Optimum location and amount of new Fire Stations based on Geographic Information System and Analytic Hierarchy Methods

In this study the Geographic Information System (GIS) method and the Analytical Hierarchical Process (AHP) are combined in order to present a methodology for locating fire stations in cities. Initially, we collect the complete technical information and thus generate a statistical data, from which we derive the attributes of Fire Stations. This methodology evaluates the efficiency of different proposals based on the response time restriction variable, in combination with human perception and the need and experience of firefighters, academics and vulnerable population. The main result is an operational tool that defines the locations and number of stations and the choice of the best alternative considering multiple criteria.

[1]  Mohammad Hossein Fazel Zarandi,et al.  Maximal covering location problem (MCLP) with fuzzy travel times , 2011, Expert Syst. Appl..

[2]  Amr K. Mortagy,et al.  A multi-objective model for locating fire stations , 1998, Eur. J. Oper. Res..

[3]  Jared L. Cohon,et al.  Some models for fire protection locational decisions , 1980 .

[4]  Remzi Karagüzel,et al.  Combining AHP with GIS for landfill site selection: a case study in the Lake Beyşehir catchment area (Konya, Turkey). , 2010, Waste management.

[5]  Turan Erden,et al.  Multi-criteria site selection for fire services: the interaction with analytic hierarchy process and geographic information systems , 2010 .

[6]  Donald R. Plane,et al.  Mathematical Programming and the Location of Fire Companies for the Denver Fire Department , 1977, Oper. Res..

[7]  Jane M. Hogg,et al.  The Siting of Fire Stations , 1968 .

[8]  Mohammad Javad Koohsari,et al.  Spatial Analysis of Urban Fire Station Locations by Integrating AHP Model and IO Logic Using GIS (A Case Study of Zone 6 of Tehran) , 2008 .

[9]  Charles S. ReVelle,et al.  The Location of Emergency Service Facilities , 1971, Oper. Res..

[10]  Ivana Ljubić,et al.  The recoverable robust facility location problem , 2015 .

[11]  V. Doyuran,et al.  Landfill site selection by using geographic information systems , 2006 .

[12]  Karin Pfeffer,et al.  Risk perception: The social construction of spatial knowledge around climate change-related scenarios in Lima , 2016 .

[13]  José L. Verdegay,et al.  An approach for solving maximal covering location problems with fuzzy constraints , 2016, Int. J. Comput. Intell. Syst..

[14]  Shitai Bao,et al.  Optimizing watchtower locations for forest fire monitoring using location models , 2015 .

[15]  Richard L. Church,et al.  The maximal covering location problem , 1974 .

[16]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[17]  Bo Huang,et al.  Optimal Siting of Fire Stations using GIS and ANT Algorithm , 2006 .

[18]  Alan T. Murray,et al.  A computational approach for eliminating error in the solution of the location set covering problem , 2013, Eur. J. Oper. Res..

[19]  Estimation of Seismic Risk at Fire Stations in Lima City and Callao Region , 2018, 2018 Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI).

[20]  Esra Karasakal,et al.  A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage , 2016 .

[21]  Alan T. Murray Optimising the spatial location of urban fire stations , 2013 .