Fairness in Maximal Covering Location Problems

Blanco, Víctor Francisco Sampedro; Ricardo, Gázquez,

doi:10.48550/arxiv.2204.06446

Cited by 1 publication

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, there are also increasing concerns about fairness on facility location problems (FLPs) that aim to find optimal locations of facilities to satisfy the demand of customers. In [166], the notion of fairness is to ensure that the maximization of the covered demand of one facility does not negatively impact the demand coverage of others. In ambulance location problem [160], [167], the fairness is denoted by the maximization of Bernoulli-Nash social welfare [168], i.e., maximizing the joint probability that everyone receives ambulance on time.…”

Section: Fairness In Multi-objective Optimizationmentioning

confidence: 99%

Towards Fairness-Aware Multi-Objective Optimization

Yu¹,

Ma²,

Du³

et al. 2022

Preprint

View full text Add to dashboard Cite

Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such as fair resource allocation problems and datadriven multi-objective optimization problems. This paper aims to illuminate and broaden our understanding of multi-objective optimization from the perspective of fairness. To this end, we start with a discussion of user preferences in multi-objective optimization and then explore its relationship to fairness in machine learning and multi-objective optimization. Following the above discussions, representative cases of fairness-aware multiobjective optimization are presented, further elaborating the importance of fairness in traditional multi-objective optimization, data-driven optimization and federated optimization. Finally, challenges and opportunities in fairness-aware multi-objective optimization are addressed. We hope that this article makes a small step forward towards understanding fairness in the context of optimization and promote research interest in fairness-aware multi-objective optimization.

show abstract