Field service routing: technicians, jobs and real roads
Which technician does which job, in which order — so every customer is visited inside the agreed window?
Drag to rotate, scroll or pinch to zoom. The schedule below plays the plan; hover a bar for details. Plan computed with a Timefold model.
The planning question
A service company installs wallboxes, routers, smart-home kits, kitchens and furniture in homes around Windhagen in the Westerwald. Eight technicians start at one service hub. Every job needs a skill, has an arrival window agreed with the customer and takes space in the van. Travel times come from the real road network.
How we plan it
The model is a vehicle routing problem with time windows and skills, solved with Timefold. Urgent jobs, sick technicians or moved appointments re-plan the rest of the day while the jobs already started stay fixed.
What the plan shows
| KPI | Optimized plan | Comparison: dispatcher board |
|---|---|---|
| jobs planned | 29 / 29 +5 | 24 / 29 |
| late arrivals | 0 | 0 |
| overtime (min) | 0 | 0 |
| driving (km) | 104 +9 | 95 |
Decisions
- technician per job (skills: electrical, IT, appliances, kitchens, furniture)
- sequence of the jobs on each route
- arrival time at each customer
Constraints
- skill required by the job
- arrival window agreed with the customer
- working time and overtime limits per technician
- van capacity (kitchens and furniture need space)
- travel times on the real road network
Objective
all jobs planned, no late arrivals and no overtime, little driving
Questions
Can the plan react to an urgent job during the day?
Yes. Jobs that have started are pinned; the solver re-plans the open part of the day in seconds and keeps changes for the other customers as small as possible.
Where do the travel times come from?
From a road network (here OpenStreetMap). In projects we use routing services or the customer’s own travel-time matrix.
More use cases
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