Skip to main content
Log in

Optimal allocation of material dispatch in emergency events using multi-objective constraint for vehicular networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

In the early stage of large-scale disasters, the first batch of emergency supplies are often in short supply, and decision-makers responsible for material distributions need to send emergency materials to the recipients in the shortest possible time, while also taking into account the minimum transportation costs. In these scenarios, the traditional particle swarm algorithm has been frequently used, however it faces the challenge of “precocious puberty" and is unable to resolve the scheduling problem. To solve this issue, this paper proposes an optimization model for material dispatch in emergency events using a non-dominant sorting algorithm for vehicular communication. The model first satisfies the shortest delivery time and material demand, establishes the shortest route for vehicle travel, and then proposes a multi-objective uncontrolled solving ant colony algorithm to break through the bottleneck of the juvenile algorithm by solving the problems of convergence of NSGA-II algorithm and uneven distribution of Pareto front surface. Moreover, the objective function and constraints for vehicles at each emergency supply point are defined, which must not exceed the total number of available vehicles. The case study shows that the Pareto non-inferior solution searched by NSGA-II is ideal under the premise that multiple goals are optimal, and the Pareto non-inferior solution scheme available for researchers to choose is improved. The model and algorithm objectively optimize the overall layout of emergency material distribution.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Sun, Y., Lin, F., & Xu, H. (2018). Multi-objective optimization of resource scheduling in fog computing using an improved NSGA-II. Wirel. Pers. Commun., 102(2), 1369–1385.

    Article  Google Scholar 

  2. Tang, C., Zhu, C., Wei, X., Peng, H., Wang, Y., (2019) December. Integration of UAV and fog-enabled vehicle: application in post-disaster relief. In: 2019 IEEE 25th international conference on parallel and distributed systems (ICPADS) (pp. 548–555). IEEE.

  3. Sho, H., et al. (2021). Embedding a low-carbon interregional supply chain into a recovery plan for future natural disasters. J. Clean. Prod., 315, 128160.

    Article  Google Scholar 

  4. Dash, B. P., & Dixit, V. (2022). Disaster supply chain with information and digital technology integrated in its institutional framework. Int. J. Prod. Res. https://doi.org/10.1080/00207543.2022.2042612

    Article  Google Scholar 

  5. Fragkiadakis, A. G., Askoxylakis, I. G., Tragos, E. Z., & Verikoukis, C. V. (2011). Ubiquitous robust communications for emergency response using multi-operator heterogeneous networks. EURASIP J. Wirel. Commun. Netw., 2011(1), 1–16.

    Article  Google Scholar 

  6. Florios, K., Mavrotas, G., & Diakoulaki, D. (2010). Solving multiobjective, multiconstraint knapsack problems using mathematical programming and evolutionary algorithms. Europ. J. Op. Res., 203(1), 14–21.

    Article  MathSciNet  Google Scholar 

  7. Marinescu, R., (2010) October. Best-first vs. depth-first and/or search for multi-objective constraint optimization. In: 2010 22nd IEEE international conference on tools with artificial intelligence (Vol. 1, pp. 439–446). IEEE.

  8. Qing, C. (2018). Vehicle scheduling model of emergency logistics distribution based on internet of things. Int. J. Appl. Decis. Sci., 11(1), 36–54.

    Google Scholar 

  9. Wex, F., Schryen, G., Feuerriegel, S., & Neumann, D. (2014). Emergency response in natural disaster management: allocation and scheduling of rescue units. Europ. J. Op. Res., 235(3), 697–708.

    Article  MathSciNet  Google Scholar 

  10. Liu, S.C., Chen, C., Zhan, Z.H. and Zhang, J., 2021, March. Multi-objective emergency resource dispatch based on coevolutionary multiswarm particle swarm optimization. In: International conference on evolutionary multi-criterion optimization (pp. 746–758). Springer, Cham.

  11. Liu, C., Zeng, Q., Duan, H., Zhou, M., Lu, F., & Cheng, J. (2014). E-net modeling and analysis of emergency response processes constrained by resources and uncertain durations. IEEE Trans. Syst. Man Cybern.: Syst., 45(1), 84–96.

    Article  Google Scholar 

  12. Zahedi, A., Kargari, M., & Kashan, A. H. (2020). Multi-objective decision-making model for distribution planning of goods and routing of vehicles in emergency multi-objective decision-making model for distribution planning of goods and routing of vehicles in emergency. Int. J. Disaster Risk Reduct., 48, 101587.

    Article  Google Scholar 

  13. Zheng, Y. J., Wang, Y., Ling, H. F., Xue, Y., & Chen, S. Y. (2017). Integrated civilian–military pre-positioning of emergency supplies: a multiobjective optimization approach. Appl. Soft Comput., 58, 732–741.

    Article  Google Scholar 

  14. Wang, D., Qi, C., & Wang, H. (2014). Improving emergency response collaboration and resource allocation by task network mapping and analysis. Safety Sci., 70, 9–18.

    Article  Google Scholar 

  15. Zhang, J. H., Li, J., & Liu, Z. P. (2012). Multiple-resource and multiple-depot emergency response problem considering secondary disasters. Exp. Syst. Appl., 39(12), 11066–11071.

    Article  Google Scholar 

  16. Biswas, P. P., Ray, S., & Samanta, A. N. (2007). Multi-objective constraint optimizing IOL control of distillation column with nonlinear observer. J. Process. Control, 17(1), 73–81.

    Article  Google Scholar 

  17. Xu, X., Gu, R., Dai, F., Qi, L., & Wan, S. (2020). Multi-objective computation offloading for internet of vehicles in cloud-edge computing. Wirel. Networks, 26(3), 1611–1629.

    Article  Google Scholar 

  18. Jiang, Y., Li, L., & Liu, Z. (2018). A multi-objective robust optimization design for grid emergency goods distribution under mixed uncertainty. IEEE Access, 6, 61117–61129.

    Article  Google Scholar 

  19. Ji, B., Yuan, X., & Yuan, Y. (2017). Modified NSGA-II for solving continuous berth allocation problem: using multiobjective constraint-handling strategy. IEEE Trans. Cybern., 47(9), 2885–2895.

    Article  Google Scholar 

  20. Parthiban, P., & Raman, P. (2020). Multi-objective constraint and hybrid optimisation-based VM migration in a community cloud. IET Comput. Digit. Tech., 14(1), 37–45.

    Article  Google Scholar 

  21. Liu, C., Li, L., & Huang, Y. (2012). Optimization research on distribution of emergency supplies and minimize save points based on grey correlation analysis. Adv. Inf. Sci. Serv. Sci., 4(15), 50.

    Google Scholar 

  22. Xiong, X., Zhao, F., Wang, Y., & Wang, Y. (2019). Research on the model and algorithm for multimodal distribution of emergency supplies after earthquake in the perspective of fairness. Math. Problems Engi., 2019, 1.

    Google Scholar 

  23. Li, X., Yin, H., & Yan, F. (2020). Routing optimization of the emergency supplies distribution vehicles using NSGA-II algorithm: a case study. MATEC Web Conf. EDP Sci., 325, 03002.

    Article  Google Scholar 

  24. Jiang, J., Li, Q., Wu, L., & Tu, W. (2017). Multi-objective emergency material vehicle dispatching and routing under dynamic constraints in an earthquake disaster environment. ISPRS Int. J. Geo-Inf., 6(5), 142.

    Article  Google Scholar 

  25. Vitoriano, B., Ortuño, M. T., Tirado, G., & Montero, J. (2011). A multi-criteria optimization model for humanitarian aid distribution. J. Global optimiz., 51(2), 189–208.

    Article  MathSciNet  Google Scholar 

  26. Najafi, M., Eshghi, K., & Dullaert, W. (2013). A multi-objective robust optimization model for logistics planning in the earthquake response phase. Trans. Res. Part E: Logist. Trans. Rev., 49(1), 217–249.

    Article  Google Scholar 

  27. Haghani, A., & Oh, S. C. (1996). Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations. Trans. Res. Part A: Policy Practice, 30(3), 231–250.

    Google Scholar 

  28. Jotshi, A., Gong, Q., & Batta, R. (2009). Dispatching and routing of emergency vehicles in disaster mitigation using data fusion. Socio-Economic Plan. Sci., 43(1), 1–24.

    Article  Google Scholar 

Download references

Funding

There is no specific funding to support this research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chen Liu.

Ethics declarations

Conflict of interest

The authors declared that they have no conflict of interest regarding this work.

Data availability

The experimental data used to support the findings of this study are available from the corresponding author upon request.

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 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

Liu, C., Qian, Y. Optimal allocation of material dispatch in emergency events using multi-objective constraint for vehicular networks. Wireless Netw 28, 3715–3727 (2022). https://doi.org/10.1007/s11276-022-03069-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-022-03069-8

Keyword

Navigation