Transforming Petroleum Distribution with AI
Multi-agent RL + Generative AI for real-time optimization. A three-layer platform that unifies scenario generation, logistics coordination and financial risk management across your entire distribution network.
0.0%
Cost Reduction
delivered cost per barrel
+0.0%
Service Reliability
on-time fulfillment rate
-0.0%
Risk Reduction (VaR)
vs. static hedged baseline
0%
5-Year ROI
net of platform cost
The Platform
A three-layer architecture, working as one
Each layer produces what the next consumes: scenarios feed the logistics agents, and the realized risk distribution drives hedging decisions — a closed loop of AI decisions.
Layer 1 · Generative Scenario Engine
Generative Scenario Engine
Learns the joint distribution of prices, demand and geopolitics, then samples thousands of realistic futures. Every logistics and hedging decision is stress-tested against the full ensemble.
- Probabilistic price & demand paths
- Regime-switching shocks
- 100-path ensembles in ms
Layer 2 · MADRL Logistics Coordinator
MADRL Logistics Coordinator
A fleet of RL agents — one per terminal, carrier and corridor — negotiate dispatch, routing and inventory policy in real time, balancing service reliability against operating cost.
- Decentralized policy learning
- Real-time re-routing
- Service reliability +23.8%
Layer 3 · Financial Risk Module
Financial Risk Module
Translates scenario distributions into optimal hedge positions under Value-at-Risk and Conditional VaR constraints, continuously rebalancing across futures, options and swaps.
- Minimum-variance hedge ratios
- VaR / CVaR constraints
- −31.2% risk reduction
Technology
Built on a modern ML & data stack
Every component is containerized, streamed and observable — from diffusion training to live dashboards.
PyTorch
ModelingTime-series diffusion backbone
Ray RLlib
ModelingMulti-agent RL training
Stable-Baselines3
ModelingPPO / SAC baselines
XGBoost
ModelingDemand forecasting
FastAPI
BackendInference API
Kafka
InfrastructureReal-time event streaming
ClickHouse
InfrastructureTime-series analytics
Docker / K8s
InfrastructureOrchestration
TimescaleDB
DataOperational store
Next.js 14
FrontendThis platform
Recharts
FrontendScenario visualization
Framer Motion
FrontendInteraction design
Research
Peer-reviewed foundation
The platform is backed by a Q1 journal paper on AI-driven digital transformation in petroleum product distribution — covering the generative scenario engine, the MADRL coordinator and the dynamic hedging module end-to-end.
AI-Driven Digital Transformation in Petroleum Product Distribution: A Multi-Agent Reinforcement Learning and Generative Modeling Approach
The distribution of petroleum products is a high-stakes, multi-scale optimization problem: decisions at the terminal, fleet, and financial layers interact under deep uncertainty in prices, demand, and geopolitics. This work introduces a three-layer AI platform. Layer 1 embeds a generative scenario engine built on time-series diffusion models that produce realistic price and demand paths. Layer 2 coordinates logistics with a multi-agent deep reinforcement learning (MADRL) framework that balances service reliability, inventory cost, and utilization. Layer 3 couples the scenario distribution to a financial risk module performing dynamic hedging under VaR and CVaR constraints. Across a simulated US network of 10 terminals, 20 heterogeneous carriers and 5 pipelines, the platform reduces delivered cost by 17.3%, improves service reliability by 23.8%, cuts value-at-risk by 31.2%, and delivers a five-year ROI of 1,332% versus the static baseline.