Quantum competitive decision algorithm for the emergency siting problem under given deadline conditions | Cluster Computing Skip to main content
Springer Nature Link
Log in

Quantum competitive decision algorithm for the emergency siting problem under given deadline conditions

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Allocating emergency resources effectively is an essential aspect of disaster preparation and response. The Emergency Siting Problem (ESP) involves identifying the best places to locate emergency services in order that it can serve the most people in the least amount of time. Maintaining time limitations is of greatest significance in situations where each second matters, such as during disasters or public health emergencies. In this study, we concentrate on the difficulty of solving the ESP under extreme time limits. In this research, Genetic-adaptive reptile search optimization (GRSO) is proposed to provide a different way to solve the ESP problem within the constraints of limited time. The proposed GRSO method takes into account travel times, prospective facility places, and the geographic location of demand sites while keeping to the established time restrictions. In this study, the proposed method demonstrating superior performance accuracy in locating transportation facilities under extreme time limits for Emergency Service Planning (ESP), outperforming established optimization strategies and heuristics commonly applied to ESP problems. A fitness function is created to assess the standard of responses based on elements including response speed, coverage, and meeting deadlines. The GRSO algorithm has been modified and altered to handle the distinctive features of the ESP, such as precise facility placements and time constraints. Simulated and real-world datasets describing emergency circumstances are used in computational research to confirm the efficiency of the proposed method. The results are evaluated with established optimization strategies and heuristics generally applied to ESP problems. Results show that the GRSOapproach provides solutions that are more in pace with time limit constraints without sacrificing sufficient degrees of coverage or response time.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Algorithm 1
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Figure10

Similar content being viewed by others

References

  1. Li, P., Fan, X.: The Application of the Improved Jellyfish Search Algorithm in a Site Selection Model of an Emergency Logistics Distribution Center Considering Time Satisfaction. Biomimetics 8(4), 349 (2023)

    Article  MathSciNet  Google Scholar 

  2. Xu, W., Xu, J., Proverbs, D., Zhang, Y.: A hybrid decision-making approach for locating rescue materials storage points under public emergencies. Kybernetes 53(1), 293–313 (2022)

    Article  Google Scholar 

  3. Liu, J., Li, Y., Li, Y., Zibo, C., Lian, X., Zhang, Y.: Location optimization of emergency medical facilities for public health emergencies in megacities based on genetic algorithm. Eng. Constr. Archit. Manag. 30(8), 3330–3356 (2022)

    Article  Google Scholar 

  4. Newton, R.A., Kumara, S., Ventura, J.A. and Griffin, P.M.: A Generalized Model for Including Equity in the Siting of Emergency Services (2022)

  5. Karatas, M., Yakıcı, E.: An iterative solution approach to a multi-objective facility location problem. Appl. Soft Comput.Comput. 62, 272–287 (2018)

    Article  Google Scholar 

  6. Han, S., Park, J., Jeong, H.: A solution procedure for emergency logistics problem in disaster scene. Industrial Engineering & Management Systems 17(2), 166–176 (2018)

    Article  Google Scholar 

  7. Yan, X., Hou, H., Yang, J., Fang, J.: Site Selection and Layout of Material Reserve Based on Emergency Demand Graduation under Large-Scale Earthquake. Sustainability 13(3), 1236 (2021)

    Article  Google Scholar 

  8. Alizadeh, R., Nishi, T.: A genetic algorithm for multi-period location problem with modular emergency facilities and backup services. Transactions of the Institute of Systems, Control and Information Engineers 32(10), 370–377 (2019)

    Article  Google Scholar 

  9. Yu, W., Guan, M., Chen, Y.: Fire stations siting with multiple objectives and geospatial big data. Earth Sci. Inf. 14, 141–160 (2021)

    Article  Google Scholar 

  10. Yang, B., Wu, L., Xiong, J., Zhang, Y. and Chen, L.: Planning of location and path for urban emergency rescue by an approach with hybridization of clustering and ant colony algorithm. Available at SSRN 4041695 (2022)

  11. Trivedi, A., Singh, A.: A hybrid multi-objective decision model for emergency shelter location-relocation projects using fuzzy analytic hierarchy process and goal programming approach. Int. J. Project Manage. 35(5), 827–840 (2017)

    Article  Google Scholar 

  12. Rodríguez-Espíndola, O., Albores, P., Brewster, C.: Disaster preparedness in humanitarian logistics: A collaborative approach for resource management in floods. Eur. J. Oper. Res.Oper. Res. 264(3), 978–993 (2018)

    Article  MathSciNet  Google Scholar 

  13. Manopiniwes, W., Irohara, T.: Stochastic optimisation model for integrated decisions on relief supply chains: preparedness for disaster response. Int. J. Prod. Res. 55(4), 979–996 (2017)

    Article  Google Scholar 

  14. He, J., Feng, C., Hu, D., Liang, L.: A decision model for emergency warehouse location based on a novel stochastic MCDA method: evidence from China. Math. Probl. Eng. 2017, 1–10 (2017)

    Article  Google Scholar 

  15. Alosta, A., Elmansuri, O., Badi, I.: Resolving a location selection problem by means of an integrated AHP-RAFSI approach. Reports in Mechanical Engineering 2(1), 135–142 (2021)

    Article  Google Scholar 

  16. Zhang, B., Peng, J., Li, S.: Covering location problem of emergency service facilities in an uncertain environment. Appl. Math. Model. 51, 429–447 (2017)

    Article  MathSciNet  Google Scholar 

  17. Demir, E.: Approach for siting a support facility for transporting supplies in emergency cases in the Republic of Bulgaria. MATEC Web Conf. 234, 06001 (2018)

    Article  Google Scholar 

  18. Alizadeh, R., Nishi, T.: Hybrid set covering and dynamic modular covering location problem: application to an emergency humanitarian logistics problem. Appl. Sci. 10(20), 7110 (2020)

    Article  Google Scholar 

  19. Rizeei, H.M., Pradhan, B., Saharkhiz, M.A.: Allocation of emergency response centres in response to pluvial flooding-prone demand points using integrated multiple layer perceptron and maximum coverage location problem models. International Journal of Disaster Risk Reduction 38, 101205 (2019)

    Article  Google Scholar 

  20. Zhou, J., and Shao, Y.: Rational selection of rail transit emergency site using complex network topology and genetic algorithm. Scientific Programming, 2022 (2022)

  21. Cui, S., Liu, S., Tang, X. and Zhu, T.: March. Emergency material allocation problem considering post-disaster impact. In 2019 8th International Conference on Industrial Technology and Management (ICITM) (pp. 290–294). IEEE (2019)

  22. Geng, J., Hou, H., Geng, S.: Optimization of warehouse location and supplies allocation for emergency rescue under joint government–enterprise cooperation considering disaster victims’ distress perception. Sustainability 13(19), 10560 (2021)

    Article  Google Scholar 

  23. Babaei, A., Shahanaghi, K.: A new model for planning the distributed facilities locations under emergency conditions and uncertainty space in relief logistics. Uncertain Supply Chain Management 5(2), 105–125 (2017)

    Article  Google Scholar 

  24. Zhao, M., Chen, Q.W., Ma, J. and Cai, D.: Optimizing Temporary Rescue Facility Locations for Large-Scale Urban Environmental Emergencies to Improve Public Safety. Journal of Environmental Informatics, 29(1) (2017)

  25. Gai, L., Peng, Z., Zhang, J., Zhang, J.: Emergency medical center location problem with people evacuation solved by extended TODIM and objective programming. J. Comb. Optim.Optim. 42, 1004–1029 (2021)

    Article  MathSciNet  Google Scholar 

Download references

Funding

R&D Program of Beijing Municipal Education Commission, KM202310020001.

Author information

Authors and Affiliations

  1. School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Qinhuangdao, 066004, Hebei, China

    Wei Zhao

  2. Computer Center, Hebei Normal University of Science & Technology, Qinhuangdao, 066004, Hebei, China

    Weiming Gao

  3. College of Intelligent Science and Engineering, Beijing University of Agriculture, Beijing, 066000, China

    Shengnan Gao

  4. Soochow University, Suzhou, 215127, Jiangsu, China

    Chenmei Teng & Xiaoya Zhu

Authors
  1. Wei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weiming Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shengnan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenmei Teng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoya Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design.All authors read and approved the final manuscript

Corresponding authors

Correspondence to Shengnan Gao or Xiaoya Zhu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, W., Gao, W., Gao, S. et al. Quantum competitive decision algorithm for the emergency siting problem under given deadline conditions. Cluster Comput 27, 13029–13046 (2024). https://doi.org/10.1007/s10586-024-04548-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-024-04548-7

Keywords

Search

Navigation