Problem: Manual route planning takes hours while the client waits
A major logistics operator with a fleet of 120 vehicles processed 10,000 orders per month. Dispatchers spent 3–4 hours building routes, and 25% of orders were delivered late. After implementing our AI system, planning time dropped to 40 seconds, mileage decreased by 18%, and the on-time delivery rate rose from 65% to 92%. According to McKinsey's digital logistics report, AI optimization reduces operational costs by 15–25%.
The Vehicle Routing Problem (VRP) is NP-hard: for 50 points, exact solution requires 50! possibilities. We use a hybrid of Reinforcement Learning (Attention Model) and metaheuristics (Guided Local Search), yielding a solution in seconds with a gap of <3% from the optimum. Our stack: PyTorch, OR-Tools, vLLM for inference.
Clients often ask: how does AI handle dynamic orders? The answer is an insertion heuristic with ML evaluation: a new order is inserted into the route in fractions of a second without full recalculation. Accuracy is 95% of optimal. Fuel savings reach up to $1,500 per vehicle per month.
How does Reinforcement Learning work for VRP?
The agent (policy) selects the next point at each step using context embeddings. Training uses REINFORCE with advantage. For stability, we use an experience replay buffer.Mathematical Foundation: VRP and Its Variants
- VRPTW (Time Windows) — each point must be visited within a given interval
- CVRP (Capacitated) — load capacity constraints
- MDVRP (Multi-Depot) — multiple warehouses
- DVRP (Dynamic) — orders arrive in real time
- VRPPD (Pickup and Delivery) — pairs of pickup and delivery
In practice, all these constraints appear simultaneously. Exhaustive enumeration for 50 points is impossible even on a supercomputer.
How AI Solves VRP in Seconds
We use a hybrid approach: Reinforcement Learning (Attention Model) + classical metaheuristics. RL learns to 'construct' a route by adding points one by one — inference takes milliseconds. But to guarantee quality, we apply local search (Guided Local Search) on top of the RL solution.
from ortools.constraint_solver import routing_enums_pb2, pywracp
def solve_vrptw(locations, time_windows, demands, vehicle_capacities):
manager = pywracp.RoutingIndexManager(len(locations), len(vehicle_capacities), 0)
routing = pywracp.RoutingModel(manager)
transit_callback_index = routing.RegisterTransitCallback(...)
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
time_dimension = routing.GetDimensionOrDie('Time')
for node, (start, end) in enumerate(time_windows):
index = manager.NodeToIndex(node)
time_dimension.CumulVar(index).SetRange(start, end)
search_params = pywracp.DefaultRoutingSearchParameters()
search_params.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
search_params.local_search_metaheuristic = (
routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
search_params.time_limit.FromSeconds(30)
solution = routing.SolveWithParameters(search_params)
Why the Hybrid Approach Is the Industry Standard
Pure RL yields solutions that are 10–15% worse than optimal on complex instances. Metaheuristics (Genetic Algorithm, Simulated Annealing) are 10 times slower. The hybrid approach is 10 times faster than metaheuristics and 10–15% more accurate than pure RL. We also use a surrogate ML model for fast solution quality estimation — this speeds up search by 5–20 times.
Incorporating Real-World Factors: Traffic, Weather, Driver Status
Real-time dynamic data:
- Traffic (HERE Traffic API, Yandex.Traffic)
- Weather (impact on travel time and road availability)
- Driver status (breaks, working time restrictions)
- Telematics (fuel consumption, remaining fuel)
Travel time prediction: LSTM model trained on historical GPS tracks. MAE: 2–5 minutes in the city vs 8–15 minutes for static maps. Average fuel savings reach $1,500 per vehicle per month, enabling fast ROI.
Comparison: Manual Planning vs AI
| Criteria | Manual (dispatcher) | AI optimization |
|---|---|---|
| Time per cycle | 2–4 hours | 30–90 seconds |
| Mileage reduction | — | 12–22% |
| Fleet size reduction | — | 10–18% |
| On-time delivery rate | 60–70% | 85–95% |
| Adaptation to new orders | Minutes | Fractions of a second |
ROI: for a fleet of 50+ vehicles — 6–12 months from fuel and labor savings. Average fuel savings: $15,000 per vehicle per year, planning time reduction: 98%.
Comparison of VRP Solution Approaches
| Approach | Solution time | Quality (gap) | Applicability |
|---|---|---|---|
| Pure RL | milliseconds | 10–15% | Fast but inaccurate |
| Metaheuristics (GA, SA) | seconds–minutes | <3% | Accurate but slow |
| Hybrid (RL + LS) | milliseconds–seconds | <3% | Best speed-accuracy trade-off |
What's Included in the Work
- Audit of current routes, data, and business processes (2–3 days)
- Model selection — RL, metaheuristic, or hybrid (1 week)
- Integration with TMS, GPS, ERP (2–3 weeks)
- Testing on historical data — A/B test in sandbox (1 week)
- Production launch — parallel operation with manual planning (1 week)
- Dispatcher training + documentation + 3 months support
Estimated Timelines
From 4 to 8 weeks depending on complexity (number of vehicles, points, dynamics). Cost is calculated individually for your fleet. Get a consultation — we'll evaluate your project in 2 days.
Why Choose Us?
- 7+ years of experience in AI/ML
- 50+ successful projects in logistics and retail
- Certified specialists in Google OR-Tools and PyTorch
- Guarantee of at least 10% mileage reduction
Contact us for a consultation, order a pilot project, or get a personal estimate in 2 days — and you'll see the results on your own data.