A framework for shelter location decisions by Ant Colony Optimization

A Framework for Shelter Location Decisions by Ant Colony Optimization

Hossein Baharmand

Tina Comes

Centre for Integrated Emergency Management (CIEM)

University of Agder

[email protected]

[email protected]

25.05.2015

SHELTER LOCATION DECISIONS

Introduction of

25.05.2015 Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization

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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:

25.05.2015 Baharmand & 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

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

7 Baharmand & Comes: A Framework for Shelter Location Decisions by Ant Colony Optimization

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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

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Co

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Mean

Sit

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Su

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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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Technology

A framework for shelter location decisions by Ant Colony Optimization