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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | """Synthetic instance generators for all registered problem types."""
from __future__ import annotations
import hashlib
import random
from typing import Any
from optos.constants import PROBLEM_TYPES, SIZE_PRESETS
from optos.models import InstanceFeatures, ProblemInstance
def _rng(seed: int) -> random.Random:
return random.Random(seed)
def _instance_id(problem_type: str, size: str, seed: int) -> str:
raw = f"{problem_type}_{size}_{seed}"
return hashlib.md5(raw.encode()).hexdigest()[:12]
def _scale_n(base: int, size: str) -> int:
return max(2, int(base * SIZE_PRESETS[size]["scale"]))
def generate_scheduling(rng: random.Random, size: str) -> dict[str, Any]:
n_jobs = _scale_n(6, size)
n_machines = _scale_n(4, size)
processing_times = [
[rng.randint(2, 12) for _ in range(n_machines)]
for _ in range(n_jobs)
]
machine_order = [
list(range(n_machines))
for _ in range(n_jobs)
]
return {
"n_jobs": n_jobs,
"n_machines": n_machines,
"processing_times": processing_times,
"machine_order": machine_order,
}
def generate_routing(rng: random.Random, size: str) -> dict[str, Any]:
n_customers = _scale_n(12, size)
n_vehicles = max(2, n_customers // 4)
depot = (50.0, 50.0)
customers = [
(rng.uniform(0, 100), rng.uniform(0, 100))
for _ in range(n_customers)
]
demands = [rng.randint(1, 10) for _ in range(n_customers)]
vehicle_capacity = max(demands) * 3
return {
"n_customers": n_customers,
"n_vehicles": n_vehicles,
"depot": depot,
"customers": customers,
"demands": demands,
"vehicle_capacity": vehicle_capacity,
}
def generate_assignment(rng: random.Random, size: str) -> dict[str, Any]:
n = _scale_n(8, size)
cost_matrix = [
[rng.randint(1, 50) for _ in range(n)]
for _ in range(n)
]
return {"n_agents": n, "cost_matrix": cost_matrix}
def generate_inventory(rng: random.Random, size: str) -> dict[str, Any]:
n_items = _scale_n(10, size)
horizon = _scale_n(14, size)
demand = [
[rng.randint(5, 30) for _ in range(horizon)]
for _ in range(n_items)
]
holding_cost = [rng.uniform(0.5, 2.0) for _ in range(n_items)]
stockout_cost = [rng.uniform(5.0, 20.0) for _ in range(n_items)]
order_cost = [rng.uniform(10.0, 50.0) for _ in range(n_items)]
initial_stock = [rng.randint(10, 40) for _ in range(n_items)]
return {
"n_items": n_items,
"horizon": horizon,
"demand": demand,
"holding_cost": holding_cost,
"stockout_cost": stockout_cost,
"order_cost": order_cost,
"initial_stock": initial_stock,
"max_order": [max(d) * 2 for d in demand],
}
def generate_facility_location(rng: random.Random, size: str) -> dict[str, Any]:
n_facilities = _scale_n(6, size)
n_customers = _scale_n(15, size)
fixed_costs = [rng.randint(100, 500) for _ in range(n_facilities)]
transport_costs = [
[rng.randint(1, 30) for _ in range(n_facilities)]
for _ in range(n_customers)
]
return {
"n_facilities": n_facilities,
"n_customers": n_customers,
"fixed_costs": fixed_costs,
"transport_costs": transport_costs,
}
def generate_packing(rng: random.Random, size: str) -> dict[str, Any]:
n_items = _scale_n(20, size)
bin_capacity = 100
item_sizes = [rng.randint(10, 45) for _ in range(n_items)]
return {
"n_items": n_items,
"bin_capacity": bin_capacity,
"item_sizes": item_sizes,
}
GENERATORS = {
"scheduling": generate_scheduling,
"routing": generate_routing,
"assignment": generate_assignment,
"inventory": generate_inventory,
"facility_location": generate_facility_location,
"packing": generate_packing,
}
def _estimate_features(problem_type: str, data: dict[str, Any]) -> InstanceFeatures:
if problem_type == "scheduling":
n_vars = data["n_jobs"] * data["n_machines"] * 2
n_cons = data["n_jobs"] * (data["n_machines"] - 1) + data["n_machines"]
elif problem_type == "routing":
n_vars = data["n_customers"] * data["n_vehicles"]
n_cons = data["n_customers"] + data["n_vehicles"]
elif problem_type == "assignment":
n = data["n_agents"]
n_vars = n * n
n_cons = 2 * n
elif problem_type == "inventory":
n_vars = data["n_items"] * data["horizon"]
n_cons = data["n_items"] * data["horizon"]
elif problem_type == "facility_location":
nf, nc = data["n_facilities"], data["n_customers"]
n_vars = nf + nf * nc
n_cons = nc + nf * nc
elif problem_type == "packing":
n_vars = data["n_items"] * data["n_items"]
n_cons = data["n_items"] + data["n_items"]
else:
n_vars, n_cons = 10, 10
return InstanceFeatures(
n_variables=n_vars,
n_constraints=n_cons,
density=round(min(1.0, n_cons / max(n_vars, 1)), 3),
pct_integer=0.85,
constraint_tightness=round(random.Random(0).uniform(0.4, 0.8), 3),
)
def generate_instance(
problem_type: str,
size: str = "medium",
seed: int = 42,
constraints: dict[str, Any] | None = None,
objectives: dict[str, Any] | None = None,
) -> ProblemInstance:
if problem_type not in PROBLEM_TYPES:
raise ValueError(f"Unknown problem type: {problem_type}")
rng = _rng(seed)
data = GENERATORS[problem_type](rng, size)
features = _estimate_features(problem_type, data)
meta = PROBLEM_TYPES[problem_type]
label = f"{meta['label']} · {SIZE_PRESETS.get(size, {}).get('label', size)} · seed={seed}"
return ProblemInstance(
problem_type=problem_type,
instance_id=_instance_id(problem_type, size, seed),
label=label,
size=size,
seed=seed,
data=data,
features=features,
constraints=constraints or {},
objectives=objectives or {"primary": meta.get("objective", "minimize")},
)
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