  For decades, voting-by-mail and the use of ballot drop boxes has
substantially grown, and in response, many election officials have added new
drop boxes to their voting infrastructure. However, existing guidance for
locating drop boxes is limited. In this paper, we introduce an integer
programming model, the drop box location problem (DBLP), to locate drop boxes.
The DBLP considers criteria of cost, voter access, and risk. The cost of the
drop box system is determined by the fixed cost of adding drop boxes and the
operational cost of a collection tour by a bipartisan team who regularly
collects ballots from selected locations. The DBLP utilizes covering sets to
ensure each voter is in close proximity to a drop box and incorporates a novel
measure of access to measure the ability to use multiple voting pathways to
vote. The DBLP is shown to be NP-Hard, and we introduce a heuristic to generate
a large number of feasible solutions for policy makers to select from a
posteriori. Using a real-world case study of Milwaukee, WI, we study the
benefit of the DBLP. The results demonstrate that the proposed optimization
model identifies drop box locations that perform well across multiple criteria.
The results also demonstrate that the trade-off between cost, access, and risk
is non-trivial, which supports the use of the proposed optimization-based
approach to select drop box locations.
