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Fair Optimization and Networks: A Survey

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  • Wlodzimierz Ogryczak
  • Hanan Luss
  • Michał Pióro
  • Dritan Nace
  • Artur Tomaszewski

Abstract

Optimization models related to designing and operating complex systems are mainly focused on some efficiency metrics such as response time, queue length, throughput, and cost. However, in systems which serve many entities there is also a need for respecting fairness: each system entity ought to be provided with an adequate share of the system’s services. Still, due to system operations-dependant constraints, fair treatment of the entities does not directly imply that each of them is assigned equal amount of the services. That leads to concepts of fair optimization expressed by the equitable models that represent inequality averse optimization rather than strict inequality minimization; a particular widely applied example of that concept is the so-called lexicographic maximin optimization (max-min fairness). The fair optimization methodology delivers a variety of techniques to generate fair and efficient solutions. This paper reviews fair optimization models and methods applied to systems that are based on some kind of network of connections and dependencies, especially, fair optimization methods for the location problems and for the resource allocation problems in communication networks.

Suggested Citation

  • Wlodzimierz Ogryczak & Hanan Luss & Michał Pióro & Dritan Nace & Artur Tomaszewski, 2014. "Fair Optimization and Networks: A Survey," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-25, September.
  • Handle: RePEc:hin:jnljam:612018
    DOI: 10.1155/2014/612018
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    Cited by:

    1. Lehuédé, Fabien & Péton, Olivier & Tricoire, Fabien, 2020. "A lexicographic minimax approach to the vehicle routing problem with route balancing," European Journal of Operational Research, Elsevier, vol. 282(1), pages 129-147.
    2. David Rea & Craig Froehle & Suzanne Masterson & Brian Stettler & Gregory Fermann & Arthur Pancioli, 2021. "Unequal but Fair: Incorporating Distributive Justice in Operational Allocation Models," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2304-2320, July.
    3. Wenhang Bao, 2019. "Fairness in Multi-agent Reinforcement Learning for Stock Trading," Papers 2001.00918, arXiv.org.
    4. Natalia Novikova & Irina Pospelova, 2022. "Germeier’s Scalarization for Approximating Solution of Multicriteria Matrix Games," Mathematics, MDPI, vol. 11(1), pages 1-28, December.
    5. Gutjahr, Walter J., 2021. "Inequity-averse stochastic decision processes," European Journal of Operational Research, Elsevier, vol. 288(1), pages 258-270.
    6. Argyris, Nikolaos & Karsu, Özlem & Yavuz, Mirel, 2022. "Fair resource allocation: Using welfare-based dominance constraints," European Journal of Operational Research, Elsevier, vol. 297(2), pages 560-578.
    7. Violet Xinying Chen & J. N. Hooker, 2023. "A guide to formulating fairness in an optimization model," Annals of Operations Research, Springer, vol. 326(1), pages 581-619, July.
    8. Christ, Quentin & Dauzère-Pérès, Stéphane & Lepelletier, Guillaume, 2019. "An Iterated Min–Max procedure for practical workload balancing on non-identical parallel machines in manufacturing systems," European Journal of Operational Research, Elsevier, vol. 279(2), pages 419-428.
    9. Acuna, Jorge A. & Zayas-Castro, José L. & Charkhgard, Hadi, 2020. "Ambulance allocation optimization model for the overcrowding problem in US emergency departments: A case study in Florida," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    10. Philippe Ezran & Yoram Haddad & Mérouane Debbah, 2019. "Allais’ paradox and resource allocation in telecommunication networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 70(3), pages 337-348, March.
    11. Bevrani, Bayan & Burdett, Robert & Bhaskar, Ashish & Yarlagadda, Prasad K.D.V., 2020. "A multi-criteria multi-commodity flow model for analysing transportation networks," Operations Research Perspectives, Elsevier, vol. 7(C).

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