Operations · Application area 16 of 26
AI Scheduling and Dispatch for Field Teams
CyberGipsy installs scheduling that accounts for skills, travel time, parts, permits and priority, then re-plans automatically when a job overruns or a customer cancels. Dispatchers stop rebuilding the day by hand every morning.
Definition. AI scheduling and dispatch is the automated assignment of jobs to people and time slots, optimised for travel, skills, availability and priority, with continuous re-planning as reality changes.
The problem this removes
- The schedule is a whiteboard and it is wrong by 10:00.
- Crews spend hours in traffic crossing the city in the wrong order.
- One cancellation collapses the whole day and nobody backfills the slot.
What we actually build
Constraint model
Skills, certifications, vehicles, tools, parts, working hours, zones, customer windows.
Optimisation
Routes and sequences that minimise travel and maximise completed jobs per day.
Live re-planning
Overruns, cancellations and emergencies trigger an automatic reshuffle with human approval.
Backfill
Cancelled slots offered instantly to waiting customers.
Customer communication
Confirmations, on-the-way messages and accurate arrival windows.
Capacity forecasting
Where you will run out of people, and when, by service and season.
Field app
Today's list, navigation, job detail, checklists, photo capture, sign-off.
How the install works
Five steps. Nothing here is a workshop.
Model the constraints
Written down properly, usually for the first time.
Import the real data
Historical job durations, not optimistic ones.
Run parallel
Optimised plan compared to the dispatcher's plan for two weeks.
Hand over control
Automatic planning with dispatcher override, never the reverse.
Tune seasonally
Demand patterns change; the model is reviewed quarterly.
What changes, in numbers
Honest ranges from installs of this type. Your baseline is measured during the diagnostic so the comparison is yours, not an industry average.
| Measure | Typical before | After install |
|---|---|---|
| Time spent building the daily plan | 1-2 h | Minutes, reviewed |
| Travel time per crew per day | Baseline | Typically reduced |
| Jobs completed per crew per day | Baseline | Increased where travel dominates |
| Accurate arrival windows given to customers | Rarely | Every job |
| Cancelled slots refilled | Seldom | Automatically offered |
What AI will not do here
Optimisation is bounded by data quality. If your job durations are guesses, the plan will be a well-organised guess. It also cannot fix understaffing or unrealistic promises made at the sales stage, and it should never override a dispatcher's local knowledge about a difficult customer or a difficult building. Human override always stays available.
AI Scheduling & Dispatch: questions we get asked
Do I need a fleet tracking system?
It helps but it is not required. Phone-based location during working hours is usually enough, and it raises fewer privacy issues with the crew.
How does it handle emergencies?
Priority rules insert urgent jobs and reshuffle the rest, showing the dispatcher exactly what changed and who needs to be told.
Will it schedule people into unrealistic days?
Not if the duration data is honest. We deliberately load historical actuals rather than planned durations, because optimism in the model becomes overtime in reality.
What about crew preferences and fairness?
Fairness rules can be encoded: rotation of unpopular jobs, distance from home, preferred zones. Ignoring this is the fastest way to lose good technicians.
Does it work with subcontractors?
Yes, with separate availability, rates and skill sets, and with clear rules about which work can be subcontracted.
How long to install?
Two to four weeks, with the constraint modelling being the slow part rather than the software.
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