Wildlife reserves serve as a critical tool for conserving wildlife species. The design of such reserves can be formulated as a simulation optimization problem, with the objective of minimizing conservation costs while satisfying species survival constraints. Our research explores this problem formulation and the relevant solution methods, with a particular focus on the Chance Constrained Selection of the Best algorithm. We formulate the problem using a deterministic objective function subject to a probabilistic constraint. To estimate the survival probability under various policies, we have developed a Gray Wolf (Canis lupus) model that simulates the wolves’ dispersal, breeding, and death processes in discrete time
steps. Our poster presents three scenarios that demonstrate the potential use of Simulation Optimization techniques in wildlife conservation