Route Optimization at Scale: Algorithms vs ML Approaches

Route optimization, given N stops and M vehicles, find the best routes, is one of the most studied problems in operations research. Modern ML adds new tools but doesn't replace the classics. Here's the working view.

Key takeaways

• Classical VRP solvers (OR-Tools, optimization libraries) are best for fixed problems with hard constraints.
• ML enhances by predicting travel times, demand patterns, and customer windows better than averages.
• Hybrid: ML-predicted inputs feeding classical solver is the realistic production pattern.
• Real-time re-optimization on events (cancellations, traffic) matters more than perfect initial routes.

The classical approach

Vehicle Routing Problem (VRP)

Given stops with demand and time windows, vehicles with capacity, find the assignment and order that minimizes some cost (distance, time, fuel).

Solvers

Google OR-Tools is the industry standard open-source solver. Solves VRP variants for 100-1000 stops in seconds to minutes.

When classical wins

• Fixed constraint set (vehicle capacity, time windows)
• Predictable travel times
• Small to medium scale (under 1000 stops per planning window)

The ML approach

Where ML helps

• Predicting travel times more accurately than averages (traffic, weather, time-of-day)
• Predicting demand (when will deliveries cluster?)
• Customer time window preferences (when do they actually accept deliveries?)
• Driver behavior modeling

Where ML doesn't help (alone)

• Hard constraints (capacity, must-deliver-today): solver handles
• Optimization itself: ML doesn't beat OR for combinatorial problems

The hybrid pattern (what production looks like)

1. ML predicts: travel time per leg, customer window preferences, demand patterns
2. Solver consumes ML predictions as inputs
3. Solver runs VRP to produce routes
4. Routes pushed to drivers
5. On real-time events (cancellations, traffic incidents), re-run with updated state

Most production logistics ops follow this pattern. ML is enrichment; OR is optimization.

Scaling to 10,000+ stops

Decompose

Geographic clustering before solver. Solve each cluster independently. Combine.

Approximate

Above some scale, exact optimization is too slow. Heuristics + local search produce 95% solutions in seconds.

Real-time re-optimization

Rather than perfect upfront, optimize-as-you-go. Each completed delivery triggers consideration of remaining route.

What changes for India

Address ambiguity

Indian addresses are messier than developed-country standards. AI-based address parsing and geocoding matters.

Two-wheeler vs four-wheeler

Bikes vs cars have very different route constraints. Model separately.

Traffic patterns

Indian urban traffic varies hugely by hour. Travel-time ML must capture this.

Returns

Indian eCommerce has high return rates. Routes must include reverse-pickup capacity.

Common pitfalls

Optimize once, ignore reality. Real ops change every hour. Build for re-opt.

Ignore driver preferences. Drivers know their territory. Let them override.

Single objective. "Minimize distance" misses customer experience; multi-objective is real.

ML for everything. Use ML where it adds; use OR for what it's good at.

What we recommend

OR-Tools (or similar) as the solver backbone. ML for travel time and customer window prediction. Real-time re-optimization on events.

FAQs

Build vs buy? Routing optimization SaaS exists (Onfleet, OptimoRoute); build only at significant scale.

Cloud vs edge? Cloud for optimization; edge for navigation.

How fast does optimization need to be? Sub-minute for typical use.

Talk to Techpuvi about route optimization.