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Location planning for transit-based evacuation under the risk of service disruptions
Affiliation:1. School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14850, USA;2. Department of Civil and Environmental Engineering, University of Delaware, Newark, DE 19716, USA;3. Renaissance Computing Institute, University of North Carolina at Chapel Hill, 100 Europa Drive, Suite 540, Chapel Hill, NC 27517, USA;4. School of Marine and Atmospheric Sciences, Stony Brook University, New York, NY, USA
Abstract:The effectiveness of transit-based emergency evacuation highly depends on the location of pick-up facilities, resource allocation, and management. These facilities themselves are often subject to service disruptions during or after the emergency. This paper proposes a reliable emergency facility location model that determines both pre-emergency facility location planning and the evacuation operations afterwards, while facilities are subject to the risk of disruptions. We analyze how evacuation resource availability leverages individual evacuees’ response to service disruptions, and show how equilibrium of the evacuee arrival process could be reached at a functioning pick-up facility. Based on this equilibrium, an optimal resource allocation strategy is found to balance the tradeoff between the evacuees’ risks and the evacuation agency’s operation costs. This leads to the development of a compact polynomial-size linear integer programming formulation that minimizes the total expected system cost from both pre-emergency planning (e.g., facility set-up) and the evacuation operations (e.g., fleet management, transportation, and exposure to hazardous surroundings) across an exponential number of possible disruption scenarios. We also show how the model can be flexibly used to plan not only pre-disaster evacuation but also post-disaster rescue actions. Numerical experiments and an empirical case study for three coastal cities in the State of Mississippi (Biloxi, Gulfport, and D’lberville) are conducted to study the performance of the proposed models and to draw managerial insights.
Keywords:Evacuation  Transit-based  Location  Reliability  Service disruption
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