  In this study, we explore the implications of integrating social distancing
with emergency evacuation when a hurricane approaches a major city during the
COVID-19 pandemic. Specifically, we compare DNN (Deep Neural Network)-based and
non-DNN methods for generating evacuation strategies that minimize evacuation
time while allowing for social distancing in rescue vehicles. A central
question is whether a DNN-based method provides sufficient extra efficiency to
accommodate social distancing, in a time-constrained evacuation operation. We
describe the problem as a Capacitated Vehicle Routing Problem and solve it
using one non-DNN solution (Sweep Algorithm) and one DNN-based solution (Deep
Reinforcement Learning). DNN-based solution can provide decision-makers with
more efficient routing than non-DNN solution. Although DNN-based solution can
save considerable time in evacuation routing, it does not come close to
compensating for the extra time required for social distancing and its
advantage disappears as the vehicle capacity approaches the number of people
per household.
