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The call for equity: simulation optimization models to minimize the range of waiting times

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  • Muer Yang
  • Theodore Allen
  • Michael Fry
  • W. Kelton

Abstract

Providing equal access to public service resources is a fundamental goal of democratic societies. Growing research interest in public services (e.g., health care, humanitarian relief, elections) has increased the importance of considering objective functions related to equity. This article studies discrete resource allocation problems where the decision maker is concerned with maintaining equity between some defined subgroups of a customer population and where non-closed-form functions of equity are allowed. Simulation optimization techniques are used to develop rigorous algorithms to allocate resources equitably among these subgroups. The presented solutions are associated with probabilistic bounds on solution quality. A full-factorial experimental design demonstrates that the proposed algorithm outperforms competing heuristics and is robust over various inequity metrics. Additionally, the algorithm is applied to a case study of allocating voting machines to election precincts in Franklin County, Ohio. [Supplementary material is available for this article. Go to the publisher’s online edition of IIE Transactions for the Appendices to the article.]

Suggested Citation

  • Muer Yang & Theodore Allen & Michael Fry & W. Kelton, 2013. "The call for equity: simulation optimization models to minimize the range of waiting times," IISE Transactions, Taylor & Francis Journals, vol. 45(7), pages 781-795.
    • Handle: RePEc:taf:uiiexx:v:45:y:2013:i:7:p:781-795
    DOI: 10.1080/0740817X.2012.721947
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    Cited by:

    1. Feng, Yuanjun & Song, Dong-Ping & Li, Dong & Zeng, Qingcheng, 2020. "The stochastic container relocation problem with flexible service policies," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 116-163.
    2. Schmidt, Adam & Albert, Laura A., 2022. "Designing pandemic-resilient voting systems," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    3. Sandra Sülz & Andreas Fügener & Michael Becker-Peth & Bernhard Roth, 2024. "The potential of patient-based nurse staffing – a queuing theory application in the neonatal intensive care setting," Health Care Management Science, Springer, vol. 27(2), pages 239-253, June.
    4. Karsu, Özlem & Morton, Alec, 2015. "Inequity averse optimization in operational research," European Journal of Operational Research, Elsevier, vol. 245(2), pages 343-359.
    5. Yang, Yongjian & Yin, Yunqiang & Wang, Dujuan & Ignatius, Joshua & Cheng, T.C.E. & Dhamotharan, Lalitha, 2023. "Distributionally robust multi-period location-allocation with multiple resources and capacity levels in humanitarian logistics," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1042-1062.
    6. Cao, Wenwei & Çelik, Melih & Ergun, Özlem & Swann, Julie & Viljoen, Nadia, 2016. "Challenges in service network expansion: An application in donated breastmilk banking in South Africa," Socio-Economic Planning Sciences, Elsevier, vol. 53(C), pages 33-48.
    7. Yang, Muer & Wang, Xinfang (Jocelyn) & Xu, Nuo, 2015. "A robust voting machine allocation model to reduce extreme waiting," Omega, Elsevier, vol. 57(PB), pages 230-237.
    8. Bélanger, V. & Ruiz, A. & Soriano, P., 2019. "Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 1-23.
    9. Gérard P. Cachon & Dawson Kaaua, 2022. "Serving Democracy: Evidence of Voting Resource Disparity in Florida," Management Science, INFORMS, vol. 68(9), pages 6687-6696, September.
    10. Feng, Yuanjun & Song, Dong-Ping & Li, Dong & Xie, Ying, 2022. "Service fairness and value of customer information for the stochastic container relocation problem under flexible service policy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    11. Liu, Kanglin & Li, Qiaofeng & Zhang, Zhi-Hai, 2019. "Distributionally robust optimization of an emergency medical service station location and sizing problem with joint chance constraints," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 79-101.
    12. Kumar, Sameer & Yang, Muer & Goldschmidt, Kyle H., 2018. "Will aging voting machines cause more voters to experience long waits?," International Journal of Production Economics, Elsevier, vol. 198(C), pages 1-10.
    13. Thomas W. Lucas & W. David Kelton & Paul J. Sánchez & Susan M. Sanchez & Ben L. Anderson, 2015. "Changing the paradigm: Simulation, now a method of first resort," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(4), pages 293-303, June.

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