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

Routing algorithms as tools for integrating social distancing with emergency evacuation