Every day, thousands of trucks leave warehouses, but manual route planning leads to 15–20% excess mileage. The Vehicle Routing Problem (VRP) is NP-hard, requiring a combination of exact methods (OR-Tools) and heuristics. We implement hybrid systems that build near-optimal routes for hundreds of points in seconds, accounting for time windows, load capacity, traffic, and dynamic orders. AI delivery route optimization is key to reducing costs. Our experience: over 50 projects for retail, food delivery, and field service. Results: mileage reduced by 15–25%, OTIF improves by 10–20 percentage points, fuel costs drop by 12–20%. Guaranteed performance with a typical payback under 12 months. Our team is ISO 9001 certified and has delivered consistent savings across diverse fleets.
Classic VRP has many variants: CVRP (capacity), VRPTW (time windows), VRPPD (pickup & delivery), MDVRP (multiple depots), DVRP (dynamic). Each adds complexity, but hybrid models can process up to 500 points in 30 seconds — 100x faster than manual planning. According to Wikipedia, the vehicle routing problem is formulated as finding an optimal set of routes. We use metaheuristics like Large Neighborhood Search and machine learning (LightGBM) for delivery time prediction.
Why VRP is harder than it seems
VRP variants in logistics add constraints: CVRP (load capacity), VRPTW (time windows), VRPPD (pickup & delivery), MDVRP (multiple depots), DVRP (dynamic orders). Combining several makes the problem resource-intensive. OR-Tools processes 500 points in 30 seconds — 100x faster than manual planning.
How AI finds optimal routes
Google OR-Tools (for problems up to 500 points):
from ortools.constraint_solver import routing_enums_pb2 from ortools.constraint_solver import pywrapcp def solve_vrptw(distance_matrix, time_windows, demands, vehicle_capacities, depot=0): manager = pywrapcp.RoutingIndexManager( len(distance_matrix), len(vehicle_capacities), depot ) routing = pywrapcp.RoutingModel(manager) def distance_callback(from_idx, to_idx): from_node = manager.IndexToNode(from_idx) to_node = manager.IndexToNode(to_idx) return distance_matrix[from_node][to_node] transit_callback_index = routing.RegisterTransitCallback(distance_callback) routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index) time_dimension = routing.AddDimension( transit_callback_index, slack_max=30, capacity=480, fix_start_cumul_to_zero=False, name='Time' ) time_dim = routing.GetDimensionOrDie('Time') for location_idx, (start, end) in enumerate(time_windows): index = manager.NodeToIndex(location_idx) time_dim.CumulVar(index).SetRange(start, end) def demand_callback(from_idx): return demands[manager.IndexToNode(from_idx)] demand_idx = routing.RegisterUnaryTransitCallback(demand_callback) routing.AddDimensionWithVehicleCapacity( demand_idx, 0, vehicle_capacities, True, 'Capacity' ) search_params = pywrapcp.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.seconds = 30 solution = routing.SolveWithParameters(search_params) return solution, routing, manager Dynamic routing: Orders arrive during the day. Large Neighborhood Search with adaptive destroy/repair operators. When a new order arrives, the system finds optimal insertion position without full recomputation using regret insertion, considering the "regret" of losing alternative options.
Comparison of VRP solution methods:
| Method | Accuracy | Solution time | Problem size |
|---|---|---|---|
| OR-Tools (exact) | High | 5-60 s | up to 500 points |
| Metaheuristic (LNS) | Approximate | 1-10 s | up to 5000 points |
| LightGBM (prediction) | 8-12% MAPE | <1 s | any number |
Accounting for real traffic
The distance and travel time matrix is built using OSRM (self-hosted, ~50ms per 100x100 matrix request) or commercial APIs — HERE, Google Maps, Yandex.Maps. Historical traffic data is averaged by road segments with hourly breakdown.
Delivery time prediction uses GBDT (LightGBM) on features: hour, day of week, holidays, current road congestion (Yandex.Maps / 2GIS API), weather (speed and vehicle weight affect time), and zone type (city center vs. industrial area). MAPE of arrival time prediction: 8–12% for intra-city delivery.
Economic effect
| Metric | Change |
|---|---|
| Total mileage | -15 to -25% |
| Fuel cost | -12 to -20% |
| Number of trips | -8 to -15% |
| OTIF | +10 to +20 pp |
| Drivers needed for same volume | -5 to -15% |
For a fleet of 50 trucks, this yields fuel savings of up to $22k–32k per year. Late delivery penalties are reduced — savings up to $2.7k–3.9k per year. Payback period is less than 12 months. Order a logistics audit to assess potential savings.
Real-time monitoring
GPS tracking of drivers enables comparison with planned route, with alerts on deviations >500m or delays >15 min. The system automatically recalculates the remaining route on significant deviations and integrates with any GPS trackers via API.
Commercial deliverables
- Audit report with current logistics analysis and savings forecast
- VRP model design document defining all constraints and objectives
- Algorithm implementation (OR-Tools, LNS, LightGBM) with documented code
- Integration with existing TMS (1C, Manhattan, SAP) via API
- Driver mobile app with navigation and electronic waybills
- BI dashboard with KPIs by drivers and zones
- Team training and user documentation
- 3 months post-launch support including bug fixes and optimization
Process
- Analytics — study routes, load order history
- Design — define metrics, select algorithms
- Development — implement in Python, integrate with maps
- Testing — A/B test on historical data, pilot on 10% of routes
- Deployment — rollout to entire fleet, monitor deviations
Timeline and cost
Development time: 3–5 months for system with VRPTW and dynamic replanning, TMS integration, and driver mobile app. Cost is calculated individually after audit. To discuss your task, contact our engineers. Get a free consultation.
For CVRP tasks, specify max_weight for each vehicle. In OR-Tools, this is implemented via AddDimensionWithVehicleCapacity with a vehicle_capacities array. For example, for 3 vehicles: [1000, 1500, 2000] kg.