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Evacuating People with Mobility‐Challenges in a Short‐Notice Disaster
Author(s) -
Apte Aruna,
Heath Susan K.,
Pico Andres,
Ronny Tan Yong Hui
Publication year - 2015
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/deci.12153
Subject(s) - notice , stylized fact , computer science , plan (archaeology) , operations research , order (exchange) , state (computer science) , computer security , ant colony optimization algorithms , emergency management , business , transport engineering , artificial intelligence , political science , economics , engineering , finance , geography , economic growth , law , algorithm , archaeology , macroeconomics
In past disasters, arrangements have been made to evacuate people without their own transportation, requiring them to gather at select locations to be evacuated. Unfortunately, this type of plan does not help those people who are unable to move themselves to the designated meeting locations. In the United States, according to the Post‐Katrina Emergency Management Reform Act of 2006, state or local governments have the responsibility to coordinate evacuation plans for all populations. These include those with disabilities. However, few, if any, have plans in place for those who are mobility‐challenged. The problem of evacuating mobility‐challenged people from their individual locations in a short‐notice disaster is a challenging combinatorial optimization problem. In order to develop the model and select a solution approach, we surveyed related literature. Based on our review, we formulate the problem and develop an Ant Colony Optimization (ACO) algorithm to solve it. We then test two different versions of the ACO algorithm on five stylized datasets with several different parameter settings.