Integration Of Travel Time Zone For Optimal Siting Of Emergency Facilities
Conventional facility location models only define a facility’s service area simply as a circular coverage. Such definition is not appropriate for emergency facilities like fire stations and ambulances, as the services are influenced by road accessibility. To improve service area definition in conven...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English |
| Published: |
2008
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| Online Access: | http://psasir.upm.edu.my/id/eprint/5443/1/FK_2008_60a.pdf http://psasir.upm.edu.my/id/eprint/5443/ |
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| Summary: | Conventional facility location models only define a facility’s service area simply as a circular coverage. Such definition is not appropriate for emergency facilities like fire stations and ambulances, as the services are influenced by road accessibility. To improve service area definition in conventional models, this study developed the model that utilizes the capability of GIS to define service areas as travel time zones generated through road network analysis. The objective of the model is to maximize total service area of a fixed number of facilities. Hence it is called the Maximal Service Area Problem (MSAP). The MSAP is a discrete model where a specified number of facilities that achieve the best objective function value of the model are selected out of a finite set of candidate sites. A method involving multi criteria analysis was introduced to determine candidate sites in a per zone basis. Particular geometric figures commonly used for tessellations, like hexagon and square, were utilized to divide the study area into zones of equal size. The candidate sites were then chosen from the sites that have the highest value of the site suitability index within each zone, combined with the sites of existing facilities. Fire stations in Jakarta Selatan were chosen for simulation. Two algorithms, Greedy Adding (Add) and Greedy Adding with Travel Time Evaluation (GAT), were applied to solve the optimization problem of the MSAP. The planar space of demand region was divided into regular points to simplify calculation of area of coverage. The number of points intersecting with the set of service area polygons (z) was used as the surrogate information to measure the actual area of coverage (A). This way has made the optimization process faster. In a fine resolution of demand points, percentages of coverage based on z and A values were not much different. Hence, the z values were sufficient to measure solution qualities yielded by the algorithms. Integration of the site suitability evaluation and tessellations has been proved workable to obtain scattered candidate sites that allow good solutions to be achieved in the optimization process. Of four simulations conducted, both Add and GAT yielded better coverage than the existing coverage with the same number of fire stations within the same travel time. Add managed to reach the best 82.81% coverage and GAT did 81.68%, whereas the existing only reaches 73.69%. |
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