What's the most cost-effective way to balance supply and demand using a real-time routing engine?

The most cost-effective way is usually to combine dynamic matching with re-optimization on a rolling horizon, rather than trying to solve the entire problem globally every second.

Practical approach

1. Predict supply and demand in short windows

   • Forecast near-term demand by zone/time.
   • Estimate available supply, travel times, and service times.

2. Partition the service area

   • Use zones or grids so the engine only considers nearby matches.
   • This cuts compute cost and reduces unnecessary long-distance routing.

3. Use a rolling-horizon optimizer

   • Every few seconds or minutes, re-run an assignment model for the next 10–30 minutes.
   • Include current assignments as constraints so you don’t disrupt already-committed routes too much.

4. Optimize for total cost, not just immediate matches

   • Objective should combine:
     • pickup delay
     • deadhead/empty miles
     • driver idle time
     • penalties for lateness or missed demand
   • This is where the real savings usually come from.

5. Apply local heuristics for real-time decisions

   • For urgent requests, use fast heuristics:
     • nearest feasible vehicle
     • best insertion into existing route
     • zone-based prioritization
   • Reserve full optimization for batch rebalancing.

6. Reposition idle supply proactively

   • Send surplus vehicles to predicted hotspots before demand arrives.
   • This often yields better cost efficiency than reacting after shortages appear.

Best cost-saving pattern

A strong default architecture is:

• Fast real-time matcher for immediate requests
• Periodic optimizer for rebalancing and route improvement
• Predictive demand model to guide repositioning

This hybrid setup is typically more cost-effective than:

• purely greedy dispatching, which can create inefficiency
• fully exact optimization, which is often too expensive computationally for real-time use

If you want the shortest answer:

Use a hybrid real-time routing system: greedy local matching for instant decisions plus periodic rolling-horizon optimization with demand forecasting and proactive repositioning.

If you want, I can also sketch a sample objective function or a system architecture for this.