All student projects Opportunities
Scaling Up Combinatorial Optimisation for Conservation Planning
Combinatorial optimisation (mixed integer linear programming, MILP) has transformed how practitioners conduct conservation planning to address trade-offs across land-use, food and biodiversity needs. Yet algorithms used for conservation planning are frequently too slow for large-scale problems, and recent formulations of these optimisation problems offer ways to exploit their structure to speed them up.
In this project, students will explore how much techniques such as column generation, aggregation and disaggregation can speed up conservation planning MILP algorithms, focusing on the minimum set (Project A) and minimum shortfall (Project B) formulations. This can enable interactive, real-time decision support for policymakers seeking to optimise simultaneously for land-use and biodiversity goals.