Equity-driven facility location: A two-stage robust optimization approach
研究了在需求不确定下将公平性纳入p-中位设施选址模型的计算挑战，提出两阶段鲁棒优化框架，并设计了精确与非精确列与约束生成算法，通过温哥华案例验证了模型的可处理性和公平性指标的有效性。
This paper explores the computational challenge of incorporating equity in p-median facility location models under uncertain demand and discusses how two-stage robust programming can be employed to address the challenge. Our research evaluates various equity measures appropriate for facility location modeling and proposes a novel approach to reformulating the problem into a two-stage robust optimization framework, enhancing computational efficiency caused by incorporating equity and uncertainty into these models. We provide two solution algorithms: an exact and an inexact column-and-constraint generation (C&CG) method. Our findings suggest that although the exact C&CG method generally outperforms the inexact approach, both methods perform well when the number of variables is small, with the inexact C&CG demonstrating a slight advantage in computational time. We further conduct a detailed evaluation of the tractability of our reformulated model and the effectiveness of various equity measures through a real-world case study of Metro Vancouver.