A spatial optimization model for resource allocation for wildfire suppression and resident evacuation. (December 2019)

• Record Type: Journal Article
• Authors: Zhou, Siqiong; Erdogan, Ayca
• Is Part Of: Computers & industrial engineering. Volume 138(2019)
• Publication Date: 2019-12
• Subjects: Wildfire management -- Goal programming -- Stochastic programming; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.106101
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: The model considers residents at risk and resource allocation for wildfires. Scenarios consider wind direction, wind speed, and humidity. Constant problem size regardless of geographical area affected in scenarios. Short computation time to solve large size WUI wildfire cases. Experiments demonstrate trade-offs to provide insights to decision makers. Abstract: Wildland-urban interface wildfires have been a significant threat in many countries. This paper presents an integer two-stage stochastic goal programming model for comprehensive, efficient response to a 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. The 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. Numerical experiments show that this model works well to find solutions by considering a trade-off between two objectives; and varying cell size based on scenarios, reduces problem dimension and improves solution time.