Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/30052
Title: | Many-Objective Simulation Optimization for Camp Location Problems in Humanitarian Logistics |
Authors: | Xue, Y Li, M Arabnejad, H Suleimenova, D Jahani, A C. Geiger, B Boesjes, F Anagnostou, A J.E. Taylor, S Liu, X Groen, D |
Keywords: | facility location problem;many-objective optimization;simulation;evolutionary algorithms |
Issue Date: | 26-Sep-2024 |
Publisher: | Australia Academic Press |
Citation: | Xue, Y. et al. (2024) 'Many-Objective Simulation Optimization for Camp Location Problems in Humanitarian Logistics', International Journal of Network Dynamics and Intelligence, 13 (3), 100017, pp. 1 - 14. doi: 10.53941/ijndi.2024.100017. |
Abstract: | Humanitarian organizations face a rising number of people fleeing violence or persecution, people who need their protection and support. When this support is given in the right locations, it can be timely, effective and cost-efficient. Successful refugee settlement planning not only considers the support needs of displaced people, but also local environmental conditions and available resources for ensuring survival and health. It is indeed very challenging to find optimal locations for establishing a new refugee camp that satisfy all these objectives. In this paper, we present a novel formulation of the facility location problem with a simulation-based evolutionary many-objective optimization approach to address this problem. We show how this approach, applied to migration simulations, can inform camp selection decisions by demonstrating it for a recent conflict in South Sudan. Our approach may be applicable to diverse humanitarian contexts, and the experimental results have shown it is capable of providing a set of solutions that effectively balance up to five objectives. |
Description: | Data Availability Statement: The Integrated Food Security Phase Classification (IPC) datasets can be downloaded from: https://www.ipcinfo.org/ipc-country-analysis/, the JAXA ALOS Global 30m DSM 2021 dataset can be downloaded from https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AW3D30_V3_2, the Esri Land Cover 2020 source can be downloaded from https://www.arcgis.com/apps/instant/media/index.html?appid=fc92d38533d440078f17678ebc20e8e2. |
URI: | https://bura.brunel.ac.uk/handle/2438/30052 |
DOI: | https://doi.org/10.53941/ijndi.2024.100017 |
Other Identifiers: | ORCiD: Yani Xue https://orcid.org/0000-0002-7526-9085 ORCiD: Hamid Arabnejad https://orcid.org/0000-0002-0789-1825 ORCiD: Diana Suleimenova https://orcid.org/0000-0003-4474-0943 ORCiD: Alireza Jahani https://orcid.org/0000-0001-9813-352X ORCiD: Anastasia Anagnostou https://orcid.org/0000-0003-3397-8307 ORCiD: Simon J.E. Taylor https://orcid.org/0000-0001-8252-0189 ORCiD: Derek Groen https://orcid.org/0000-0001-7463-3765 100017 |
Appears in Collections: | Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © 2024 by the authors. Creative Commons License/. This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). | 2.38 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License