An integrated simulation-optimization framework to optimize search and treatment path for controlling a biological invader

Invasive species cause large economic losses and are difficult to control due to the high cost of locating and treating the continual emergence of new individuals. The national strategy against invasive species encompasses prevention, early detection, and rapid response. In particular, determining an optimal route for search and treatment under limited budget is critical to reducing management costs. In this paper, we present a new integrated simulation-optimization framework to effectively search and treat invasive species under a limited management budget. The simulation mimics invader growth over a landscape and 12 years, while a bio-economic optimization model finds an optimal search and treatment path to minimize its economic damage to agricultural production. Our optimization model is new in the sense that it prescribes the optimal path for searching and treating sites for controlling the invader. This study is also the first in the literature to present and combine simulation and optimization models of the complex bio-economic search-path problem, and to prescribe a pathway for search and treatment that is applicable to the real-world management of invasive species. Our case study data and parameter calibration are based on the large-scale field data on Sericea, an aggressive invasive plant threatening the Great Plains of the U.S., collected over twelve pastures in Kansas during the past two years. Solving simulation and optimization models in a consecutive fashion provides a considerable computational advantage to find an optimal solution to practical size problems in a reasonable time. Our results imply that applying yearly treatment with a slow search-and-treatment speed results in the biggest bang for the buck under most invasion scenarios.