Abstract
In crowded cities, like Tehran, when a major accident occurs, such as a fire, the response from more than one fire station is usually needed at the scene. The present study focuses on demand allocation to fire stations at two ranked levels to determine the priorities of fire stations to service relevant demands. To solve this problem, this paper uses the Vector Assignment Ordered Median Problem (VAOMP), a new location–allocation model that can allocate demands to facilities at several ranked levels, based on the particular objective function. Thus, this paper uses the meta-heuristic methods of Tabu and genetic algorithms to minimize the arrival time from fire stations to demands, at two levels, at up to 5 min in the GIS environment of the 21st and 22nd districts of Tehran. The optimum parameters for each algorithm were obtained through sensitivity analysis. The results of applying the model with two algorithms in these districts with 10 existing fire stations and 336,600 inhabitants showed that the current stations are insufficient for two levels of service and that 52,840 people at level 1 and 81,320 people at level 2 have no access to services. As such, the results of two algorithms for relocation–reallocation analysis at two levels with different weightings for 13 potential and existing fire stations showed that at least 3 new stations need to be created. Furthermore, the genetic algorithm produced qualitatively superior results, in optimal values, the accuracy of allocation and timeframe, compared with the Tabu algorithm.
Similar content being viewed by others
Notes
Ordered median problem
Maximal covering location problem
Integer linear programming
References
Abd-El Monsef H, E. Smith S (2019) Integrating remote sensing, geographic information system, and analytical hierarchy process for hazardous waste landfill site selection. Arab J Geosci 12:155
Aghamohammadi H, Mesgari M, Molaei D, Aghamohammadi H (2013) Development a heuristic method to locate and allocate the medical centers to minimize the earthquake relief operation time. Iran J Public Health 42(1):63–71
Akbary M, Kermani A, Alijani A (2018) Simulation and analysis of polluted days in Tehran. International Journal of Environmental Research 12(1):67–75
Algharib S (2011) Distance and coverage: an assessment of location-allocation models for fire stations in Kuwait city, Kuwait. Dissertation for the degree of Doctor of Philosophy, Kent State University.
Beyazli D, Aydemir S, Oksuz A, Ozlu S (2017) Rural topology with and inductive approach. International Journal of Environmental Research 11(2):225–241
Boland N, Domìnguez-Marìn P, Nickel S, Puerto J (2006) Exact procedures for solving the discrete ordered median problem. Comput Oper Res 33:3270–3300
Bolouri S, Vafaeinejad A, Alesheikh A, Aghamohammadi H (2018) The ordered capacitated multi objective location-allocation problem for fire stations. ISPRS Int J Geo Inf 7(2):44
Bolouri S, Vafaeinejad A, Alesheikh A, Aghamohammadi H (2019) Investigating the effect of capacity criterion on the optimal allocation of emergency facilities in GIS environment. Geospatial conference2019. Joint conferences SMPR and GI research, 12-14 October, Karaj, Tehran.
Brandeau ML, Chiu S (1989) An overview of representative problems in location research. Manag Sci 33:645–674
Church RL, ReVelle CS (1976) Theoretical and computational links between the p-median, location set-covering, and the maximal covering location problem. Geogr Anal 8(4):407–415
Didier Lins I, López Droguett E (2011) Redundancy allocation problems considering systems with imperfect repairs using multi-objective genetic algorithms and discrete event simulation. Simul Model Pract Theory 19:362–381
Domìnguez-Marìn P, Nickel S, Hansen P, Mladenovìc N (2005) Heuristic procedures for solving the discrete ordered median problem. Ann Oper Res 136:145–173
Fan W, Machemehl R (2008) Tabu search strategies for the public transportation network optimizations with variable transit demand. Computer-Aided Civil and Infrastructure Engineering 23:502–520
Geroliminis N, Kepaptsoglou K, Karlaftis M (2011) A hybrid hypercube-genetic algorithm approach for deploying many emergency response mobile units in an urban network. Eur J Oper Res 210(2):287–300
Habet D (2009) Tabu search to solve real life combinatorial optimization problems: a case of study. Foundations of Computational Intelligence 3:129–151
Habibi K, Lotfi S, Koohsari MJ (2008) Spatial analysis of urban fire station locations by integrating AHP model and IO logic using GIS. J Appl Sci 8(9):3302–3315
Hillsman EL (1984) The p-median structure as a unified linear model for location-allocation analysis. Environ Plan A 16:305–318
Holland J (1975) Adaption in natural and artificial systems. Cambridge, M. A: MIT Press.
Jaramillo J, Bhadury HJ, Batta R (2002) On the use of genetic algorithms to solve location problems. Comput Oper Res 29:761–779
Kratica J, Stanimirovic Z, Tosic D, Filipovic V (2007) Two genetic algorithms for solving the uncapacitated single allocation p-hub median problem. Eur J Oper Res 182(1):15–28
Lei T. L, Church R. L (2014) Vector assignment ordered median problem: a unified median problem. Int Reg Sci Rev 37(2): 194–224
Lei T, Church R, Lei Z (2016) A unified approach for location- allocation analysis: integration GIS, distributed computing and spatial optimization. Int J Geogr Inf Sci 30:515–534
Li X, Yeh A (2005) Integration of genetic algorithms and GIS for optimal location search. International Geographical Information Science 19(5):581–601
Liao SH, Hsieh CL, Lai PJ (2011) An evolutionary approach for multi-objective optimization of the integrated location-inventory distribution network problem in vendor-managed inventory. Expert Syst Appl 38:6768–6776
Murray A (2010) advances in location modeling: GIS linkage and contribution. J Geogr Syst 12:335–354
Neema MN, Ohgai A (2010) Multi-objective location modeling of urban parks and open spaces: continuous optimization. Comput Environ Urban Syst 34:359–376
Nickel S, Puerto J (1999) A unified approach to network location problems. Networks 34(4):283–290
Radwan FA, Alazba A, Mossad A (2018) Estimating potential direct runoff for ungauged urban watersheds based on RST and GIS. Arab J Geosci 11:748
Saidi S, Houimli H, Zid J (2017) Geodetic and GIS tools for dam safety: case of Sidi Salem dam (northern Tunisia). Arab J Geosci 10:505
Shamsul Arifin MD (2011). Location allocation problem using genetic algorithm and simulated annealing: a case study based on school in Enschede. Master of Science in Geo-information Science and Earth Observation. University of Twente.
Stanimirovìc Z, Kratica J, Dugoŝija D (2007) Genetic algorithms for solving the discrete ordered median problem. Eur J Oper Res 182:983–1001
Vafaeinejad A (2017) Dynamic guidance of an autonomous vehicle with spatio-temporal GIS. Lecture notes in Computer Science, LNSC 10407:502–511
Wang K, Makond B, Liu SY (2011) Location and allocation decisions in a two-echelon supply chain with stochastic demand-a genetic-algorithm based solution. Expert Syst Appl 38:1044–1056
Yang LB, Jones F, Yang S (2007) A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms. Eur J Oper Res 181:903–915
Zhou G, Min H, Gen M (2003) A genetic algorithm approach to the bi-criteria allocation of customers to warehouses. Int J Prod Econ 86:35–45
Zolekar R (2018) Integrative approach of RS and GIS in characterization of land suitability for agriculture: a case study of Darna catchment. Arab J Geosci 11:780
Acknowledgements
I would like to thank my supervisors and my family for their support and guidance throughout this work.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of nterest
The authors declare that they have no conflict of interest.
Additional information
Responsible Editor: Biswajeet Pradhan
Rights and permissions
About this article
Cite this article
Bolouri, S., Vafaeinejad, A., Alesheikh, A. et al. Minimizing response time to accidents in big cities: a two ranked level model for allocating fire stations. Arab J Geosci 13, 758 (2020). https://doi.org/10.1007/s12517-020-05728-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-020-05728-6