Matthew J. Saltzman

Political redistricting is a complex, multi‐objective problem that has been extensively studied. One approach to solve this geographical partitioning problem is to use multiobjective genetic algorithms applied to the adjacency graph of precincts. Following the work of Vanneschi et al., we develop an algorithm using a Non‐Dominated Sorting Genetic Algorithm (NSGA‐II) to build electoral plans in the state of South Carolina. Our work (which we name Cluster‐NSGA‐II) utilizes a novel clustering approach to breed children rather than the standard crossover step. We find that this approach is competitive with other similar algorithms in terms of both solution diversity and Pareto optimality.

The COVID-19 pandemic forced universities to upend their class scheduling. At Clemson University, the administration implemented hybrid schedules for fall 2020, in which students attend classes partly online and partly in person. To limit exposures of COVID-19 in the classroom, we propose two rotational attendance models (the three-cohort model and the once-a-week model) that aim to allow in-person classroom time and minimize exposure between students. In a baseline strategy, students would interact with an average of 84 students per week and attend class in person 2.6 days a week. By contrast, the three-cohort model and once-a-week model achieve about 57 and 83 student interactions per week and 1.6 and 1.9 in-person student attendance days a week, respectively. Although these figures of merit may imply that the three-cohort model is preferable, it achieves its results by forcing about 1,600 of the 21,000 students who want to attend courses in person to participate online instead and forcing courses that meet twice a week to be attended twice in a three-week rotation. Considering the tradeoffs between the figures of merit related to student interaction and anticipated implementation challenges, Clemson University implemented the once-a-week model for fall 2020 and spring 2021. History: This paper was refereed. This article has been selected for inclusion in the Special Issue on Analytics Remedies to COVID-19.