A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment

Bian Liang, Dapeng Yang, Xinghong Qin and Teresa Tinta
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Bian Liang: School of Economics & Management, Tongji University, Shanghai 200092, China
Dapeng Yang: School of Economics & Management, Tongji University, Shanghai 200092, China
Xinghong Qin: School of Business Planning, Chongqing Technology and Business University, Chongqing 400067, China
Teresa Tinta: Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

IJERPH, 2019, vol. 16, issue 20, 1-28

Abstract: Disasters such as hurricanes, earthquakes and floods continue to have devastating socioeconomic impacts and endanger millions of lives. Shelters are safe zones that protect victims from possible damage, and evacuation routes are the paths from disaster zones toward shelter areas. To enable the timely evacuation of disaster zones, decisions regarding shelter location and routing assignment (i.e., traffic assignment) should be considered simultaneously. In this work, we propose a risk-averse stochastic programming model with a chance constraint that takes into account the uncertainty in the demand of disaster sites while minimizing the total evacuation time. The total evacuation time reflects the efficacy of emergency management from a system optimal (SO) perspective. A conditional value-at-risk (CVaR) is incorporated into the objective function to account for risk measures in the presence of uncertain post-disaster demand. We resolve the non-linear travel time function of traffic flow by employing a second-order cone programming (SOCP) approach and linearizing the non-linear chance constraints into a new mixed-integer linear programming (MILP) reformulation so that the problem can be directly solved by state-of-the-art optimization solvers. We illustrate the application of our model using two case studies. The first case study is used to demonstrate the difference between a risk-neutral model and our proposed model. An extensive computational study provides practical insight into the proposed modeling approach using another case study concerning the Black Saturday bushfire in Australia.

Keywords: shelter location; traffic assignment; disaster management; stochastic programming; risk aversion; uncertainty (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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