Optimization and Fine-Tuning Guide

1. How Klau Optimization Works

Unlike generic routing software, Klau was built specifically for roll-off operations. Standard route optimization focuses solely on minimizing driving distance. Klau goes further by using Strategic Value Optimization to maximize the total economic value of your driver's day.

The Optimization Strategy

You can choose the "aggressiveness" of the optimization based on your business model. This is configured in Settings → Optimization:

The Chain-First Philosophy

Klau's core insight is simple: if a driver picks up a 20-yard container and the next delivery also needs a 20-yard, why return to the yard? The driver can go straight to the dump, empty the container, and deliver it to the next customer. This is container chaining, and it's the foundation of Klau's optimization.

Dynamic Dump Site Selection

Klau doesn't just send drivers to the nearest dump site. It dynamically selects the best disposal facility for every load based on:

• Material Licensing: Klau filters dump sites based on the materials they are licensed to accept. If you're hauling scrap metal, Klau won't even suggest a general waste transfer station.
• Economic Net Value: Klau balances tipping fees (or scrap values) against drive time costs to find the most profitable destination.
• Operating Hours: Real-time arrival estimation ensures drivers never show up to a closed gate.

What Makes Roll-Off Optimization Unique

• Equipment Continuity: Unlike package delivery where every stop is independent, roll-off jobs are linked by the physical container being transported.
• Job Type Sequencing: Pickups generate containers. Deliveries consume them. Dump and Returns are self-contained. The sequence matters.
• Yard Returns Are Expensive: Every unnecessary trip back to the yard costs 20-40 minutes of drive time, fuel, and wear on equipment.

Familiarity-Aware Driver Assignment

Optimization isn't just about the truck—it's about the driver. Klau tracks driver site history to improve efficiency and safety:

• Site History: Klau records every visit a driver makes to a job site.
• Familiarity Preference: The optimizer prioritizes assigning jobs to drivers who have visited that specific site before.
• Efficiency & Safety: Drivers who know the site entrance, gate codes, and preferred container placement locations are faster and less likely to have incidents.

The Optimization Score (0-100)

After every optimization, Klau provides a Chain Scorecard rated from 0 to 100. This score reflects how well the optimized routes take advantage of chaining opportunities and minimize waste.

2. Economic Optimization: Net Value Model

Standard dispatchers often choose the nearest dump site to save time. However, Klau's Economic Optimization model understands that the cheapest disposal isn't always the closest one.

Balancing Tipping Fees vs. Drive Time

Klau calculates the "Net Value" of every dump site option for every job. This model accounts for:

Estimated Weight Trends

To make economic decisions before the truck hits the scale, Klau estimates the weight of every job based on industry trends for the container size (e.g., assuming 400 lbs per yard as a baseline). You can override these estimates for specific jobs if you know they'll be particularly heavy or light.

3. Transparency & Simulation

Optimization should never be a "black box." Klau provides tools to help you understand why the engine made specific choices and allows you to simulate changes safely.

Optimization Insights (Attribution)

Every job card on the dispatch board now includes an Optimization Insight. Hover over the sparkle icon to see exactly why that job was assigned to that driver:

• Economic Breakdown: See the specific dollar impact of drive time versus disposal savings.
• Familiarity Bonuses: See how much weight the optimizer gave to a driver's history with that site.
• Chain Opportunity: See if a job was assigned specifically to set up a lucrative "back-to-back" chain later in the day.

What-If Simulation (Real-time Feedback)

When you drag a job to a new driver or sequence, Klau runs a What-If Simulationin the background. Before you even drop the job, you'll see a preview of the impact:

Route Confidence Levels

Klau tracks Route Confidence based on historical SMS drive time data. Segments on your dispatch board are color-coded based on how much "real-world" data we have for that route:

• High Confidence (Green): We have multiple recent driver observations for this route. ETAs are very reliable.
• Medium Confidence (Yellow): We have limited data or old observations.
• Low Confidence (Red): We have zero historical data for this specific segment. Klau is using routing API estimates only.

4. Understanding Chain Optimization

Chain optimization is the secret sauce that separates Klau from generic routing software. Understanding how chains work will help you make better manual adjustments and appreciate why Klau sequences jobs the way it does.

What is Container Chaining?

A chain occurs when a driver can complete multiple jobs without returning to the yard for a different container. The container picked up from one site becomes the container delivered to the next site (after being dumped).

How Chains Eliminate Yard Returns

Without chaining, the same three jobs would require multiple yard visits:

The Value of a Single Chain Link

Each successful chain link (where the optimizer connects a pickup to a subsequent delivery of the same size) typically saves:

• 20-40 minutes of drive time (depending on distance from dump to yard)
• 15-25 miles of driving (fuel, wear, emissions)
• $25-50 in operating costs per eliminated yard return

A driver who completes a 4-link chain instead of 4 independent jobs could save over 2 hours of driving time in a single day. Over a week, that's an extra day of productive capacity.

Chain Types

5. The Optimization Score Breakdown

The Chain Scorecard isn't a single number—it's composed of five weighted factors. Understanding each factor helps you interpret why a particular optimization scored the way it did and what you can do to improve it.

6. Running Optimization

Klau offers two optimization modes to balance speed and accuracy. Understanding when to use each mode helps you make the best decision for your situation.

The Auto-Optimize Button

Located in the dispatch board header, the Auto-Optimize button triggers Klau's optimization engine. It analyzes all unassigned jobs, available drivers, and current assignments to produce an optimal route plan.

Quick Optimize vs Real Drive Times

When to Use Each Mode

• Early Morning Planning: Start with Quick Optimize to see the overall shape of the day. Run Real Drive Times before publishing.
• Adding Rush Jobs: Use Quick Optimize for fast iterations. Apply when satisfied.
• Urban Areas: Real Drive Times matters more where traffic varies significantly by time of day.
• Rural Areas: Quick Optimize is often accurate enough since routes are simpler.

Understanding Optimization Results

After optimization completes, Klau presents the results in a modal dialog showing:

• Chain Scorecard: The overall score with component breakdown
• Routes Preview: Visual representation of each driver's route
• Key Metrics: Total miles, yard returns eliminated, time windows hit
• Warnings: Any time window conflicts or constraint violations

7. Fine-Tuning Routes Manually

Optimization provides an excellent starting point, but sometimes local knowledge or customer relationships require manual adjustments. Klau makes fine-tuning easy while keeping you informed about the impact.

When to Override Optimization

• Driver Knowledge: "Mike knows that site has a tricky entrance—give it to him"
• Customer Relationships: "This customer specifically requested Sarah"
• Equipment Needs: "Only Truck #5 can access that alley"
• Real-Time Changes: "Driver is already nearby—add this job to their route"
• Balancing Preferences: "Give the new driver an easier route today"

Drag-and-Drop Job Reordering

To change the sequence of jobs within a driver's route, simply drag a job card up or down within the column. Klau instantly recalculates:

• Estimated arrival times for all subsequent jobs
• Whether any time windows are now violated
• Impact on chains (breaking or creating new ones)
• Updated total route time and mileage

Moving Jobs Between Drivers

Drag a job from one driver's column to another. Klau recalculates both routes and shows the impact on each driver's day.

• Source driver: Reduced workload, routes re-sequenced
• Target driver: Increased workload, job inserted at optimal position
• Both routes: Chain opportunities reassessed

The Impact of Manual Changes on Score

After any manual adjustment, the Chain Scorecard updates in real-time. A small score drop (1-3 points) for a good business reason is fine. A large drop (10+ points) suggests you might want to reconsider or let Klau re-optimize around your constraints.

8. Job Pinning Strategies

Pinning is how you tell Klau "this constraint is non-negotiable." Klau will optimize around your pins, never violating them. Strategic pinning gets you the best of both worlds: optimization efficiency plus business requirements.

Types of Pins

Combining Multiple Pins

Pins can be combined for precise control:

• Driver + Time: "Mike at 8:00 AM" — locks both who and when
• Driver + Position: "Sarah's first stop" — driver-specific sequencing
• Time + Position: "8:00 AM and must be first" — redundant but explicit

When Pinning Hurts Optimization

Every pin reduces Klau's flexibility. Use pins strategically:

9. Using the Map View

The Map View provides a visual perspective that complements the dispatch board. Patterns that are hard to see in a list become obvious on a map—like a driver crossing town twice or jobs clustered perfectly for chaining.

Visualizing Routes Geographically

Each driver's route is displayed as a colored line connecting their jobs in sequence. The yard appears as a central hub, with routes radiating outward.

• Numbered Markers: Each job shows its sequence number within the route
• Color Coding: Each driver has a distinct route color for easy identification
• Route Lines: Connect jobs in order, showing the actual path
• Dump Sites: Marked distinctly to show where containers are emptied

Identifying Inefficient Patterns

Look for these red flags in the map view:

Spotting Clustering Opportunities

Good routes show tight geographic clusters. When you see several jobs from different drivers in the same area, consider:

• Could one driver handle all jobs in that cluster?
• Are there chaining opportunities by container size?
• Would resequencing reduce total distance?

Map-Based Job Reassignment

You can reassign jobs directly from the map view:

1. Click any job marker to open the job details
2. Select "Reassign" and choose a different driver
3. Watch the route lines update in real-time
4. See the impact on both affected drivers' routes

10. Handling Special Situations

The best optimization plan meets reality the moment drivers leave the yard. Here's how to handle common disruptions while preserving as much efficiency as possible.

Rush Jobs Mid-Day

When a high-priority job comes in after routes are published:

1. Add the job and mark it as High Priority or Urgent
2. Click "Re-Optimize" — Klau will insert it optimally
3. Review which driver got the job and their updated ETA
4. Check if any existing time windows are now at risk
5. Republish the affected driver's route

Driver Callouts

When a driver calls in sick or is unavailable:

1. Mark the driver as unavailable in the system
2. Their jobs move back to the unassigned pool
3. Run optimization to redistribute among remaining drivers
4. Review workload balance—overtime may be needed
5. Consider which jobs can be rescheduled if overloaded

Truck Breakdowns

When a truck breaks down mid-route:

1. Mark the truck as out of service
2. Driver's remaining jobs return to unassigned
3. If another truck is available, reassign the driver
4. Re-optimize to redistribute remaining work
5. Notify affected customers of delays if needed

Weather Delays

Severe weather affects everyone equally. Consider:

• Delay Start: Push all routes back by the same amount
• Reduce Volume: Reschedule non-urgent jobs to tomorrow
• Adjust Times: Re-run optimization with updated service times (wet conditions = slower)
• Customer Communication: Proactive delay notices preserve trust

Re-Optimizing After Changes

After any significant change, ask yourself:

• One job change: Manual adjustment is usually faster
• Multiple jobs affected: Re-optimize to find best new solution
• Driver removed: Always re-optimize—too many variables to do manually
• Late in day: Manual adjustments often better than full re-optimization

11. Optimization Settings

Klau's optimization engine uses configurable parameters that should match your operation's reality. Proper settings lead to better optimization and more accurate ETAs.

Company Hours Configuration

Set your standard operating hours so Klau doesn't schedule jobs outside your window:

• Start Time: Earliest a driver leaves the yard (typically 6:00-7:00 AM)
• End Time: Latest a driver should return (typically 4:00-5:00 PM)
• Saturday/Sunday: Enable weekend operations if applicable

Service Time Estimates

Default service times are based on industry averages. Adjust them to match your operation:

Buffer Time Between Jobs

Add realistic buffer for driver needs:

• Pre-Trip Inspection: 15 minutes (DOT requirement at start of day)
• Per-Job Buffer: 5 minutes (paperwork, bathroom, unexpected delays)
• Post-Trip: 10 minutes (cleanup, paperwork at end of day)

Dump Site Preferences

Configure your dump site options:

• Primary Dump: Default dump site for most jobs
• Alternate Dumps: Used when geography favors a different site
• Material Routing: Specific waste types may require specific facilities
• Dump Site Hours: Klau respects facility operating hours

12. Measuring Optimization ROI

Optimization isn't just about prettier routes—it's about real savings. Here's how to measure and communicate the value Klau delivers.

Tracking Yard Returns Eliminated

Every eliminated yard return represents concrete savings. Klau tracks this automatically in your daily and weekly reports.

Minutes Saved Per Day

Driver time is your most valuable resource. Track:

• Drive Time Reduction: Compare optimized vs. unoptimized route times
• Jobs Per Driver: More efficiency = more capacity per driver
• Overtime Reduction: Track if drivers finish earlier

Fuel Cost Reduction Estimates

Use these industry benchmarks to estimate fuel savings:

• Roll-off trucks: ~5-6 MPG average
• Cost per mile: ~$0.50 fuel only, ~$1.50 all-in
• Each eliminated yard return: ~15-25 miles saved
• Monthly impact: (Yard returns eliminated x miles per return x cost per mile)

Driver Overtime Reduction

When routes are optimized and balanced:

• Drivers complete work within standard hours more often
• Fewer "last job runs long" overtime situations
• More predictable schedules improve driver satisfaction
• Track OT hours before/after Klau implementation

13. Pro Tips from Experienced Dispatchers

These insights come from dispatchers who've been using optimization tools for years. They represent hard-won wisdom about balancing technology with human judgment.

Morning vs Afternoon Optimization Runs

Morning Optimization (Before 6 AM):

• Run on all jobs for the day
• Maximum flexibility—no jobs in progress yet
• Best time for major route changes
• Use Real Drive Times for final confirmation

Afternoon Optimization (After Noon):

• Only affects remaining unfinished jobs
• Good for accommodating rush jobs
• Preserves driver momentum—minimal disruption
• Quick Optimize usually sufficient

Leaving Room for Walk-Ins

Experienced dispatchers know that every day brings unexpected calls. Strategies:

• Buffer Driver: Keep one driver at 80% capacity to absorb rush jobs
• Afternoon Slack: Don't over-schedule the afternoon—it's when rush calls peak
• Geographic Holes: If you expect calls from a certain area, keep a driver nearby

Balancing Optimization with Driver Preferences

Drivers are humans, not routing robots. Consider:

• Familiar Territory: Drivers perform better in areas they know well
• Customer Relationships: Some drivers have rapport with specific customers
• Physical Considerations: Some sites are physically demanding—rotate fairly
• New Driver Training: Pair with easier routes while learning

Building Driver Trust in the System

Drivers may initially resist "computer-generated" routes. Build trust by:

• Explaining the Why: "This sequence eliminates two yard returns"
• Respecting Feedback: When a driver says a route won't work, listen
• Showing Results: Share the savings numbers with the team
• Allowing Adjustments: Drivers on the ground see things the algorithm can't
• Celebrating Wins: Acknowledge when optimization helps complete more jobs