Optimal Consolidation of Polling Locations

Problem definition: Many logistical and financial challenges of facilitating an election lead election officials to consolidate polling locations. However, determining when it is appropriate to consolidate polling locations and how to consolidate polling locations, if necessary, is a difficult and high-stakes decision that influences voter participation. Methodology/results: We formalize the set of constraints and criteria that election officials should follow as the polling location consolidation problem (PLCP), which is formulated as an integer programming model. The PLCP simultaneously selects which polling locations will be used in the upcoming election, reassigns voter precincts to polling locations, and allocates critical resources to the selected polling locations. The PLCP minimizes the increased distance that voters must travel to their updated polling location. Because empirical research also demonstrates the importance of reducing the voters’ wait times, we require that most voters do not wait longer than a prespecified limit, such as 30 minutes, using a chance constraint. We prove that identifying a feasible solution to PLCP is NP-complete, which demonstrates the difficulty in making consolidation decisions, as well as the importance of optimization for this problem. Managerial implications: This paper introduces a structured and transparent approach to support election officials in making informed, data-driven decisions regarding how and when to consolidate polling locations that minimally impact voters and encourage voter participation. The approach could be used to develop preapproved contingency plans that could be employed if there are major disruptions to an election. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.0497 .

• 저널Manufacturing & Service Operations Management · 26(3) · 1028–1042
• 토픽Facility Location and Emergency Management · Organizational Behavior and Human Resource Management
• DOI10.1287/msom.2022.0497
• 저자Adam Schmidt, Duncan A. Buell, Laura A. Albert