A Unified Framework for Analyzing and Optimizing a Class of Convex Fairness Measures
提出了一个统一框架，将多种凸公平性度量纳入一个参数化类别，并给出了优化问题的统一重构和求解方法，通过资源分配和设施选址实验验证了计算效率。
Fairness concerns arise naturally across a wide range of decision-making contexts and application domains. Addressing these concerns requires integrating fairness measures into optimization models; however, quantifying fairness, as well as formulating and solving fairness-promoting optimization problems, remain significant challenges. In “A Unified Framework for Analyzing and Optimizing a Class of Convex Fairness Measures,” M. Y. Tsang and K. S. Shehadeh propose a new framework that unifies different fairness measures into a general, parameterized class of convex fairness measures. They introduce a unified framework for optimization problems with a convex fairness measure objective or constraint, including unified reformulations and solution methods. Additionally, they establish mechanisms for quantifying the impact of employing different convex fairness measures on the optimal solutions to the resulting fairness-promoting optimization problem. Numerical experiments, including applications to resource allocation and facility location, demonstrate the computational efficiency of the unified framework over traditional ones.