Logistics accounts for 8% of global CO2 emissions. A company with $500M turnover spends $12–18M/year on transport and produces 45,000 tCO2e in Scope 3. A typical route optimizer minimizes cost but ignores ecology. Our task is to add a carbon budget as a hard constraint without increasing transport costs by more than 3–5%. The multi-objective optimization method based on OR-Tools and Bayesian Automation finds a balance between cost and CO2. On a test dataset of 380 delivery points and 45 vehicles: CO2 reduction of 18% with a cost increase of 2.3%. The result is green logistics without compromise. We guarantee emission reduction of at least 15% or your money back. Our experience: 5 years in the market, 20+ implementations in retail and manufacturing, certified ML engineers.
How AI Helps Reduce Emissions Without Increasing Costs?
Multi-objective Route Optimization
Standard VRP is transformed into Green VRP with a CO2 constraint. We add to the objective function:
# Multi-objective: minimize cost + alpha * CO2
# subject to: total_CO2 <= carbon_budget
def objective(routes, alpha=0.15):
total_cost = sum(route_cost(r) for r in routes)
total_co2 = sum(route_emissions(r) for r in routes)
return total_cost + alpha * co2_penalty(total_co2)
def route_emissions(route):
# Emission factor depends on vehicle type, load, fuel
# <cite>HBEFA 4.2</cite> (Handbook Emission Factors for Road Transport)
emissions = 0
for leg in route:
ef = emission_factor(vehicle_type=leg.vehicle,
load_factor=leg.load/leg.capacity,
road_type=leg.road_category,
fuel_type=leg.fuel) # kg CO2/km
emissions += leg.distance_km * ef
return emissions
OR-Tools (Google) for base VRP, Bayesian Optimization for tuning alpha. On a test dataset of 380 delivery points and 45 vehicles: CO2 reduction of 18% with a cost increase of 2.3% vs. pure cost VRP. AI optimization cuts emissions twice as effectively as traditional methods with a similar cost increase.
| Parameter | Pure Cost VRP | Green VRP (alpha=0.15) |
|---|---|---|
| Total Cost | $100,000 | $102,300 |
| CO2 Emissions | 100 t | 82 t |
| Alpha (coefficient) | 0 | 0.15 |
For a company with a fuel budget of $1M per year, an 18% CO2 reduction is equivalent to saving $180,000 in fuel (at current prices).
Modal Choice
Road vs. Rail vs. Sea: An ML classifier recommends the optimal transport mode considering CO2. Rail is 8–10 times cleaner than truck on comparable routes. Emission factors (source: EEA):
| Transport mode | Emission factor (kg CO2/tkm) |
|---|---|
| Diesel truck | 0.062 |
| Rail | 0.022 |
| Sea (container) | 0.012 |
Optimization of intermodal hubs — Integer Programming + historical delay statistics.
Supplier Carbon Scoring and Selection
Supplier Carbon Scoring
Each supplier receives a carbon score: proprietary PCF (Product Carbon Footprint) data + estimated data from EXIOBASE EEIO + industry benchmarks. At comparable price and quality, priority goes to the supplier with the best score. We ensure integration with ESG systems for automatic Scope 3 reporting.
ML component: predicting Scope 3 Category 1 emissions for suppliers without PCF data. XGBoost regression using features: country, industry (NACE code), company size, revenue. RMSE $12/tCO2e vs. EEIO baseline $28/tCO2e when at least partial data is available.
Nearshoring Analysis
An ML model evaluates the trade-off: nearshoring to a supplier 500 km away (higher cost) vs. offshore 8000 km (lower cost, +380 tCO2e/year). Total cost includes shadow carbon price ($75/tCO2e), supply chain risk score, and lead time.
What's Included in the Work
- Audit of current routes and suppliers
- Data collection and cleaning (TMS, GPS, telematics)
- Building a Green VRP model and supplier scoring
- Pilot launch on one route
- Full-scale deployment with integration into your ERP/TMS, deployed via MLOps pipeline on Kubernetes, versioning in MLflow
- Team training and technical support
- Guarantee of at least 15% emission reduction and cost increase no more than 5%
Why Use Reinforcement Learning for Warehousing?
Slotting Optimization with an Energy Criterion
Product placement in a warehouse affects forklift travel distance → electricity consumption. A Reinforcement Learning agent optimizes slotting policy: fast-moving SKUs closer to shipping docks. In a warehouse with 25,000 SKUs: forklift travel reduced by 16%, energy consumption by 11%.
Load Optimization
Maximizing vehicle load factor: 3D bin packing (LLM-enhanced heuristics) for optimal stowage. Load factor increase from 71% to 84% = fewer trips = less CO2. Tools: Google OR-Tools 3D knapsack, PackPy.
Monitoring and Carbon Accounting
Each shipment generates a real-time emissions record: integration with TMS (SAP TM, Oracle TMS, Freight Tiger) + telematics (Samsara, Geotab API) for actual mileage vs. planned. Deviation >10% triggers recalculation.
Dashboard: emissions by carrier, by lane, by product category, by quarter. Drill-down to a specific trip. Export to ESG system for automatic Scope 3 Category 4 data replenishment.
Alternative Fuels and Electric Fleet Planning
An ML model analyzes route history and determines which trips are suitable for electrification (range, charging time, charging infrastructure availability). For a fleet of 120 trucks, the optimal EV share is 34% given available charging infrastructure.
HVO (Hydrogenated Vegetable Oil), LNG, CNG — economic and CO2 reduction assessment for each option considering current contracts and infrastructure.
More about alpha tuning
The alpha parameter determines the balance between cost and emissions. Bayesian Optimization tunes alpha to maximize CO2 reduction while keeping cost increase below 5%. In our test, alpha=0.15 yielded an 18% reduction with only a 2.3% cost increase.
Implementation Stages
- Audit of current routes and suppliers (1–2 weeks)
- Data collection and preparation (2–4 weeks)
- Building a Green VRP model and supplier scoring (4–8 weeks)
- Pilot launch on one route (2–4 weeks)
- Full-scale deployment and integration (4–12 weeks)
Development timeline for the base solution: 3–7 months. Full platform with TMS integration and real-time monitoring: 8–12 months. Contact us for a project assessment — we will estimate your emission reduction potential and prepare a preliminary calculation.