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Water distribution in disaster relief

Water distribution in disaster relief Purpose – The purpose of this paper is to present an operations research (OR) model for planning water distribution tours in disaster relief. Especially in situations after a disaster occurrence, characterized by instability and the immediate need of help, high‐quality decisions have to be made fast. For this reason, it is very useful if planning decisions can be alleviated by a decision support system (DSS) using an efficient multi‐objective metaheuristic as its algorithmic core. Design/methodology/approach – The paper develops a metaheuristic search technique based on evolutionary concepts for a real‐world extension of a multi‐objective covering tour problem. Findings – The proposed method supports decision makers in finding appropriate compromise solutions with respect to conflicting objectives (e.g. coverage and travel time). With this work, the authors want to reduce the gap between theory and practical applications. They apply OR methods to a real‐world application in the field of disaster relief operations planning. Research limitations/implications – The success of the proposed approach depends on the availability of reasonable and useful data. However, data generation in this context represents an upcoming discipline, especially under the circumstances of increasing threat by natural hazards. Practical implications – When the approach is integrated in a DSS, different scenarios can be investigated immediately and presented with a geographic information tool. The most appropriate solution for the decision makers can be realized. Originality/value – Heterogeneous transport modes and different road types were not considered so far in these types of problems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Physical Distribution & Logistics Management Emerald Publishing

Water distribution in disaster relief

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References (31)

Publisher
Emerald Publishing
Copyright
Copyright © 2010 Emerald Group Publishing Limited. All rights reserved.
ISSN
0960-0035
DOI
10.1108/09600031011079337
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to present an operations research (OR) model for planning water distribution tours in disaster relief. Especially in situations after a disaster occurrence, characterized by instability and the immediate need of help, high‐quality decisions have to be made fast. For this reason, it is very useful if planning decisions can be alleviated by a decision support system (DSS) using an efficient multi‐objective metaheuristic as its algorithmic core. Design/methodology/approach – The paper develops a metaheuristic search technique based on evolutionary concepts for a real‐world extension of a multi‐objective covering tour problem. Findings – The proposed method supports decision makers in finding appropriate compromise solutions with respect to conflicting objectives (e.g. coverage and travel time). With this work, the authors want to reduce the gap between theory and practical applications. They apply OR methods to a real‐world application in the field of disaster relief operations planning. Research limitations/implications – The success of the proposed approach depends on the availability of reasonable and useful data. However, data generation in this context represents an upcoming discipline, especially under the circumstances of increasing threat by natural hazards. Practical implications – When the approach is integrated in a DSS, different scenarios can be investigated immediately and presented with a geographic information tool. The most appropriate solution for the decision makers can be realized. Originality/value – Heterogeneous transport modes and different road types were not considered so far in these types of problems.

Journal

International Journal of Physical Distribution & Logistics Management – Emerald Publishing

Published: Sep 7, 2010

Keywords: Decision support systems; Aid agencies; Disasters; Operations management; Water supply

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