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1271. Robust Network-based Optimization for Multi-Period Fuel Treatment
Invited abstract in session MA-12: Optimization in Agriculture, stream OR in Agriculture and Forestry .
Monday, 8:30-10:00Room: 13 (building: 116)
Authors (first author is the speaker)
| 1. | Tomas Lagos
|
| Discipline of Business Analytics, The University of Sydney | |
| 2. | Nam Ho-Nguyen
|
| The University of Sydney | |
| 3. | Dmytro Matsypura
|
| Discipline of Business Analytics, Business School, The University of Sydney | |
| 4. | Oleg Prokopyev
|
| University of Pittsburgh |
Abstract
Wildfires pose grave risks to human life, health, and infrastructure. To address these challenges, proactive fuel treatment strategies are crucial before each fire season. However, managing fuel treatment resources in south-east Australia becomes problematic due to species protection and forest maturity considerations. Existing models to tackle the multi-period problem consider inaccurate approximations based on estimated parameters in order to design treatment plan schedules. Our study extends previous work and introduces a robust modeling approach for devising multi-year treatment strategies. Initially, we introduce a new, more efficient formulation to solve the deterministic counterpart of the problem, which is then extended to account for a given fixed noise that surges the amount of fuel load due to uncertainties in vegetation growth and treatment effect. We then expand this formulation to accommodate a fixed noise factor, representing uncertainties in vegetation growth and treatment effects. Subsequently, we adapt the model to a robust setting, allowing ``nature'' to select values from a decision-dependent uncertainty set. Our polynomial size robust mixed-integer optimization model incorporates worst-case scenarios from the planner's objective perspective of fuel growth and treatment effects, offering adjustable uncertainty levels. Moreover, our modeling approach provides flexibility for planners, enabling them to minimize the total fuel load or optimize network connect
Keywords
- Robust Optimization
- Forestry Management
- OR in Environment and Climate change
Status: accepted
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