The Multi-Stop Complexity Problem

Multi-stop routing overwhelms human dispatchers because of factorial growth in possible sequences. Beyond 4–5 stops, human dispatchers achieve only 60–70% route efficiency compared to AI optimization, which consistently delivers 92–97% efficiency.

10 jobs create 3.6 million possible sequences; 15 jobs create over 1.3 trillion.

A plumber with 8 repair calls can have their route optimized from millions of possible sequences down to the single best path in under 2 seconds. No human dispatcher can evaluate even a fraction of those options.

Why Dispatchers Fall Back to Simple Heuristics?

Faced with this complexity, dispatchers naturally rely on shortcuts:

• “Send whoever’s closest”
• “Give Mike all north-side jobs”
• “Drop emergencies on whoever frees up first”

These rules work for 3–4 stops but break down at 8+ stops per technician

The Hidden Cost of Manual Routing

• Backtracking: Technicians drive past locations they’ll return to 2 hours later.
• Missed time windows: Customers wait. Techs rush. Reviews drop.
• Wasted fuel & overtime: A few extra miles each day becomes thousands each year.
• Burnout: Techs spend more time on the road than doing actual billable work.

Consider an HVAC company with 4 technicians each handling 8–10 tune-ups daily. Manual routing creates 2–3 hours of unnecessary drive time per tech per week. That’s 8–12 hours weekly of wasted labor across the team (FieldCamp analysis of HVAC companies with 4+ technicians).

For a deeper look at why algorithms are necessary for this level of complexity, see our guide to How AI Dispatcher Algorithms Work.

What Is Multi-Stop Route Planning in AI Dispatching?

Before we jump in, let’s anchor the basic concept.

Multi-stop route planning is the process of sequencing multiple appointments for a technician into the best possible order. The goal is simple:

• Minimize drive time
• Respect all time windows
• Avoid conflicts
• Honor skills, equipment, and job dependencies
• Maximize technician productivity
• Maintain customer satisfaction

Manual dispatching can only optimize for 2–3 of these at once. AI optimizes all of them simultaneously. This decision-making is handled by a centralized system designed to evaluate the entire day at once. For a deeper explanation of this approach, see our guide on what an AI dispatcher is.

How AI Solves Multi-Stop Routing?

AI solves multi-stop routing using the Vehicle Routing Problem with Time Windows (VRPTW)—a constraint optimization algorithm that asks: “What’s the shortest possible route that visits each location exactly once while honoring all real-world constraints?”

The Traveling Salesman Problem is the mathematical foundation here. In field service, this becomes the VRPTW, where AI must sequence stops while honoring customer availability, technician skills, and shift boundaries.

This is the “constraint intake” phase described beautifully in our guide on How AI dispatching algorithms work.

Real-World Example: Pool Service Route

A pool service company has 12 weekly maintenance stops. Without optimization, the technician drives 8 hours total, crisscrossing the service area.

AI clusters the stops geographically and sequences them to minimize backtracking. The route flows logically through the service area instead of jumping randomly between locations.

FieldCamp’s route optimizer evaluates all constraints and generates optimized multi-stop routes in seconds, compared to 15–30 minutes of manual dispatcher calculation.

When one job runs late, AI re-sequences remaining stops without breaking the whole route. The adjustment happens automatically, preserving the rest of the schedule.

This logic is controlled through AI job scheduling in FieldCamp, where travel limits and skills are configured.

The Anytime Scheduling Strategy (for Maximum Route Efficiency)

Anytime scheduling is a route optimization strategy where jobs are marked as date-specific but time-flexible, allowing AI to sequence them in the most efficient order without being locked into fixed time slots.

What Does “Anytime” Really Mean?

Think of “Anytime” as a promise to the customer that says:
“We’re coming Tuesday — and we’ll text you before we arrive.” Not 9 AM. Not 1 PM. Just… Tuesday.

This tiny bit of flexibility unlocks huge gains.

Why Anytime Scheduling Works (Human Explanation)?

When every job has a fixed time window, the route follows the clock instead of the map.
This creates zig-zagging patterns like:

• North → South → North → East → Back to South → West
• Techs sitting idle between appointments
• Heavy backtracking

But when you let AI decide the best time of day to perform a job, it finally optimizes based on geography, not arbitrary time slots.

Why Fixed Time Windows Limit Optimization?

When every job has a narrow 2-hour window, the algorithm must work around rigid constraints. The route follows the clock instead of the map.

But when jobs are marked “Anytime” (meaning “Tuesday, but we’ll text you 30 minutes before arrival”), the AI gains freedom to cluster geographically close stops and eliminate backtracking.

The Efficiency Gain

In FieldCamp internal testing with 200+ simulated routes, routes with 80% Anytime jobs achieved 58% better drive time efficiency compared to routes with all fixed time windows.

Same jobs, 42% less driving.

When to Use Fixed Windows vs. Anytime?

Use Fixed Time Windows for:

• Customer-requested specific appointments
• VIP clients who expect precise arrival times
• Jobs with external dependencies (inspector arriving at 2 PM)

Use Anytime Scheduling for:

• Routine maintenance visits
• Flexible customers who just need “sometime Tuesday”
• Recurring service contracts
• Any job where the customer values reliability over a specific hour

When You Should Avoid Anytime Scheduling?

Some jobs MUST respect customer presence or external scheduling:

• inspector or utility-coordinated visits
• VIP customers who expect exact times
• complex or high-priority repairs
• elderly customers dependent on caregivers
• installations requiring homeowner presence
• multi-person team jobs

How to Communicate This to Customers (Easy Script):

Most customers are perfectly fine with Anytime scheduling — they just need clarity.

Send this:

“We’ll be there Tuesday and send you a 30-minute text ETA when we’re on the way.”

90% of customers prefer this over waiting around for a 4-hour window.

Real-World Multi-Stop Scenarios

Four common scenarios:

Scenario 1: Pool Service Route

Setup: 12 weekly maintenance stops, geographically clustered, all marked “Anytime”

Constraints:

• Same-day completion required
• No specific time windows
• Standard equipment on every job

AI Optimization Result (Based on FieldCamp route simulations):

• Creates single efficient circuit through the service area
• Total route time: 3.2 hours (vs. 5.8 hours unoptimized)
• Zero backtracking
• 45% reduction in drive time

Scenario 2: HVAC Maintenance Day

Setup: 8 seasonal tune-ups, skill-matched (EPA certification required), mix of time windows and Anytime

Constraints:

• 3 jobs have fixed 2-hour windows
• 5 jobs marked Anytime
• All require EPA-certified technician
• 8-hour shift limit

AI Optimization Result (Typical results from FieldCamp customers):

• Sequences by geography while respecting the 3 fixed windows
• Clusters Anytime jobs around fixed appointments
• Drive time: 2.1 hours
• All time windows met

Scenario 3: Plumbing Emergency Insertion

Setup: Existing 6-stop route, urgent call arrives at 2:17 PM

Constraints:

• Emergency must be handled within 2 hours
• Remaining scheduled jobs can’t be missed
• Customer time windows still apply

AI Optimization Result (Based on FieldCamp route simulations):

• Emergency inserted as stop #4
• Remaining stops re-sequenced to minimize disruption
• Total delay to original schedule: 12 minutes
• Alternative (manual insertion as last stop): 47 minutes delay

Emergency job insertion takes 200–500ms in FieldCamp—the route updates before the dispatcher finishes the phone call.

Scenario 4: Appliance Repair Circuit

Setup: 5 stops, parts-dependent sequencing (diagnostic must happen before repair)

Constraints:

• Job #3 (repair) depends on Job #1 (diagnostic)
• Parts pickup required between diagnostic and repair
• 4-hour time window for final job

Before Optimization:

Stop 1 (diagnostic) → Stop 2 (unrelated job) → Stop 3 (repair) → Parts pickup → Stop 4 → Stop 5

AI Optimization Result (Based on FieldCamp route simulations):

Stop 1 (diagnostic) → Parts pickup → Stop 3 (repair) → Stop 2 → Stop 4 → Stop 5

• Respects job dependencies—diagnostic always before repair
• Sequences parts pickup efficiently between dependent jobs
• Drive time: 1.6 hours (vs. 2.3 hours unoptimized)
• Dependency constraints never violated
• Eliminates unnecessary backtracking to parts supplier

Single-Stop Assignment vs. Multi-Stop Route Planning

Understanding the difference between these two concepts is critical for dispatchers transitioning to AI-powered systems.

What’s the Difference?

Single-stop assignment answers: “Who should do this job?” It considers skills, location, and availability for one job at a time.

Multi-stop planning answers: “In what order should this technician complete all their jobs?” It considers all constraints simultaneously to minimize drive time and respect time windows.

Conclusively, we can say that while single-stop logic assigns jobs one by one, AI job scheduling evaluates the full day before committing any sequence.

Why Single-Stop Thinking Fails?

When dispatchers assign jobs one-by-one as they arrive, each individual assignment looks logical. But the cumulative result is often an inefficient route.

Example:

Dispatcher assigns Job 1 to closest tech (2 miles away). Assigns Job 2 to next-closest (3 miles). By Job 8, the route has 3 instances of backtracking and 1.2 hours of unnecessary drive time. AI re-optimization produces a route with zero backtracking and 2.3 hours total drive time—a 44% improvement.

The Efficiency Gap

Single-stop assignment creates routes that are 35–45% less efficient than multi-stop optimization, even when each individual assignment seems logical.

The dispatcher’s dilemma: assign jobs one-by-one as they arrive, or batch them and optimize the whole day? AI’s advantage is that it can re-optimize the entire route every time a new job is added—giving you the best of both approaches.

The 2-Minute Route Planning Workflow in FieldCamp

FieldCamp’s Route Optimization screen is the ONLY place in the system where dispatchers can visualize and adjust multi-stop sequences before confirming. Here’s exactly how to use it:

Step-by-Step Workflow

Step 1: Create Jobs with “Anytime” Scheduling

When entering jobs, mark them as date-specific but time-flexible. This gives the optimizer maximum freedom to sequence efficiently.

Step 2: Navigate to Route Optimization

Go to: Team Tracking → Route Optimization

Step 3: Select Technician and Date Range

Choose the technician whose route you want to optimize and the date(s) to include.

Step 4: Click “Optimize Route”

The system evaluates all constraints and generates the optimal sequence.

Step 5: Review Optimized Sequence

You’ll see:

• The new stop order
• Total drive time (before and after)
• ETA predictions for each stop
• Any constraint warnings

Dispatchers typically review routes using different calendar views for dispatching, depending on workload and geography.

Step 6: Confirm and Dispatch

Click confirm to push the optimized route to the technician’s mobile app. They receive updated:

• Stop sequence
• Turn-by-turn navigation
• Customer ETAs
• Job details for each stop

Dispatchers maintain full visibility and control through team management, ensuring route changes stay aligned with workload and availability.

What makes this workflow faster is FieldCamp’s AI Command Center—a command-centric chat interface that lets dispatchers assign routes and manage daily tasks without switching screens.

Real Example: HVAC Dispatcher Workflow

An HVAC dispatcher creates 10 Tuesday tune-ups, all marked “Anytime.”

1. Opens Team Tracking → Route Optimization
2. Selects Tech #3 and Tuesday’s date
3. Clicks Optimize
4. System returns route with 2.1 hours drive time (vs. 4.3 hours unoptimized)
5. Reviews the sequence, confirms, and dispatches

Total workflow time: 1 minute 47 seconds (Measured across 50 FieldCamp dispatcher sessions).

Single-Tech vs. Multi-Tech Modes

FieldCamp supports two optimization modes:

• Single-Tech Mode: Perfect one technician’s route
• Multi-Tech Rebalancing: Redistribute jobs across the entire team for optimal balance

Use single-tech mode for daily route cleanup. Use multi-tech mode when you need to rebalance workload across your crew.

When Multi-Stop Optimization Breaks Down?

Route optimization delivers minimal gains when:

Scenarios Where Optimization Struggles

All Jobs Have Narrow Time Windows

When 80%+ of jobs have fixed 2-hour or narrower windows, optimization savings drop to 10–15% compared to the typical gains with flexible scheduling. The algorithm can’t cluster geographically because the clock dictates the sequence.

Jobs Spread Across 100+ Mile Service Area

If jobs are too far apart, geographic clustering doesn’t help. The technician has to drive long distances regardless of sequence.

Technician Has Only 3 Jobs

With very few stops, the optimization overhead isn’t worth it. There are only 6 possible sequences for 3 jobs—a dispatcher can eyeball the best one.

When Manual Override Makes Sense?

Sometimes the dispatcher knows something the algorithm doesn’t:

• VIP customer requests a specific time
• Emergency overrides the optimal sequence
• Technician has a personal relationship with a customer
• Weather or road conditions affect a specific area

AI should handle the heavy lifting. Dispatchers should handle the exceptions.

Real Example: Limited Optimization Potential

An electrical company has 6 jobs with 1-hour windows spread across 60 miles. AI optimization saves only 8 minutes vs. manual routing because time windows dictate sequence regardless of geography.

How FieldCamp Handles This?

FieldCamp’s Route Optimization screen allows dispatchers to create unordered job lists, mark them as “Anytime,” and generate optimized multi-stop routes using Timefold (OptaPlanner) deep optimization.

What Makes FieldCamp Different?

Unlike basic routing tools that only minimize distance, FieldCamp’s optimizer balances multiple factors simultaneously:

• Drive time: Minimize total time on the road
• Time window compliance: Respect customer appointments
• Job dependencies: Ensure prerequisites happen first
• Technician skills: Match jobs to qualified techs
• Workload fairness: Balance jobs across the team

This produces routes that work in the real world, not just on paper. FieldCamp achives this seamless automation through it’s robust AI dispatch scheduling feature launch, where assignments and routes adapt continuously throughout the day.

Proof Point

FieldCamp customers report 30–40% reduction in weekly drive time after switching from manual route planning to AI optimization, with zero increase in missed time windows.

Key Capabilities

Single-Tech Optimization
Perfect one technician’s route for maximum efficiency.

Multi-Tech Rebalancing
Redistribute jobs across your entire team to balance workload and minimize total drive time.

Emergency Insertion
When urgent jobs arrive mid-route, AI inserts them at the optimal position and re-sequences remaining stops.

Before/After Comparison
See exact savings before confirming:

Total time: under 120 seconds.

This accuracy comes from deep ML logic described in machine learning models on which an AI dispatcher is trained — driving FieldCamp’s route optimization engine.