Skip to main content
SpringerLink
Log in

A Hierarchical Multi-objective Programming Approach to Planning Locations for Macro and Micro Fire Stations

  • Conference paper
  • First Online:
Computer and Communication Engineering (CCCE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1630))

Included in the following conference series:

Abstract

Fire stations are among the most crucial emergency facilities in urban emergency control system in terms of their quick response to fires and other emergencies. Location planning for fire stations has a significant influence on their effectiveness and capability of emergency responses trading off with the cost of constructions. To obtain efficient and practical siting plans for fire stations, various major requirements including effectiveness maximization, distance constraint and workload limitation are required to be considered in location models. This paper proposes a novel hierarchical optimization approach taking all the major requirements for location planning into consideration and bonds functional connections between different levels of fire stations at the same time. A single-objective and a multi-objective optimization model are established coupled with genetic algorithm (GA) with elitist reservation and Pareto-based multi-objective evolutionary algorithm for model solving. The proposed hierarchical location model is further performed in a case study of Futian District in Shenzhen, and the siting results justify the effectiveness and practicality of our novel approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Farahani, R.Z., Fallah, S., Ruiz, R., Hosseini, S., Asgari, N.: OR models in urban service facility location: a critical review of applications and future developments. Eur. J. Oper. Res. 276(1), 1–27 (2019)

    Article  MathSciNet  Google Scholar 

  2. Paul, N., Lunday, B., Nurre, S.: A multi-objective, maximal conditional covering location problem applied to the relocation of hierarchical emergency response facilitie. Omega 66, 147–158 (2017)

    Article  Google Scholar 

  3. Yu, W., Chen, Y., Guan, M.: Hierarchical siting of macro fire station and micro fire station. Environ. Plan. B: Urban Analytics City Sci. 48, 1972–1988 (2020)

    Google Scholar 

  4. Degel, D., Wiesche, L., Rachuba, S., Werners, B.: Reorganizing an existing volunteer fire station network in Germany. Socio-Econ. Plann. Sci. 48(2), 149–157 (2014)

    Article  Google Scholar 

  5. http://www.mohurd.gov.cn/

  6. Church, R.L., ReVelle, C.S.: Theoretical and computational links between the p-median, location set-covering, and the maximal covering location problem. Geogr. Anal. 8(4), 406–415 (1976)

    Article  Google Scholar 

  7. Yang, L., Jones, B.F., Yang, S.H.: A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms. Eur. J. Oper. Res. 181(2), 903–915 (2007)

    Article  Google Scholar 

  8. Yao, J., Zhang, X., Murray, A.T.: Location optimization of urban fire stations: access and service coverage. Comput. Environ. Urban Syst. 73, 184–190 (2019)

    Article  Google Scholar 

  9. Aktaş, E., Özaydın, Ö., Bozkaya, B., Ülengin, F., Önsel, Ş: Optimizing fire station locations for the istanbul metropolitan municipality. Interfaces 43(3), 240–255 (2013)

    Article  Google Scholar 

  10. Bolouri, S., Vafaeinejad, A., Alesheikh, A., Aghamohammadi, H.: Minimizing response time to accidents in big cities: a two ranked level model for allocating fire stations. Arab. J. Geosci. 13(16), 1–13 (2020). https://doi.org/10.1007/s12517-020-05728-6

    Article  Google Scholar 

  11. Plane, D., Hendrick, T.: Mathematical programming and the location of fire companies for the denver fire department. Oper. Res. 25, 563–578 (1977)

    Article  Google Scholar 

  12. Schilling, D., Revelle, C., Cohon, J., Elzinga, D.: Some models for fire protection locational decisions. Eur. J. Oper. Res. 5, 1–7 (1980)

    Article  Google Scholar 

  13. Badri, M., Mortagy, A., Alsayed, C.: a multi-objective model for locating fire stations. Eur. J. Oper. Res. 110, 243–260 (1998)

    Article  Google Scholar 

  14. Murray, A.: Optimising the spatial location of urban fire stations. Fire Saf. J. 62, 64–71 (2013)

    Article  Google Scholar 

  15. Sakawa, M., Kato, K., Sunada, H., Shibano, T.: Fuzzy programming for multi-objective 0–1 programming problems through revised genetic algorithms. Eur. J. Oper. Res. 97, 149–158 (1997)

    Article  Google Scholar 

  16. Tzeng, G.-H., Chen, Y.-W.: The optimal location of airport fire stations: a fuzzy multi-objective programming and revised genetic algorithm approach. Transp. Plan. Technol. 23, 37–55 (2007)

    Article  Google Scholar 

  17. Farahani, R.Z., Hekmatfar, M., Fahimnia, B., Kazemzadeh, N.: Hierarchical facility location problem: Models, classifications, techniques, and applications. Comput. Ind. Eng. 68, 104–117 (2014)

    Article  Google Scholar 

  18. Şahin, G., Süral, H., Meral, S.: Locational analysis for regionalization of Turkish Red Crescent blood services. Comput. Oper. Res. 34(3), 692–704 (2007)

    Article  Google Scholar 

  19. Gourdin, E., Labbé, M., Yaman, H.: Telecommunication and location (2001)

    Google Scholar 

  20. http://www.geatpy.com/

  21. http://www.szft.gov.cn/

  22. Chen, J., Yang, S., Li, H., Zhang, B., Lv, J.: Research on geographical environment unit division based on the method of natural breaks (Jenks). Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 3, 47–50 (2013)

    Article  Google Scholar 

  23. Zhao, M., Chen, Q.: Risk-based optimization of emergency rescue facilities locations for large-scale environmental accidents to improve urban public safety. Nat. Hazards 75(1), 163–189 (2014). https://doi.org/10.1007/s11069-014-1313-2

    Article  Google Scholar 

  24. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  25. Konstantinidis, A., Yang, K., Zhang, Q.: An evolutionary algorithm to a multi-objective deployment and power assignment problem in wireless sensor networks. In: IEEE GLOBECOM 2008-2008 IEEE Global Telecommunications Conference. IEEE (2008)

    Google Scholar 

  26. Men, J., et al.: A multi-objective emergency rescue facilities location model for catastrophic interlocking chemical accidents in Chemical Parks. IEEE Trans. Intell. Transp. Syst. 21(11), 4749–4761 (2019)

    Article  Google Scholar 

  27. Audet, C., Bigeon, J., Cartier, D., Le Digabel, S., Salomon, L.: Performance indicators in multiobjective optimizatio. Eur. J. Oper. Res. 292(2), 397–422 (2020)

    Article  Google Scholar 

  28. Sun, Y., Yen, G.G., Yi, Z.: IGD indicator-based evolutionary algorithm for many-objective optimization problems. IEEE Trans. Evol. Comput. 23(2), 173–187 (2018)

    Article  Google Scholar 

  29. Fonseca, C.M., Paquete, L., López-Ibánez, M.: An improved dimension-sweep algorithm for the hypervolume indicator. In: 2006 IEEE International Conference on Evolutionary Computation. IEEE (2006)

    Google Scholar 

Download references

Acknowledgement

This research was supported by National Key R&D Program of China (2019YFC0810700 and 2018YFC-0807000), National Natural Science Foundation of China (71771113, 71704091 and 71804026 No. 72004141) and Shenzhen Science and Technology Plan Project (N0. JSGG20180717170802038) and Basic and Applied Basic Research Foundation of Guangdong Province (No. 2019-A1515111074).

Author information

Authors and Affiliations

  1. Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China

    Xinghan Gong, Jun Liang, Yiping Zeng, Fanyu Meng & Lili Yang

  2. Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China

    Fanyu Meng

  3. Department of Computer and Information Science, University of Macau, Macau, China

    Simon Fong

Authors
  1. Xinghan Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yiping Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fanyu Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Simon Fong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lili Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lili Yang .

Editor information

Editors and Affiliations

  1. University of Naples Federico II, Naples, Italy

    Filippo Neri

  2. Concordia University, Montreal, QC, Canada

    Ke-Lin Du

  3. The University of New South Wales, Sydney, NSW, Australia

    Vijaya Kumar Varadarajan

  4. Miguel Hernandez University, Elche, Spain

    San-Blas Angel-Antonio

  5. Northwestern Polytechnical University, Xi'an, China

    Zhiyu Jiang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gong, X., Liang, J., Zeng, Y., Meng, F., Fong, S., Yang, L. (2022). A Hierarchical Multi-objective Programming Approach to Planning Locations for Macro and Micro Fire Stations. In: Neri, F., Du, KL., Varadarajan, V.K., Angel-Antonio, SB., Jiang, Z. (eds) Computer and Communication Engineering. CCCE 2022. Communications in Computer and Information Science, vol 1630. Springer, Cham. https://doi.org/10.1007/978-3-031-17422-3_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-17422-3_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17421-6

  • Online ISBN: 978-3-031-17422-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation