An enterprise-grade decision engine that combines causal inference, risk-aware stochastic optimization, and a digital twin to recommend the next operational action — and certify the uplift offline before deployment.
Every operations team drowns in dashboards that scream “this shipment will be late” — but none of them tell the manager what to actually do, or what happens if they don't.
The right question is causal: “If I move 200 units from warehouse A to B today, what is the worst-case cost I should expect next month?” Answering that requires do-calculus, tail-risk optimization, and a simulator — not another gradient-boosted ETA.
Smart Logistics is the first open platform to wire all three together into a single closed loop with a fully auditable trail.
A DoWhy average-treatment-effect estimate modulates the stockout penalty of a Rockafellar–Uryasev CVaR linear program. First open implementation.
Post-deployment realized costs are logged and refit via OLS, so penalty sensitivity β adapts to your operation — no manual tuning.
Every prescriptive decision is certified Pareto-improving in a SimPy twin before it ever reaches production.
OR-Tools deterministic CVRP is re-scored with Monte-Carlo CVaR of makespan under travel-time noise for route robustness.
Every box is a tested FastAPI endpoint. Every arrow is an integration test. Every artifact — model, scenario set, decision, KPI uplift — is persisted in MLflow as experimental evidence.
The live demo runs API + UI + Postgres + Redis on Vercel and Render. The full monitoring stack (MLflow, Prometheus, Grafana) ships with the repo and runs locally via docker compose up.
Replace your delivery-time dashboard with a decision copilot that recommends reallocations and proves the uplift.
Get risk-aware vehicle routing that is robust to traffic shocks, not just optimal on paper.
Causal inventory reallocation across warehouses to defend service-level under demand uncertainty.
A real, reproducible end-to-end stack to benchmark your algorithm on a public dataset (Olist).
White-label the UI, swap the dataset, deliver prescriptive recommendations with an audit trail.
Every component is MLflow-logged with timestamps — ready-made experimental evidence.
Every dollar funds causal benchmarks on new datasets and keeps the Docker stack running for the community.
Issues tagged good-first-issue, causal, optimizer, simulator. PRs welcome.
Plug your causal estimator or solver into our pipeline and publish uplift numbers.
Security disclosures are handled privately — see our policy.