Abstract
Apportionment in election systems refers to determination of the number of voting resources (poll books, poll workers, or voting machines) needed to ensure that all voters can expect to wait no longer than an appropriate amount, even the voter who waits the longest. Apportionment is a common problem for election officials and legislatures. A related problem is “allocation,” which relates to the deployment of an existing number of resources so that the longest expected wait is held to an appropritate amount. Provisioning and allocation are difficult because the numbers of expected voters, the ballot lengths, and the education levels of voters may all differ significantly from precinct-to-precinct in a county. Consider that predicting the waiting time of the voter who waits the longest generally requires discrete event simulation.•The methods here rigorously guarantee that all voters expect to wait a prescribed time with a bounded probability, e.g., everyone expects to wait less than thirty minutes with probability greater than 95%.•The methods here can handle both a single type of resource (e.g., voting machines or scan machines) and multiple resource types (e.g., voting machines and poll books).•The methods are provided in a freely available, easy-to-use Excel software program.
Highlights
In our linked publication [6], we prove that the Indifference-Zone Generalized Binary Search (IZGBS) method provides the needed solution quality guarantees and describe our free enabling software
The GBS can guarantee termination in a small number of steps with an optimal solution but not if the evaluations are only within an indifference-zone of being correct with known probability. This indifference-zone is associated with simulation or other empirical evaluations. This explains the need for the IZGBS
We find a total of 4865 machines are needed, compared to the 3952 machines derived by the IZGBS method applying a 30-minutes-or-less waiting time
Summary
Hernandez a a Integrated Systems Engineering, The Ohio State University, Columbus, OH 43210, United States b Opus College of Business, University of St. Thomas, Saint Paul, MN 55105, United States c Advanced Analytics, Nationwide E&S, Scottsdale, AZ 85258, United States abstract. Method name: Service Time Estimation and Indifference-Zone Generalized Binary Search Keywords: Simulation optimization, Multiple comparisons, Comparison with the best, Election systems, Voting machine, Allocation, Resource management, Consumer requirements management, Indifference-zone Article history: Received 31 December 2019; Accepted 11 March 2020; Available online 20 March 2020. Subject Area: More specific subject area: Method name: Name and reference of original method: Resource availability: Engineering Operations Research & Simulation Optimization Service Time Estimation and Indifference-Zone Generalized Binary Search Nowak, R. IEEE Transactions on Information Theory 57(12), 7893–7906. http://www.blying.com/sitebuildercontent/sitebuilderfiles/voteizgbs.alphav7ready.xlsm http://www.blying.com/sitebuildercontent/sitebuilderfiles/ iiepomsfranklincountryelection_analysis_datanomacro.xlsx
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
