Optimization AI and machine learning serve different functions in routing and logistics, though the terms often get conflated. Understanding the distinction clarifies when to use each approach—and why many routing problems need optimization, not machine learning.
Optimization AI solves the Vehicle Routing Problem directly. Given locations, vehicles, constraints, and an objective (minimize cost, time, or distance), optimization algorithms search through possible route combinations to find the best solution. Techniques include exact algorithms (for small problems), metaheuristics (genetic algorithms, simulated annealing), or constraint programming. The algorithm follows mathematical rules to guarantee feasibility—it won't produce routes that violate time windows or exceed vehicle capacity. This is fundamentally a search and decision problem: which vehicle visits which customer in what order?
Machine learning predicts patterns from historical data. ML models learn relationships between inputs and outputs without explicitly programming rules. For routing, ML commonly predicts travel times, demand patterns, or service durations. An ML model might predict that deliveries in downtown Seattle on Friday afternoons take 40% longer than scheduled, or that demand for HVAC service spikes during the first 90°F day of summer. The model learns these patterns from historical data.
The core difference: optimization makes decisions, machine learning makes predictions. You can't "train" an optimization algorithm to decide routes—you run it with specific inputs to get optimal outputs. You can't use a metaheuristic to predict travel times—it doesn't learn from data. The confusion arises because modern systems use both technologies together.
Real-world routing combines both approaches. Machine learning improves optimization inputs: predict demand to schedule vehicles proactively, forecast traffic to estimate travel times accurately, or classify jobs by service duration. Then feed these predictions into an optimization API that generates routes. For example, an ML model predicts 30-minute service time for complex HVAC repairs based on job description, technician experience, and customer history. The optimization algorithm uses this predicted duration when scheduling the technician's route, ensuring time windows remain achievable.
Machine learning cannot directly optimize routes because it lacks constraint guarantees. An ML model might "learn" that certain route patterns perform well historically, but it won't guarantee that generated routes satisfy time windows, capacity limits, or precedence constraints. Neural networks can approximate optimization solutions for small, fixed-size problems, but they don't scale to real VRP complexity and can't handle constraint changes without retraining.
Recent confusion stems from "AI" terminology. Companies brand both ML and optimization as "AI," but the underlying mathematics differ entirely. Optimization algorithms use operations research techniques developed over 50+ years—they're deterministic, explainable, and mathematically provable. Machine learning uses statistical models trained on data—they're probabilistic, less interpretable, and accuracy-dependent.
When you need routing decisions under constraints, use optimization algorithms (metaheuristics, constraint programming). When you need predictions to improve those decisions, use machine learning (forecast demand, predict service times, estimate traffic). Many modern routing platforms combine both: ML for predictions, optimization for decisions.
The practical takeaway: If someone offers "ML for route optimization," dig deeper. Are they using ML to predict inputs (legitimate), or claiming ML generates optimized routes directly (questionable for complex VRP)? The state-of-the-art approach uses ML for prediction and specialized optimization algorithms for route generation—not one or the other.