Route Optimization API for Multi-Location Businesses

The multi-depot vehicle routing problem (MDVRP) involves assigning and sequencing delivery stops across a fleet of vehicles that start and end at different warehouse or depot locations. The goal is to minimize total travel distance, time, or cost while respecting constraints like vehicle capacity, time windows, driver shift hours, and regulatory rules such as DOT hours-of-service. It's significantly more complex than single-depot routing because the optimizer must simultaneously decide which depot serves which stops, how many vehicles to assign per depot, and how to sequence each vehicle's route within its constraints.

A route optimization API accepts inputs — stop locations, vehicle details, constraints like time windows and capacity — and returns optimized route sequences via a REST API call. It runs mathematical optimization algorithms, such as VRP solvers, against a road network graph enriched with traffic data, vehicle rules, and custom attributes like hazmat restrictions or truck dimensions. For multi-depot problems, the solver also decides which depot serves which stops before sequencing. The result is a set of ordered stops per vehicle with ETAs, distances, and turn-by-turn navigation data, typically returned in seconds even for large stop counts.

NextBillion.ai's Route Optimization API supports multiple depot origins without a hard cap on depot count, and is engineered to handle thousands of stops per optimization run. The Distance Matrix API underpinning it supports up to 5,000×5,000 origin-destination pairs in a single call — far beyond the 25×25 limits common among competing providers — enabling large-scale multi-location planning without batching workarounds.

The API supports 50+ hard and soft constraints including delivery time windows, vehicle load capacity and multi-compartment packing, driver shift start and end times, mandatory break requirements, maximum route duration and distance, priority stop ordering, hazardous material routing rules, truck dimension compliance (height, weight, axle load), and custom road attribute restrictions set via the Road Editor App. Both hard constraints (must be satisfied) and soft constraints (should be minimized) are supported in the same call.

NextBillion.ai uses per-vehicle and per-order pricing models rather than per-API-call billing. This means re-optimization cycles, batch planning runs, and seasonal demand spikes don't generate proportional cost increases. Traditional per-call pricing from providers like Google Maps becomes exponentially expensive as fleets grow and optimization frequency increases. NextBillion.ai's model produces predictable monthly costs tied to actual fleet size or order throughput, not to the underlying computational activity of the routing engine.

Yes. The Route Dispatch API supports real-time dynamic route updates and task reassignment — pushing revised routes to affected drivers instantly when orders are added or cancelled, drivers become unavailable, or vehicles encounter disruptions. Combined with the Driver Assignment API for intelligent replacement matching and the Live Tracking API for real-time position data, the system maintains operational continuity through mid-shift changes without requiring manual dispatcher intervention for every adjustment.

NextBillion.ai's standard onboarding timeline is one week from trial start to production integration. This includes API access provisioning, data testing with your actual stop and fleet data, and dedicated support from NextBillion.ai's solutions engineering team throughout the integration process. The API is deployable on AWS, Google Cloud, and Microsoft Azure, with on-premise Kubernetes deployment available for organizations with data residency or private cloud requirements.

Yes. The Route Optimization API, Distance Matrix API, Navigation API, and Directions API all support vehicle-specific routing. You can specify vehicle type (car, motorcycle, truck, HGV), dimensions (height, width, weight, axle load), cargo classification including hazardous materials, and permit-based access rules. Routes generated for trucks automatically avoid low-clearance bridges, weight-restricted roads, and truck-prohibited streets — producing compliant, operationally safe routes rather than car-routing estimates applied to commercial vehicles.

The API supports constraints for driver shift start and end times, maximum route duration, and mandatory break requirements, which can be configured to align with DOT hours-of-service rules for commercial drivers. Since exact regulatory thresholds vary by vehicle class and jurisdiction, these limits are set as configurable constraints on your end rather than hardcoded, giving fleet operators control over how compliance rules are applied across depots and driver types.

Yes. Each depot can be configured with its own operating windows, and routes are planned around those individual constraints rather than a single fleet-wide schedule. This supports operations spanning multiple time zones or states with different regional requirements, such as state-specific permitting for oversized vehicles or local delivery windows, since depot-level and stop-level constraints are set independently within the same optimization run.