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ISPRS Int. J. Geo-Inf. 2018, 7(8), 292; https://doi.org/10.3390/ijgi7080292

Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS

1
GIS Division, Faculty of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
2
GIS Division, Faculty of Geodesy and Geomatics, and Geoinformation Technology Center of Excellence, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
3
Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney NSW 2007, Australia
*
Author to whom correspondence should be addressed.
Received: 12 June 2018 / Revised: 9 July 2018 / Accepted: 20 July 2018 / Published: 24 July 2018
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Abstract

After an earthquake, it is required to establish temporary relief centers in order to help the victims. Selection of proper sites for these centers has a significant effect on the processes of urban disaster management. In this paper, the location and allocation of relief centers in district 1 of Tehran are carried out using Geospatial Information System (GIS), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model, a simple clustering method and the two meta-heuristic algorithms of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). First, using TOPSIS, the proposed clustering method and GIS analysis tools, sites satisfying initial conditions with adequate distribution in the area are chosen. Then, the selection of proper centers and the allocation of parcels to them are modelled as a location/allocation problem, which is solved using the meta-heuristic optimization algorithms. Also, in this research, PSO and ACO are compared using different criteria. The implementation results show the general adequacy of TOPSIS, the clustering method, and the optimization algorithms. This is an appropriate approach to solve such complex site selection and allocation problems. In view of the assessment results, the PSO finds better answers, converges faster, and shows higher consistency than the ACO. View Full-Text
Keywords: disaster management; location-allocation; PSO; ACO; clustering; GIS; TOPSIS disaster management; location-allocation; PSO; ACO; clustering; GIS; TOPSIS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Saeidian, B.; Mesgari, M.S.; Pradhan, B.; Ghodousi, M. Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS. ISPRS Int. J. Geo-Inf. 2018, 7, 292.

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