Locating ballot drop boxes

TL;DR

This paper introduces an integer programming model for optimally locating ballot drop boxes considering cost, voter access, and risk, demonstrated through a Milwaukee case study, highlighting complex trade-offs.

Contribution

The paper develops a novel optimization model (DBLP) for drop box placement that balances multiple criteria and provides a heuristic solution approach.

Findings

The model effectively balances cost, access, and risk in drop box placement.

The heuristic generates many feasible solutions for policy makers.

Case study shows the model's practical benefits in real-world settings.

Abstract

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.