Master field service scheduling with AI. Learn how to reduce technician drive time by 25–35%, solve VRP at scale, and integrate optimization APIs into…

Timefold

Field service routing optimization automatically assigns technicians to jobs and sequences daily routes by solving variants of the Vehicle Routing Problem (VRP). Organizations using AI-based routing typically see 25–35% reductions in drive time. The problem is NP-hard — 50 technicians and 200 jobs produce more possible schedules than atoms in the observable universe — making metaheuristic solvers (simulated annealing, tabu search, large neighborhood search) necessary. Constraints are categorized as hard, medium, soft, and preferred, covering skill matching, time windows, SLAs, shift hours, and fairness. Modern engines are delivered as REST APIs that FSM platforms embed as back-end services, handling up to 30,000 visits per run and re-optimizing in seconds for intraday disruptions. Building such an engine in-house takes 12–18 months; integrating a specialized API typically reaches production in ~2 months.

Field Service Routing Optimization: The Definitive Guide

# What is Field Service Routing Optimization?

# How Field Service Routing Optimization works

# Manual scheduling vs Routing Optimization: A direct comparison

# How Routing Optimization is delivered: The API model