Robust Spatial Optimization for the Invasive Species Management

Rapid diffusion of non-indigenous species and plants has significant impact on environmental threats and global biodiversity loss. To sustain biodiversity, not only do conservation agencies need to create reserves to protect species, but also would they manage invasive species. The optimal cost-effective invasive species management determines strategies to help efficiently eradicate invasive species to minimize their damage to ecosystem and the costs associated with eradication (physical and chemical control). The problem of invasive species management is a difficult problem and involves ecology, economy, and mathematics. It concerns modeling the pattern of spread of the invasive, estimation of control costs, spatial design of the control effort, and accounting for uncertainties in model parameters. To treat uncertainties (perturbation in the parameters), stochastic programming (SP) represents the uncertainty via discrete scenarios in the cases where the stochastic structure of the uncertainty is known. In contrast, robust optimization (RO) constructs a solution that is feasible for any realization of the uncertainty in a given uncertainty set (achieves the best worst-case objective function value). Given the computational efficiency of robust optimization, we are developing a spatial-optimization model to select sites for efficiently controlling invasive species to minimize their ecological damage, as well as to minimize the costs given limited financial resources