Click anywhere inside the grid canvas on the right to place delivery coordinate stops, or select a preset dataset below to simulate routing optimization.
Our production routing systems solve complex multi-depot vehicle fleet tours (VRP), accounting for time windows, capacity limits, and traffic-adjusted isochrones in milliseconds.
View Routing Engine API →For physical distribution networks, retail logistics, and courier services, dispatch layout ineffeciences drive up fuel consumption and operations overheads. Computing optimized delivery orders client-side or server-side is key to automating scheduling. The simulation above solves the **Traveling Salesman Problem (TSP)** on Cartesian coordinates, generating a single optimized route.
Brute-force calculations evaluate all possible path permutations, meaning complexity grows at O(n!). For 10 delivery locations, there are over 3.6 million combinations; for 15 locations, it jumps to 1.3 trillion. To scale operations, developers deploy heuristics like Nearest Neighbor, local searching (2-Opt), and genetic models to yield near-optimal tours in milliseconds.
To run a routing search, a distance-time matrix is computed first. The matrix evaluates distance and travel time between every node in the delivery network. SynxWeb's Routing Engine API uses Contraction Hierarchies to calculate a 100x100 matrix grid (10,000 route computations) in under 80 milliseconds.
Production logistics require solving the Vehicle Routing Problem (VRP) rather than just single-route TSP. VRP maps multiple vehicles simultaneously while managing complex constraints like vehicle volumetric capacities, driver working shifts, delivery time-windows, and dynamic traffic adjustments.