Fairness in Maximal Covering Location Problems
Not on the map. Why?
Abstract (arXiv)
This paper provides a general mathematical optimization based framework to incorporate fairness measures from the facilities' perspective to Discrete and Continuous Maximal Covering Location Problems. The main ingredients to construct a function measuring fairness in this problem are the use of: (1) ordered weighted averaging operators, a family of aggregation criteria very popular to solve multiobjective combinatorial optimization problems; and (2) α-fairness operators which allow to generalize most of the equity measures. A general mathematical optimization model is derived which captures the notion of fairness in maximal covering location problems. The models are firstly formulated as mixed integer non-linear optimization problems for both the discrete and the continuous location spaces. Suitable mixed integer second order cone optimization reformulations are derived using geometric properties of the problem. Finally, the paper concludes with the results obtained on an extensive battery of computational experiments on real datasets. The obtained results support the convenience of the proposed approach.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Apr 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1.
3 papers.
- 2023Maximizing Submodular Functions for Recommendation in the Presence of Biasescited by 5
- 2022Towards Fairness-Aware Multi-Objective Optimizationcited by 4
- 2025Proportional Selection in Networkscited by 1
- 2025Proportional Selection in Networks5 Feb 2025
- 2023Maximizing Submodular Functions for Recommendation in the Presence of Biases3 May 2023
- 2022Towards Fairness-Aware Multi-Objective Optimization22 Jul 2022