README.md

metadata

title: AriaNetwork Facility Location
emoji: 🗺️
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.15.1
app_file: app.py
fullWidth: true
python_version: '3.12'
short_description: Locate warehouses and allocate regional demand
pinned: true
license: apache-2.0
tags:
  - facility-location
  - supply-chain
  - scenario-analysis
  - operations-research
  - data-visualization
datasets:
  - AriaAICompany/aria-network-scenarios

AriaNetwork — Warehouse Location and Allocation

Aria AI industrial decision-support demo. A distributor must choose which candidate warehouses to open and which demand region each one serves. The cheapest geometric site is not always feasible once capacity and service hours are applied.

The MVP is a one-period capacitated facility location model: 8 candidates, 36 regions, synthetic fixed costs, and a documented lane matrix. Uncovered demand is allowed unless full coverage is required.

What you can do

1. Load the 8-site / 36-region sample.
2. Compare the HiGHS MIP with one central warehouse and nearest feasible assignment.
3. Stress the network with a +35% demand surge or a Tehran DC outage.
4. Sweep the maximum number of open sites and inspect the cost / coverage trade-off.
5. Download assignments, uncovered demand, and the JSON report.

CPU only. No paid API. Travel hours are distance ÷ 55 km/h. Fixed and freight figures are synthetic units, not rents or quoted tariffs.

Hosting

• Live app: this Space (personal PRO compute if the organization cannot host Gradio).
• Dataset: AriaAICompany/aria-network-scenarios.
• Organization card: AriaAICompany/aria-network.

Persian

آریاشبکه از میان محل‌های نامزد انتخاب می‌کند کدام انبار باز شود و تقاضای هر ناحیه از کجا تأمین شود. ساعت سفر از سرعت فرضی جاده است. هزینه املاک یا حمل واقعی از داده ساختگی استنتاج نمی‌شود.

Links

• AriaAICompany
• aria-ai.ir
• Roadmap project 10 of 25