The Generalized Reserve Set Covering Problem with Connectivity and Buffer Requirements

TL;DR

This paper introduces a new optimization model for designing nature reserves that ensures connectivity and buffer zones, using advanced integer programming techniques and tested on real-world data for practical conservation planning.

Contribution

It extends the classical Reserve Set Covering Problem to include connectivity and buffer requirements, providing a flexible and effective solution framework.

Findings

The model effectively handles real-world reserve design challenges.

The solution approach produces high-quality solutions within reasonable times.

The framework adapts to various decision-maker requirements.

Abstract

The design of nature reserves is becoming, more and more, a crucial task for ensuring the conservation of endangered wildlife. In order to guarantee the preservation of species and a general ecological functioning, the designed reserves must typically verify a series of spatial requirements. Among the required characteristics, practitioners and researchers have pointed out two crucial aspects: (i) connectivity, so as to avoid spatial fragmentation, and (ii) the design of buffer zones surrounding (or protecting) so-called core areas. In this paper, we introduce the Generalized Reserve Set Covering Problem with Connectivity and Buffer Requirements. This problem extends the classical Reserve Set Covering Problem and allows to address these two requirements simultaneously. A solution framework based on Integer Linear Programming and branch-and-cut is developed. The framework is enhanced by valid inequalities, a construction and a primal heuristic and local branching. An extensive computational study on grid-graph instances and real-life instances based on data from three states of the U.S. and one region of Australia is carried out to assess the suitability of the proposed model to deal with the challenges faced by decision-makers in natural reserve design. The results show, on the one hand, the flexibility of the proposed models to provide solutions according to the decision-makers' requirements, and on the other hand, the effectiveness of the devised algorithm for providing' good solutions in reasonable computing times.