Journal of the Operational Research Society

, Volume 66, Issue 8, pp 1363–1369 | Cite as

Efficiency and equity tradeoffs in voting machine allocation problems

General Paper

Abstract

Efficiency and equity are the two crucial factors to be considered when allocating public resources such as voting machines. Existing allocation models are all single-objective, focusing on maximizing either efficiency or equity despite the fact that the actual decision-making process involves both issues simultaneously. We propose a bi-objective integer program to analyse the tradeoff between the two competing objectives. The new model quantifies the sacrifice in efficiency in order to achieve a certain improvement in equity and vice versa. Using data from the 2008 United States Presidential election in Franklin County, Ohio, we demonstrate that our model is capable of producing significantly more balanced allocation plans, in terms of efficiency and equity, than current practice or other competing methods.

Keywords

efficiency-equity tradeoff efficient allocation voting operations public service 

References

  1. Allen TT (2013). Delving into the reasons for long lines can bring solutions. Orlando Sentinel, 8 January, http://articles.orlandosentinel.com/2013-01-08/news/os-ed-long-lines-voting-florida-010813-20130107_1_long-lines-ballot-length-turnout, accessed 30 october 2013.
  2. Allen TT and Bernshteyn MB (2006a). Mitigating voter waiting times. Chance 19 (4): 25–36.CrossRefGoogle Scholar
  3. Allen TT and Bernshteyn MB (2006b). Optimal voting machine allocation analysis. In: Hertzberg S (ed). Analysis of May 2006 Primary Cuyahoga County. Election Science Institute: Ohio, pp 71–89.Google Scholar
  4. Belenky AS and Larson RC (2008). To queue or not to queue? Analytics. Spring: 22–26.Google Scholar
  5. Bertsimas D, Farias VF and Trichakis N (2012). On the efficiency-fairness trade-off. Management Science 58 (12): 2234–2250.CrossRefGoogle Scholar
  6. Bérubé JF, Gendreau M and Potvin JY (2009). An exact ɛ-constraint method for biobjective combinatorial optimization problems: Application to the traveling salesman problem with profits. European Journal of Operational Research 194 (1): 39–50.CrossRefGoogle Scholar
  7. Cohon JL (1978). Multiobjective Programming and Planning. Academic Press: New York.Google Scholar
  8. Demir E, Bektas T and Laporte G (2014). The bi-objective pollution-routing problem. European Journal of Operational Research 232 (3): 464–478.CrossRefGoogle Scholar
  9. Edelstein WA and Edelstein AD (2010). Queuing and elections: Long lines, DREs and paper ballots. Proceedings of the 2010 Electronic Voting Technology Workshop/Workshop on Trustworthy Elections, Washington, DC.Google Scholar
  10. Ehrgott M and Gandibleux X (2002). Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys. Springer: Norwell, MA.Google Scholar
  11. Feldman D and Belcher C (2005). Voting experience survey. Democracy at risk: The 2004 election in Ohio. Democratic National Committee. http://a9.g.akamai.net/7/9/8082/v001/www.democrats.org/pdfs/ohvrireport/section03.pdf, accessed 15 March 2013.
  12. Fessler P (2012). Fixing long election lines may be easier said than done. http://www.npr.org/blogs/itsallpolitics/2012/11/08/164651410/fixing-long-election-lines-may-be-easier-said-than-done, accessed 15 March 2013.
  13. Geoffrion AM (1968). Proper efficiency and the theory of vector maximization. Journal of Mathematical Analysis and Application 22 (3): 618–630.CrossRefGoogle Scholar
  14. Grant FH (1980). Reducing voter waiting time. Interfaces 10 (2): 19–25.CrossRefGoogle Scholar
  15. Keeney RL (1980a). Equity and public risk. Operations Research 28 (3): 527–534.CrossRefGoogle Scholar
  16. Keeney RL (1980b). Utility functions for equity and public risk. Management Science 26 (4): 345–353.CrossRefGoogle Scholar
  17. Laumanns M, Thiele L and Zitzler E (2006). An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method. European Journal of Operational Research 169 (3): 932–942.CrossRefGoogle Scholar
  18. Levine A (2008). Excitement, frustration as early voters brave long lines. http://www.cnn.com/2008/POLITICS/11/02/early.voting/index.html, accessed 15 March 2013.
  19. Lindner-Dutton L, Batta R and Karwan MH (1991). Equitable sequencing of a given set of hazardous materials shipments. Transportation Science 25 (2): 124–137.CrossRefGoogle Scholar
  20. Mavrotas G (2009). Effective implementation of the ɛ-constraint method in multi-objective mathematical programming problems. Applied Mathematics and Computation 213 (2): 455–465.CrossRefGoogle Scholar
  21. McDonald M (2012). The United States Elections Project, http://elections.gmu.edu/voter_turnout.html, accessed 31 October 2013.Google Scholar
  22. Mebane WR (2006). Voting machine allocation in Franklin County, Ohio, 2004: Response to U.S. Department of Justice Letter of June 29, 2005. http://www-personal.umich.edu/~wmebane/franklin2.pdf, accessed 6 November 2013.
  23. Miettinen KM (1999). Nonlinear Multiobjective Optimization. Kluwer Academic: Norwell, MA.Google Scholar
  24. Mulligan GF (1991). Equality measures and facility location. Regional Science 7 (4): 548–561.Google Scholar
  25. Ogryczak W (2009). Inequality measures and equitable locations. Annals of Operations Research 167 (1): 61–86.CrossRefGoogle Scholar
  26. Ripley A (2008). Secrets of What Makes Your Polling Place Work—Or Not. Time, 3 November, http://content.time.com/time/politics/article/0,8599,1855861,00.html, accessed 30 October 2013.
  27. Smith M (2012). Long lines but few snags in U.S. election. CNN, 6 November, http://www.cnn.com/2012/11/06/politics/election-voting/index.html, accessed 15 March 2013.
  28. Steuer RE (1986). Multiple Criteria Optimization, Theory, Computation and Application. Wiley: New York.Google Scholar
  29. Tricoire F, Graf A and Gutjahr WJ (2011). The bi-objective stochastic covering tour problem. Computers & Operations Research 39 (7): 1582–1592.CrossRefGoogle Scholar
  30. Ulungu L and Teghem J (1995). The two phase method: An efficient procedure to solve bi-objective combinatorial optimization problems. Foundations of Computing and Decision Sciences 20 (2): 149–165.Google Scholar
  31. Yang M, Allen TT, Fry MJ and Kelton WD (2013). The call for equity: Simulation optimization models to minimize the range of waiting times. IIE Transactions 45 (7): 781–795.CrossRefGoogle Scholar
  32. Yang M, Fry MJ, Kelton WD and Allen TT (2014). Improving voting systems through service-operations management. Production and Operations Management 23 (7): 1083–1097.CrossRefGoogle Scholar

Copyright information

© Operational Research Society Ltd. 2014

Authors and Affiliations

  • Xinfang (Jocelyn) Wang
    • 1
  • Muer Yang
    • 2
  • Michael J Fry
    • 3
  1. 1.Georgia Southern UniversityStatesboroUSA
  2. 2.University of St. ThomasSaint PaulUSA
  3. 3.University of CincinnatiCincinnatiUSA

Personalised recommendations