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Optimization Approaches for Improving Mitigation and Response Operations in Disaster Management

Esposito Amideo, Annunziata (2018) Optimization Approaches for Improving Mitigation and Response Operations in Disaster Management. Doctor of Philosophy (PhD) thesis, University of Kent,. (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:72944)

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Language: English

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Abstract

Disasters are calamitous events that severely affect the life conditions of an entire community, being the disasters either nature-based (e.g., earthquake) or man-made (e.g., terroristic attack). Disaster-related issues are usually dealt with according to the Disaster Operations Management (DOM) framework, which is composed of four phases: mitigation and preparedness, which address pre-disaster issues, and response and recovery, which tackle problems arising after the occurrence of a disaster. The ultimate scope of this dissertation is to present novel optimization models and algorithms aimed at improving operations belonging to the mitigation and response phases of the DOM.

On the response side, this thesis identifies the current challenges in devising realistic and applicable optimization models in the shelter location and evacuation routing context and outlines a roadmap for future research in this topical area. A shelter is a facility where people belonging to a community hit by a disaster are provided with different kinds of services (e.g., medical assistance, food). The role of a shelter is fundamental for two categories of people: those who are unable to make arrangements to other safe places (e.g., family or friends are too far), and those who belong to special-needs populations (e.g., disabled, elderly). People move towards shelter sites, or alternative safe destinations, when they either face or are going to face perilous circumstances. The process of leaving their own houses to seek refuge in safe zones goes under the name of evacuation. Two main types of evacuation can be identified: self-evacuation (or car-based evacuation) where individuals move towards safe sites autonomously, without receiving any kind of assistance from the responder community, and supported evacuation where special-needs populations (e.g., disabled, elderly) require support from emergency services and public authorities to reach some shelter facilities. This dissertation aims at identifying the central issues that should be addressed in a comprehensive shelter location/evacuation routing model. This is achieved by a novel meta-analysis that entail: (1) analysing existing disaster management surveys, (2) reviewing optimization models tackling shelter location and evacuation routing operations, either separately or in an integrated manner, (3) performing a critical analysis of existing papers combining shelter location and evacuation routing, concurrently with the responses of their authors, and (4) comparing the findings of the analysis of the papers with the findings of the existing disaster management surveys. The thesis also provides a discussion on the emergent challenges of shelter location and evacuation routing in optimization such as the need for future optimization models to involve stakeholders, include evacuee as well as system behaviour, be application-oriented rather than theoretical or model-driven, and interdisciplinary and, eventually, outlines a roadmap for future research. Based on the identified challenges, this thesis presents a novel scenario-based mixed-integer program which integrates shelter location, self-evacuation and supported-evacuation decisions, namely the Scenario-Indexed Shelter Location and Evacuation Routing (SISLER) problem. To the best of my knowledges, this is the second model including shelter location, self-evacuation and supported-evacuation however, SISLER deals with them based on the provided meta-analysis. The model is solved through a Branch-and-Cut algorithm of an off-the-shelf software, enriched with valid inequalities adapted from the literature. Computational results are reported for both testbed instances and a realistic case study.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Scaparra, Maria Paola
Thesis advisor: O'Hanley, Jesse
Uncontrolled keywords: Disaster Management; Critical Infrastructure Protection; Evacuation Planning; Bi-level Programming; Scenario-based Programming; Decomposition; Local-Search-based Heuristic
Subjects: H Social Sciences > HF Commerce > HF5351 Business
Divisions: Faculties > Social Sciences > Kent Business School
SWORD Depositor: System Moodle
Depositing User: System Moodle
Date Deposited: 12 Mar 2019 15:10 UTC
Last Modified: 17 Aug 2020 13:30 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/72944 (The current URI for this page, for reference purposes)
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