A Framework for Shelter Location Decisions by Ant Colony Optimization
Hossein Baharmand
Tina Comes
Centre for Integrated Emergency Management (CIEM)
University of Agder
25.05.2015
SHELTER LOCATION DECISIONS
Introduction of
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Shelter location decisions and sudden on-set earthquakes
• The trend in numbers of earthquakes
Recent experiences like Nepal
• Shelter location and
Crisis Management:
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• 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
Shelter location problem and Ant Colony Optimization
• Steps through shelter location:
• Previous research gaps:
• Integrated problem,
• Unknown number of shelters,
• Optimal routes.
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Selecting shelter locations Optimal paths to shelters
Allocation of affected people to shelters
Ant Colony Optimization
• A swarm intelligence and meta-heuristic approach
• Making use of pheromones
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SHELTER LOCATION PROBLEM
An Integrated framework based on ACO toward
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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
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• Looking for best places for shelters where:
have limited capacity,
their numbers are unknown .
• Estimating demand by residential locations.
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Framework Structure
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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
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:
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Shortest path
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.)
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Shelter location for the city of Kerman
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Establishing shelter locations
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Sh
orta
ge
Co
st
Mean
Sit
e
Su
itab
ilit
y
Iteration Iteration
Iteration
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
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Future work
[email protected] [email protected]
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