Field Service Dispatch: Constraints, Skills, and Optimization Basics

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Overview

Dispatch is constrained optimization under uncertainty. Practical automation combines rules with light optimization—not academic OR only.

Core concept

Definition

Dispatch automation assigns work orders to technicians using eligibility (skills, certifications), availability, geography, and parts readiness.

Business impact

Why it matters

Poor dispatch burns fuel, misses SLAs, and frustrates customers. AI assists suggestions; constraints enforce reality.

Practical model

Framework

Hard constraints first

Regulatory licenses, safety, union rules.

Soft scoring

Prefer repeat customers, minimize travel when ties exist.

Implementation detail

Detailed breakdown

Dynamic rescheduling

Weather, traffic, and job overrun should trigger replanning with customer notifications.

In practice

Real-world example

A facilities firm cut average drive time 15% by feeding traffic-aware estimates into assignment suggestions—humans approved exceptions.

Avoid these

Common mistakes

Optimization without real-time status—assignments go stale.
Ignoring parts availability—tech arrives unarmed.

Engineering layer

Technical patterns

Constraint layers

Feasible set: tech has skill + parts + shift window contains SLA end.
Objective: minimize travel then lateness.

Build patterns

Code examples

Feasibility filter

Narrows candidate techs before scoring.

TypeScript

export function feasibleTechs(wo, techs) {
  return techs.filter((t) =>
    t.skills.includes(wo.requiredSkill) &&
    t.shiftCovers(wo.windowEnd) &&
    t.hasParts(wo.partsNeeded)
  );
}

System view

System architecture

YAML

[WO create / update]
        → [Constraint filter]
        → [Routing optimizer / greedy heuristic]
        → [Calendar block + notify]
        → [Mobile app]

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