Field Service Dispatch: Constraints, Skills, and Optimization Basics
Overview
Dispatch is constrained optimization under uncertainty. Practical automation combines rules with light optimization—not academic OR only.
Quick definition
Dispatch automation assigns work orders using hard constraints (skills, SLA deadline, parts availability) and soft optimization (travel time)—often solved as constrained routing, not ad hoc sorting.
Definition
Dispatch automation assigns work orders to technicians using eligibility (skills, certifications), availability, geography, and parts readiness.
Why it matters
Poor dispatch burns fuel, misses SLAs, and frustrates customers. AI assists suggestions; constraints enforce reality.
Core framework
Hard constraints first
Regulatory licenses, safety, union rules.
Soft scoring
Prefer repeat customers, minimize travel when ties exist.
Detailed breakdown
Dynamic rescheduling
Weather, traffic, and job overrun should trigger replanning with customer notifications.
Technical patterns
Constraint layers
- Feasible set: tech has skill + parts + shift window contains SLA end.
- Objective: minimize travel then lateness.
Code examples
Feasibility filter
Narrows candidate techs before scoring.
export function feasibleTechs(wo, techs) {
return techs.filter((t) =>
t.skills.includes(wo.requiredSkill) &&
t.shiftCovers(wo.windowEnd) &&
t.hasParts(wo.partsNeeded)
);
}System architecture
[WO create / update]
→ [Constraint filter]
→ [Routing optimizer / greedy heuristic]
→ [Calendar block + notify]
→ [Mobile app]Real-world example
A facilities firm cut average drive time 15% by feeding traffic-aware estimates into assignment suggestions—humans approved exceptions.
Common mistakes
- Optimization without real-time status—assignments go stale.
- Ignoring parts availability—tech arrives unarmed.
Related topics
PrimeAxiom implements dispatch workflows with CRM and FSM tools—book a field ops review.