Short paper presented at ISCRAM 2015, Kristiansand, Norway. Abstract Earthquakes frequently destroy the homes and livelihoods of thousands. One of the most important concerns after an earthquake is to find a safe shelter for the affected people. Because of large numbers of potential locations, the multitude of constraints (e.g. access to infrastructures; security); and the uncertainty prevailing (e.g., the number of places required) the identification of optimal shelter locations is a complex problem. Nevertheless, rapidly locating shelters and transferring the affected people to the nearest shelters are the high priority in crisis situations. In this paper, we develop a framework based on Ant Colony Optimization (ACO) to support decisions-makers in the response phase. Using the same framework, we also derive recommendations for urban planning in the preparedness phase. We demonstrate our method with a case focusing on the city of Kerman, in Iran.

1. A Framework for
Shelter Location Decisions
by Ant Colony Optimization
Hossein Baharmand
Tina Comes
Centre for Integrated Emergency Management (CIEM)
University of Agder
hossein.baharmand@uia.no
tina.comes@uia.no
25.05.2015

2. SHELTER LOCATION
DECISIONS
Introduction of
25.05.2015Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 2

3. Shelter location decisions and sudden on-set earthquakes
• The trend in numbers of earthquakes
 Recent experiences like Nepal
• Shelter location and
Crisis Management:
25.05.2015Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 3
• Chaotic space
• Time pressure
• Limited capacity
• Lack of data
• Limited access to resources
Earthquake
• Uncertainty in predicting
earthquakes
• Hardly predictable population
behavior
• Large number of potential
locations
• The multitude of constraints

4. Shelter location problem and Ant Colony Optimization
• Steps through shelter location:
• Previous research gaps:
• Integrated problem,
• Unknown number of shelters,
• Optimal routes.
25.05.2015Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 4
Selecting shelter locations Optimal paths to shelters
Allocation of affected
people to shelters

5. Ant Colony Optimization
• A swarm intelligence and meta-heuristic approach
• Making use of pheromones
25.05.2015Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 5

6. SHELTER LOCATION
PROBLEM
An Integrated framework based on ACO toward
25.05.2015Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 6

7. Capacitated
Facility Location
Problem
Geographical
Information
Systems
Ant Colony
Optimization
Multi Criteria
Analysis
Compatible
criterion
Hospitals
Highways
Police stations
Fire stations
Place capacity
Incompatible
criterion
Gas stations
Gas pipelines
Problem Characteristics
7Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 25.05.2015
• Looking for best places for shelters where:
 have limited capacity,
 their numbers are unknown .
• Estimating demand by residential locations.

8. 




k
iNl
ilil
ijijk
ijp 



Framework Structure
25.05.2015Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 8
1. Selecting Shelter Locations by combining Weighted Lighted Combination
(WLC) and ACO:
Determining
the distances
for each
criterion and
each location
Normalize
the distances
per criterion
Eliciting the
weight of
each
criterion by
AHP
Calculating
Site
Suitability
(SS) for each
location
Analytic Hierarchy Process
Iranian Crisis Management Organization

9. Framework Structure (cont.)
2. Routing paths to shelters by using ArcGIS (Network Analyst ext.)
• Building a network dataset of the city (population data, infrastructures,..)
• Running the network analysis:
25.05.2015Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 9
Shortest path

10. 3. Allocation population to shelters by minimizing the cost of transportation
• Assumption:
 People living in one area will be routed to the same shelter,
 Equal initial value of pheromones in all routes.
• Greedy approach to allocating larger residential areas first!
• Constraints:
 The average surplus/shortage per location;
 The maximum numbers of location selections by an agent;
 The minimum average of SS;
Framework Structure (cont.)
25.05.2015Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 10

11. Shelter location for the city of Kerman
25.05.2015Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 11

12. Establishing shelter locations
25.05.2015Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 12

13. 13Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 25.05.2015
Shortage
Cost
MeanSite
Suitability
Iteration Iteration
Iteration

14. Final remarks
• Combination of MCA, GIS, and ACO
• Transportation cost is minimized while considering three constraints: the
surplus/shortage mean, maximum numbers of safe places, the
minimum mean of SS.
• The results of allocating population undeniably rely on the distribution of
safe places, their capacities and also the distribution of population blocks.
• Distribution of safe places needs to be revised;
• Identification and establishment of new safe places!
• Dynamic simulation of changing safe places and capacities
• Consideration of infrastructure failure after earthquakes
14Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 25.05.2015
Future work

15. hossein.baharmand@uia.no
tina.comes@uia.no
15Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization 25.05.2015

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