  One of the lessons from the COVID-19 pandemic is the importance of social
distancing, even in challenging circumstances such as pre-hurricane evacuation.
To explore the implications of integrating social distancing with evacuation
operations, we describe this evacuation process as a Capacitated Vehicle
Routing Problem (CVRP) and solve it using a DNN (Deep Neural Network)-based
solution (Deep Reinforcement Learning) and a non-DNN solution (Sweep
Algorithm). A central question is whether Deep Reinforcement Learning provides
sufficient extra routing efficiency to accommodate increased social distancing
in a time-constrained evacuation operation. We found that, in comparison to the
Sweep Algorithm, Deep Reinforcement Learning can provide decision-makers with
more efficient routing. However, the evacuation time saved by Deep
Reinforcement Learning does not come close to compensating for the extra time
required for social distancing, and its advantage disappears as the emergency
vehicle capacity approaches the number of people per household.
