Home > Research > Publications & Outputs > Using ℓp-norms for fairness in combinatorial op...

Electronic data

  • fairness

    Rights statement: This is the author’s version of a work that was accepted for publication in Computers and Operations Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers and Operations Research, 120, 2020 DOI: 10.1016/j.cor.2020.104975

    Accepted author manuscript, 364 KB, PDF document

    Embargo ends: 22/10/21

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

Using ℓp-norms for fairness in combinatorial optimisation

Research output: Contribution to journalJournal articlepeer-review

Published
Article number104975
<mark>Journal publication date</mark>1/08/2020
<mark>Journal</mark>Computers and Operations Research
Volume120
Number of pages11
Publication StatusPublished
Early online date22/04/20
<mark>Original language</mark>English

Abstract

The issue of fairness has received attention from researchers in many fields, including combinatorial optimisation. One way to drive the solution toward fairness is to use a modified objective function that involves so-called ℓp-norms. If done in a naive way, this approach leads to large and symmetric mixed-integer nonlinear programs (MINLPs), that may be difficult to solve. We show that, for some problems, one can obtain alternative MINLP formulations that are much smaller, do not suffer from symmetry, and have a reasonably tight continuous relaxation. We give encouraging computational results for certain vehicle routing, facility location and network design problems.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Computers and Operations Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers and Operations Research, 120, 2020 DOI: 10.1016/j.cor.2020.104975