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Routing algorithms as tools for integrating social distancing with emergency evacuation

Scientific Reports, ISSN: 2045-2322, Vol: 11, Issue: 1, Page: 19623
2021
  • 11
    Citations
  • 0
    Usage
  • 51
    Captures
  • 0
    Mentions
  • 12
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    11
    • Citation Indexes
      11
  • Captures
    51
  • Social Media
    12
    • Shares, Likes & Comments
      12
      • Facebook
        12

Article Description

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.

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