Elevate Healthcare

Service territory scenarios

Cost-first optimization · annualized results

Scenarios
Total Cost
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Cost Delta vs N=0
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Hire Payroll
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Install Profit Enabled
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Net Economic Value
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Break-Even Units
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Family-weighted patient-sim install-only view

Scenario Placements

No new-hire placements in this scenario.
Selected scenario results from the cost-first optimization. Load ratios are modeled capacity proxies. Install cards show family-weighted patient-sim install-only upside.
Historical Assignments

Actual technician dispatch record

Travel Cost Comparison

Modeled from technician base locations, not actual expense records

Total Appointments
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Est. Historical Travel Cost
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Est. Optimized Cost (N=0)
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Est. Potential Savings
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Coverage Assignments

Active techs use current bases. Former and special historical-only techs use archived bases when available; estimated bases are labeled. All cost figures annualized.
Blank Slate

Modeled rebuild with all hires placed from scratch

Scenarios

Modeled Cost

Travel cost only, excludes hire payroll

Total Appointments
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Est. Total Travel Cost
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Est. Annual Travel Cost
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Placements
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Placement List

Blank Slate uses the same modeled travel-cost rules as the optimizer, but treats all hires as new fully trained technicians and does not use current technician locations. Coverage dots are reconstructed from node-level solver quotas for visualization.
2025 Service Appointments

Actual 2025 workbook appointments, split between regular patient sims and LearningSpace.

View
Selected View
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Appointments Plotted
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This tab is a direct map of actual 2025 service appointments from the workbook. It does not use solver assignments or hiring logic.
Heat Map

Optimization-scope US service appointment demand shown as a heat surface over actual appointment points.

— appointments plotted

Heat Map uses the same US-only demand rows as the optimization model, with each service appointment counted once.
Operational Zones

Supported airports are colored by the solver's saved operational-zone labels. Appointment dots are colored by the zone each row inherits from its saved nearest supported airport.

Supported Airports
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Demand Appointments
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Zone Legend

Jump Rules

No colored regions are drawn here because the repo does not define zone polygons. The real logic is airport-based: zones live on supported airports, and Step 06 copies that zone onto each demand row through nearest_hub_airport.