Multiobjective Optimization Model for Emergency Evacuation Planning in Geographical Locations with Vulnerable Population Groups

AbstractA large-scale emergency evacuation due to an approaching natural disaster requires local and state administrations to make important decisions regarding evacuation routes, emergency shelters, and evacuation time periods, among other things. Considering a conflicting nature of certain emergen...

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Bibliographic Details
Published in:Journal of management in engineering 2020-03, Vol.36 (2)
Main Authors: Dulebenets, Maxim A, Pasha, Junayed, Kavoosi, Masoud, Abioye, Olumide F, Ozguven, Eren E, Moses, Ren, Boot, Walter R, Sando, Thobias
Format: Article
Language:English
Subjects:
Algorithms
Decisions
Demand
Disaster management
Driving conditions
Emergencies
Emergency preparedness
Evacuation routing
Evacuations & rescues
Geographical locations
Mathematical analysis
Mathematical models
Multiple objective analysis
Optimization models
Technical Papers
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Summary:AbstractA large-scale emergency evacuation due to an approaching natural disaster requires local and state administrations to make important decisions regarding evacuation routes, emergency shelters, and evacuation time periods, among other things. Considering a conflicting nature of certain emergency evacuation planning decisions, this study introduces a multiobjective optimization model for emergency evacuation planning that aims to minimize a set of critical performance indicators, including the total evacuation time, mental demand, physical demand, temporal demand, effort, and frustration endured by the individuals evacuating from a given metropolitan area anticipating a natural disaster. The major driver characteristics, evacuation route characteristics, driving conditions, and traffic characteristics that affect the driving performance of individuals, including vulnerable population groups, are incorporated in the proposed mathematical model. In order to solve the developed mathematical model and analyze the trade-offs among the conflicting objectives, this study presents four multiobjective heuristic algorithms. The computational experiments were conducted using real-world data and showcase the efficiency of the proposed methodology. The developed multiobjective methodology is expected to improve the safety of evacuees at the natural disaster preparedness stage and ensure timely evacuation from areas expecting significant natural disaster impacts.
ISSN:0742-597X
1943-5479
DOI:10.1061/(ASCE)ME.1943-5479.0000730