Simple shortest-path routing solves a fundamentally different problem than route optimization. When you ask Google Maps for directions from point A to point B, you're using shortest-path routing—it calculates the fastest single route between two locations using algorithms like Dijkstra's or A*. The problem is well-defined, with one starting point, one destination, and one vehicle.
Route optimization tackles the Vehicle Routing Problem (VRP), which is exponentially more complex. Instead of one trip, you're scheduling multiple stops across multiple vehicles with real-world constraints. A field service company might need to schedule 50 technicians visiting 200 customers, each with time windows, skill requirements, and service durations. You can't simply calculate 200 individual shortest paths—you need to determine which technician visits which customer, in what sequence, while respecting constraints and minimizing total cost.
The computational complexity differs dramatically. Shortest-path algorithms solve in polynomial time—doubling locations roughly doubles computation time. Route optimization is NP-hard, meaning computation time grows exponentially with problem size. A 20-stop problem has over 2 quintillion possible route combinations. This is why VRP requires specialized algorithms like metaheuristics (genetic algorithms, simulated annealing, tabu search) rather than exact shortest-path methods.
Constraint handling separates the two approaches. Shortest-path routing considers road networks and traffic. Route optimization adds time windows (customer available 9-11 AM), vehicle capacity (technician tools and parts), skills matching (only certified techs for certain jobs), service durations, vehicle start/end locations, priority levels, and compatibility rules. A routing API must simultaneously satisfy dozens of constraints while optimizing for cost, distance, or time.
Real-world routing demands optimization, not simple pathfinding. When your HVAC company receives an emergency service call at 2 PM, you need dynamic re-optimization—recalculating all routes to fit the urgent job while minimizing disruption to scheduled appointments. Shortest-path routing can't handle this; it doesn't understand your existing schedule, technician locations, or business rules. Route optimization APIs use warm-start capabilities to quickly adjust existing routes rather than recalculating from scratch.
The outputs differ too. Shortest-path routing returns a single route with turn-by-turn directions. Route optimization returns a complete dispatch plan: which vehicle handles which stops, in what order, with arrival times, expected delays, and utilization metrics. For a delivery company running 50 vehicles, that's 50 optimized routes updated in real-time as conditions change.
Bottom line: Use shortest-path routing when navigating from A to B. Use route optimization when scheduling multiple vehicles serving multiple locations with real business constraints. The difference isn't just complexity—it's solving an entirely different class of problem.