Aria AI Operations Research Portfolio
Collection
Enterprise OR, optimization, and decomposition demos by Aria AI • 151 items • Updated
benchmarks list | solver_metadata dict |
|---|---|
[
{
"industry": "industrial_equipment",
"stress": "baseline",
"algorithm": "two_stage_stochastic",
"sla_compliance_pct": 100,
"mttr_hours": 4.31,
"ftf_rate_pct": 100,
"logistics_cost": 597.53,
"downtime_cost": 17622.45,
"solve_time_sec": 0.011503599991556257
},
{
"industry"... | {
"ml_engine": "pre-trained logistic + duration model",
"optimization": [
"two_stage_stochastic",
"multi_echelon",
"or_tools",
"alns",
"saa"
],
"simulation": "monte_carlo"
} |
FieldOps Sample Scenarios
Synthetic field service optimization scenarios for the FieldOps Service Parts & Field Operations Optimizer.
Contents
| File | Description |
|---|---|
sample_industrial_equipment.json |
Industrial equipment baseline scenario |
sample_medical_equipment.json |
Medical equipment emergency surge |
sample_telecom.json |
Telecom parts shortage scenario |
sample_data_center.json |
Data center technician shortage |
sample_elevators.json |
Elevator peak season demand |
manifest.json |
Dataset manifest |
eval_results.json |
Benchmark evaluation results |
Schema
Each sample contains:
scenario_label— human-readable scenario namesummary— network size (sites, technicians, warehouses, requests)optimization— full optimization result with ML predictions, assignments, metrics, and simulation
Generation
Scenarios are generated deterministically with seed=42 using the FieldOps synthetic generator.
python scripts/build_assets.py
- Downloads last month
- 90