May Webinar


In 2023, the International Union for Conservation of Nature reported that, of the 150,300 evaluated species, over 42,100 are facing extinction. Establishing nature reserves is the most common method for conserving wildlife species, yet it comes with significant economic costs. The need to balance conservation efforts and economic costs has led to the application of operational research methods in reserve design problems. Shengjie's research project focuses on exploring the extent to which simulation optimisation can be used in wildlife reserve design problems, specifically using the Grey Wolf (Canis lupus) as an example wildlife species.

To demonstrate the effectiveness of using simulation optimisation in solving wildlife reserve design problems, particularly through a chance constrained approach, the upcoming webinar will present three illustrative scenarios on Grey Wolf reserve design. The first scenario uses data on the Grey Wolf population in California to show the scale of computational effort required to solve the problem. The second scenario uses a simulation model based pre-screening method to reduce this computational effort while maintaining the statistical guarantee on the selection of the best solutions. The third scenario uses a solution dominance rule-based heuristic to further reduce the computational effort but without statistical guarantee. 

The webinar will conclude by discussing potential research directions in the application of simulation optimisation in wildlife reserve design.

April Shengjie Zhou is a PhD student in the Management Science Department at Lancaster University, where she is also a member of the Simulation and Stochastic Modelling Group. Her PhD research project is supervised by Dr David Worthington, Dr Luke Rhodes-Leader, and Dr Richard Williams, all of whom are from Lancaster University.

She received her BSc in Business Studies (2:1) and MSc in Operational Research and Management Science (Distinction) from Lancaster University. Her research interests lie in wildlife conservation, specifically focusing on applying operational research methods - including simulation, simulation optimisation, and stochastic modelling - to tackle conservation challenges. 

April Zhou