Solving multi-objective optimization problems in conservation with the reference point method

TL;DR

This paper introduces a reference point method for multi-objective conservation decision problems, outperforming traditional approaches by incorporating decision-maker preferences interactively, and demonstrates its effectiveness on two ecological management problems.

Contribution

It presents new multi-objective linear programming formulations using the reference point approach for conservation, enhancing decision-making in ecological problems.

Findings

Reference point method outperforms classic methods.

Method is adaptable to various ecological combinatorial problems.

Interactive approach effectively incorporates decision-maker preferences.

Abstract

Managing biodiversity extinction crisis requires wise decision-making processes able to account for the limited of resources available. In most decision problems in conservation biology, several conflicting objectives have to be taken into account. Most methods used in conservation either provide suboptimal solutions or use strong assumptions about the decision-maker's preferences. Our paper reviews some of the existing approaches to solve multi-objective decision problems and presents new multi-objective linear programming formulations of two multi-objective optimization problems in conservation, allowing the use of a reference point approach. Reference point approaches solve multi-objective optimization problems by interactively representing the preferences of the decision-maker with a point in the criteria space, called the reference point. We modelled and solved the following two conservation problems: a dynamic multi-species management problem under uncertainty and a spatial allocation resource management problem. Results show that the reference point method outperforms classic methods while illustrating the use of an interactive methodology for solving combinatorial problems with multiple objectives. The method is general and can be adapted to a wide range of ecological combinatorial problems.