Route Optimization Feature Specification Template

Build if routing is your core product differentiator and you have operations research expertise on the team. Use a third-party API (Google OR-Tools, Routific, OptimoRoute) if routing is one feature among many. Most logistics startups start with a third-party solver and migrate to custom when they hit scale or constraint limitations.

This is the classic tension in vehicle routing. When the solver cannot satisfy all constraints, define a priority hierarchy: safety constraints first, then hard time windows, then capacity, then soft time windows. Allow the solver to violate soft constraints with a penalty score so you get a feasible solution rather than no solution.

At minimum: geocoded stop addresses, time windows, vehicle capacity, driver shift times, and historical service time per stop. Traffic data improves accuracy but is not required for an MVP. Start with static travel time estimates and add real-time traffic in a later phase.

For most delivery operations, ETAs within a 30-minute window meet customer expectations. Aim for 15 minutes if you offer narrow delivery windows (2-hour slots). ETA accuracy depends more on service time estimates and traffic data quality than on the routing algorithm itself.

Compare three metrics against your pre-optimization baseline: total miles driven (should decrease 15-25%), on-time delivery rate (should increase), and fleet utilization (percentage of capacity used per route). Track these weekly for the first 90 days and adjust solver parameters based on the data. ---