Master of Science (MS)
Industrial and Systems Engineering
Goal programming, Stochastic programming, Wildfire management
Wildland-urban interface wildfires have been a significant threat in many countries. This thesis presents an integer two-stage stochastic goal programming model for comprehensive, efficient response to wildfire including firefighting resource allocation and resident evacuation. In contrast to other natural disasters, the progression of wildfires depends on not only the probabilistic fire spread scenarios but also decisions made during firefighting. The proposed model optimizes the resource preparations before the fire starts and resource allocation decisions during the fire event. This model takes into account different wildfire spread scenarios and their impact on high-risk areas. The two objectives considered are minimizing the total cost of operations and property loss and minimizing the number of people at risk to be evacuated. A case study based on Santa Clara County in California, United States of America, is presented to demonstrate the model performance. Quantitative experiments show that this model can help to find efficient solutions by considering a trade-off between two objectives, and varying cell size based on scenarios reduces problem dimension and improves solution time.
Zhou, Siqiong, "A Spatial Optimization Model for Resource Allocation for Wildfire Suppression and Resident Evacuation" (2019). Master's Theses. 5022.