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Project Stress-Testing

Wildfire Response Drone Optimization Model

We modeled how emergency-response leaders could allocate drone resources across high-risk wildfire regions in Victoria, Australia.

The objective was to determine the optimal number and mix of surveillance and radio-repeater drones to support detection, communication coverage, and field response during extreme fire events, balancing operational coverage, equipment cost, and response reliability.

Resource AllocationRisk ForecastingOperations

The challenge

Wildfire response is a complex resource-allocation problem. Leaders must decide how much equipment to buy, where to deploy it, how to maintain communication across difficult terrain, and how to prepare for rising fire activity.

Our approach

We built a model evaluating drone quantity, drone type, communication coverage, fire-event likelihood, terrain constraints, and projected equipment costs. The analysis included:

  • Estimating the optimal number of drones needed
  • Comparing the mix of surveillance and radio-repeater drones
  • Modeling the likelihood of extreme fire events over the next decade
  • Optimizing placement for different fire sizes and terrains
  • Evaluating equipment cost increases over time
  • Producing a budget-style recommendation for decision-makers

Why it matters for businesses

Although this project focused on wildfire response, the same principles apply directly to business operations. Companies face the same questions: How much capacity do we need? Where should resources be placed? What happens if demand spikes? How do we balance cost, risk, and reliability? When does more equipment, labor, or inventory stop improving the system?

We use the same analytical approach to help businesses model operational decisions before they become expensive mistakes.

Core insight

Good decisions are not made by looking at one variable in isolation.

The best decisions come from understanding how cost, risk, capacity, geography, timing, and demand interact as one system.

Have a decision worth modeling?

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