A model for solving the prescribed burn planning problem.

Area of Science:

• Environmental Management
• Operations Research
• Forestry Science

Background:

• Increasing wildfire frequency necessitates proactive fuel management strategies.
• Long-term planning for fuel reduction activities like prescribed burning is crucial for mitigating wildfire impacts.
• Sophisticated mathematical optimization methods are being explored for complex land management decisions.

Purpose of the Study:

• To develop and compare a mixed integer programming (MIP) model with heuristic approaches for optimizing wildfire fuel reduction scheduling.
• To evaluate the trade-offs between computational expense and solution quality for different optimization methods.
• To determine the most practical approach for large-scale, long-term fuel management planning.

Main Methods:

• A mixed integer programming (MIP) model was formulated to minimize fuel load over a planning horizon, considering vegetation types and fire tolerance.
• The MIP model was compared against two heuristic approaches: one using the Knapsack Problem (KP) and another employing a greedy heuristic.
• All methods were assessed for model tractability, computational time, and objective values using data from Victoria, Australia.

Main Results:

• The exact MIP approach was computationally limited, only providing solutions for up to a 15-year planning horizon.
• Heuristic approaches successfully solved problems for 100-year or longer planning horizons.
• No substantial differences in solution quality were observed between the exact MIP and heuristic methods.

Conclusions:

• Heuristic methods are preferable to exact MIP for practical, large-scale, long-term wildfire fuel management planning due to superior tractability.
• The choice of optimization method significantly impacts the feasibility of planning horizons in fuel management.
• Simpler heuristic approaches can achieve comparable results to complex optimization models in this domain.