metadata
license: mit
tags:
- operations-research
- supply-chain
- inventory-allocation
- omnichannel
- synthetic
task_categories:
- other
Omnichannel Network and Order Scenarios
Five synthetic omnichannel fulfillment networks (one per demo scenario), each with a central distribution center, two regional warehouses, a supplier hub, a cross-dock, a returns/repair/recycle cluster, 24 stores, a 60-SKU catalog with substitute groups, noisy inventory records, and a day's worth of multi-line customer orders — generated with a fixed random seed for reproducibility.
Scenarios
| Key | Description |
|---|---|
peak_season_surge |
Demand spikes 65% while picking capacity stays flat |
regional_stockout |
Regional warehouse inventory collapses, forcing ship-from-store |
high_return_category_wave |
Apparel return rate more than doubles |
capacity_constrained_dc |
Central DC picking capacity cut to 35% |
green_logistics_push |
Corporate mandate to weight carbon heavily in allocation |
Files
| File | Contents |
|---|---|
manifest.json |
Index of all scenario files with entity counts |
sample_<scenario_key>.json |
Full network snapshot: nodes, SKUs, inventory, transport links, orders, scenario events |
Schema
Each sample_*.json file is a serialized Network object:
{
"scenario_id": "...",
"nodes": {"DC1": {"node_id": "...", "node_type": "central_dc", "daily_pick_capacity": 0}},
"skus": {"SKU0000": {"sku_id": "...", "category": "...", "unit_price": 0.0, "substitute_group": "..."}},
"inventory": [{"node_id": "...", "sku_id": "...", "on_hand_qty": 0, "accuracy": 0.0}],
"links": [{"origin_id": "...", "dest_id": "...", "distance_km": 0.0, "ship_mode": "standard"}],
"orders": [{"order_id": "...", "demand_node_id": "...", "priority_class": "...", "lines": []}],
"events": [{"event_type": "...", "description": "...", "affected_node_ids": []}]
}
Generation
Regenerate with:
python scripts/build_assets.py