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.
Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to ABSTRACT (maximum 200 words)As Humanitarian Assistance and Disaster Relief (HADR) operations gain importance, a number of problems become evident. The time-sensitive problem of evacuating non-ambulatory people from a disaster area proves to be a challenging combinatorial optimization problem. The scope of the problem is defined by drawing analogies to similar vehicle routing problems that have been previously addressed. Based on the basic Max-Min Ant System (MMAS) algorithm modeled after the behavior of ants seeking food, potential solution approaches to this problem are enhanced to improve quality and efficiency by hybridizing features such as a best solution list, elite ants, ranked contribution system, and heuristic procedures during route construction. Using a Nearly-Orthogonal Latin Hypercubes (NOLH) experimental design, the algorithm parameters are tuned for best empirical performance for a range of test scenarios. SUBJECT TERMS HEURISTICS FOR SOLVING PROBLEM OF EVACUATING NON-AMBULATORY PEOPLE IN A SHORT-NOTICE DISASTER
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.