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PLANNING & SCHEDULING

Cluster Optimization for Grouping Delivery Stops and Field Service Jobs

Preview of Cluster Optimization in eLogii Click to zoom

What this means for distribution businesses

A pure route optimizer might send a driver zigzagging across postcodes because mathematically that is the shortest path. But delivery operations need more than mileage efficiency. Cluster optimization keeps each driver in a tight geographic patch so they learn the parking spots, loading bay access, and timing quirks that shave minutes off every stop. Grocery distributors serving restaurant clusters in city centres get drivers who know which back entrance to use at 6am. Courier operations handling high-volume parcel runs build route familiarity that means fewer missed deliveries and faster drop rates. Building materials distributors serving construction sites get drivers who know which site gate is open and where the forklift will be waiting.

What this means for field service businesses

A route optimizer left unconstrained might scatter a technician across town because the maths says that is fastest. But field service businesses know the value of keeping engineers in a patch. Pest control technicians who work the same residential area every quarter spot recurring issues faster and build trust with homeowners who prefer seeing the same face. HVAC companies clustering boiler services by estate or postcode get engineers who already know the boiler models on that street and where the isolation valves are. Cleaning services grouping commercial contracts by area build site familiarity that speeds up every visit. Security alarm installers staying in a territory learn the access codes, keyholder quirks, and panel locations that turn a 90-minute job into a 60-minute one.

How it works

  • Choose from K-Means, constrained, or deterministic clustering algorithms
  • The system groups stops by geographic proximity before route optimization begins
  • Constrained clustering guarantees a minimum number of stops per cluster
  • Clusters are balanced across drivers based on capacity and time constraints
  • Re-clustering happens automatically as new jobs are added throughout the day
3 clustering algorithms to choose from: K-Means, constrained, and deterministic
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