The Mathematics of Route Optimization
Route optimization is a variant of the classic Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP) — both NP-hard computational problems. For a distribution operation with 30 stops, there are over 2.65 × 10^32 possible route permutations. Finding the optimal solution through brute force is computationally impossible.
Modern route optimization engines use heuristic algorithms — primarily Clarke-Wright savings algorithm, genetic algorithms, and simulated annealing — to find near-optimal solutions within seconds. SpireStock's engine combines these approaches with machine learning trained on millions of actual delivery data points from Indian distribution operations, accounting for factors like market congestion zones, festival-day closures, and seasonal demand shifts.