random.Random(seed)
data = copy.deepcopy(instance.data)
perturbation: dict = {"scenario_type": scenario_type}
if scenario_type == "capacity_change":
factor = rng.uniform(0.6, 1.4)
if "vehicle_capacity" in data:
data["vehicle_capacity"] = int(data["vehicle_capacity"] * factor)
elif "bin_capacity" in data:
data["bin_capacity"] = int(data["bin_capacity"] * factor)
perturbation["capacity_factor"] = factor
elif scenario_type == "demand_shift":
factor = rng.uniform(0.7, 1.5)
if "demands" in data:
data["demands"] = [max(1, int(d * factor)) for d in data["demands"]]
elif "demand" in data:
data["demand"] = [
[max(1, int(d * factor)) for d in row]
for row in data["demand"]
]
perturbation["demand_factor"] = factor
elif scenario_type == "resource_removal":
pct = rng.uniform(0.05, 0.25)
if "n_vehicles" in data:
remove = max(1, int(data["n_vehicles"] * pct))
data["n_vehicles"] = max(1, data["n_vehicles"] - remove)
elif "n_facilities" in data:
remove = max(1, int(data["n_facilities"] * pct))
data["n_facilities"] = max(2, data["n_facilities"] - remove)
perturbation["removed_pct"] = pct
elif scenario_type == "cost_increase":
factor = rng.uniform(1.1, 2.0)
if "fixed_costs" in data:
data["fixed_costs"] = [int(c * factor) for c in data["fixed_costs"]]
elif "cost_matrix" in data:
data["cost_matrix"] = [
[int(c * factor) for c in row]
for row in data["cost_matrix"]
]
perturbation["cost_factor"] = factor
elif scenario_type == "network_disruption":
pct = rng.uniform(0.1, 0.3)
if "transport_costs" in data:
nc = data["n_customers"]
disrupted = int(nc * pct)
for c in self.rng.sample(range(nc), disrupted):
for f in range(data["n_facilities"]):
data["transport_costs"][c][f] = int(data["transport_costs"][c][f] * 3)
perturbation["disrupted_pct"] = pct
perturbed = ProblemInstance(
problem_type=instance.problem_type,
instance_id=instance.instance_id + f"_sc_{scenario_type}",
label=f"{instance.label} · {SCENARIO_TYPES[scenario_type]['label']}",
size=instance.size,
seed=seed,
data=data,
features=instance.features,
constraints=instance.constraints,
objectives=instance.objectives,
)
return perturbed, perturbation
def run_scenario(
self,
instance: ProblemInstance,
scenario_type: str,
seed: int = 42,
) -> ScenarioResult:
baseline_results = self.engine.solve_instance(instance)
baseline_obj = min(
r.metrics.objective_value for r in baseline_results if r.metrics.feasible
) if any(r.metrics.feasible for r in baseline_results) else 0.0
perturbed, perturbation = self.apply_scenario(instance, scenario_type, seed)
perturbed_results = self.engine.solve_instance(perturbed)
perturbed_obj = min(
r.metrics.objective_value for r in perturbed_results if r.metrics.feasible
) if any(r.metrics.feasible for r in perturbed_results) else 0.0
delta = 0.0
if baseline_obj > 0:
delta = (perturbed_obj - baseline_obj) / baseline_obj * 100
binding = []
if scenario_type == "capacity_change":
binding.append("capacity_constraint")
elif scenario_type == "demand_shift":
binding.append("demand_coverage")
elif scenario_type == "resource_removal":
binding.append("resource_availability")
return ScenarioResult(
scenario_type=scenario_type,
scenario_label=SCENARIO_TYPES[scenario_type]["label"],
perturbation=perturbation,
baseline_objective=baseline_obj,
perturbed_objective=perturbed_obj,
delta_pct=round(delta, 2),
feasible=perturbed_obj > 0,
binding_constraints=binding,
)
def run_all_scenarios(self, instance: ProblemInstance) -> list[ScenarioResult]:
return [self.run_scenario(instance, st) for st in SCENARIO_TYPES]