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ab849c9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | """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]
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