Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 211–217, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-211-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 211–217, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-211-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

INVESTIGATING THE EFFECT OF CAPACITY CRITERION ON THE OPTIMAL ALLOCATION OF EMERGENCY FACILITIES IN GIS ENVIRONMENT

S. Bolouri1, A. Vafaeinejad2, A. Alesheikh3, and H. Aghamohammadi1 S. Bolouri et al.
  • 1Department of GIS/RS, Science and Research Branch, Islamic Azad University, Tehran, Iran
  • 2Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
  • 3Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engi neering, K. N. Toosi University of Technology, Tehran, Iran

Keywords: Capacity Criterion, Allocation, Tabu algorithm, Genetic algorithm, VAOMP, GIS

Abstract. Location-allocation analysis is one of the most GIS useful analysis, especially in allocating demands to facilities. One of these facilities is the fire stations, which the correct locations and optimal demand allocations to those have most importance. Each facility has a specific capacity that should be considered in locating the facilities and allocating the demand to those. In recent years, the use of unified models in solving allocation problems is too common because these models can solve a variety of problems, but in most of these models, the capacity criterion for facilities has been ignored. The present study tries to investigate the location-allocation problem of the fire stations with the aid of two Tabu and Genetic algorithms with the goal of maximizing the coverage using the (Vector Assignment Ordered Median Problem) VAOMP model, taking into account the capacity criterion and regardless of it. The results of using two algorithms in problem-solving show that the Genetic algorithm produces better quality solutions over a shorter time. Also, considering the capacity criterion that brings the problem closer to real-world space, in the study area, 59,640 demands will not be covered by any station within a 5-minute radius and will be highly vulnerable to potential hazards. Also, by adding 3 stations to the existing stations and re-allocating, the average of allocated demands with the help of Genetic was 93.39% and 92.74% for the Tabu algorithm. So it is necessary to consider the capacity of the facilities for optimal services.