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Optimization models for large scale network evacuation planning and management: A literature review

Bayram, Vedat (2016) Optimization models for large scale network evacuation planning and management: A literature review. Surveys in Operations Research and Management Science, 21 (2). pp. 63-84. ISSN 1876-7354. E-ISSN 1876-7362. (doi:10.1016/j.sorms.2016.11.001) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:99646)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL:
https://dx.doi.org/10.1016/j.sorms.2016.11.001

Abstract

This study presents a comprehensive review of network-based large scale emergency evacuation planning and management literature. Evacuation planning and management approaches are mostly based on traffic assignment approaches. For that reason, for a complete grasp of the ideas in evacuation planning and management, the relevant literature in urban transportation is covered including traffic assignment approaches, travel time modeling to represent congestion and traffic flow propagation approaches. Correct estimation of evacuation response rates and demand distributions by human behavior studies covered in this review contribute to an efficient evacuation planning and management at a large extent. Since it is not cost effective to design the evacuation network from scratch for rare disasters, the existing road network must be efficiently used for avoiding congestion to enable the evacuation of the disaster area in a timely manner. We present studies that propose effective supply and demand management strategies that aim to achieve this. We focus on macroscopic approaches in static/dynamic, deterministic/stochastic/robust evacuation modeling that consider different evacuee behavior assumptions, traffic assignment methodologies and supply and demand management strategies. We review the optimization-based solution methodologies so as to identify research gaps and limitations and suggest future research directions.

Item Type: Article
DOI/Identification number: 10.1016/j.sorms.2016.11.001
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Vedat Bayram
Date Deposited: 23 Jan 2023 10:00 UTC
Last Modified: 27 Jan 2023 11:19 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/99646 (The current URI for this page, for reference purposes)
Bayram, Vedat: https://orcid.org/0000-0003-0695-2806
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