Науковий вісник НГУ

Two-stage problems of optimal location and distribution of the humanitarian logistics system’s structural subdivisions

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №1 2024

Last Updated on 29 February 2024

Published on 30 November -0001

Hits: 2108

Authors:

L.S.Koriashkina*, orcid.org/0000-0001-6423-092X, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

S.V.Dziuba, orcid.org/0000-0002-3139-2989, Prydneprovsk Research Center of the National Academy of Sciences of Ukraine and of Ministry of Education and Science of Ukraine, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

S.A.Us, orcid.org/0000-0003-0311-9958, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.D.Stanina, orcid.org/0000-0001-6754-0317, Dnipro University of Technology, Dnipro, Ukraine, e-mail:  This email address is being protected from spambots. You need JavaScript enabled to view it.

M.M.Odnovol, orcid.org/0000-0002-2022-7996, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2024, (1): 130 - 139

https://doi.org/10.33271/nvngu/2024-1/130

Abstract:

Purpose. To ensure the rational organization of the evacuation of people from a region affected by an emergency by developing a mathematical and algorithmic toolkit that will allow for the early distribution of transport and material resources, maximizing coverage of the affected areas while minimizing evacuation time.

Methodology. System analysis of evacuation processes; mathematical modeling, the theory of continuous problems of optimal partitioning of sets, non-differentiable optimization.

Findings. The object of the study is the two-stage evacuation logistic processes that occur when serving the population of areas affected by emergencies of a natural or technogenic nature. The research considers the possibility of optimally distributing human flows within the transportation system, the structural subdivisions of which are first-stage centers (first aid stations that carry out the reception of citizens from areas affected by the disaster) and second-stage centers (specialized units of the emergency aid system that provide further services to the evacuated population). The proposed mathematical model deals with the problem of optimally partitioning a continuous set with the placement of subset centers and additional connections. Methods for its solution have been described. We demonstrate the versatility of these models, as they can be used to describe logistic evacuation processes, organize assembly points, intermediate locations, evacuation reception points, and those providing primary assistance to the affected population. We calculate the appropriate number of essential products and deliver them from existing warehouses through distribution centers to the affected areas.

Originality. As preventive measures to increase the level of population safety during an emergency, we consider the optimal placement of rescue facilities and the zoning of the territory to distribute evacuation traffic. We also address the problem of the optimal distribution of human flows in the transport and logistics system.

Practical value. The presented models, methods, and algorithms enable the solution of many practical problems related to the development of preventive measures and the planning of rescue operations to ensure the population’s safety in case of emergencies. The theoretical results obtained are translated into specific recommendations that can be utilized when addressing logistical problems related to the organization of primary evacuation of the population from affected areas and their subsequent transportation to safer locations for further assistance.

Keywords: humanitarian logistics, two-stage evacuation, territorial distribution, mathematical modeling

References.

1. State Service of Ukraine for Emergency Situations (2019). REPORT on the main results of activity. Retrieved from https://www.kmu.gov.ua/storage/app/sites/1/17-civik-2018/zvit_2019/zvit-2019-dsns.pdf.

2. State Service of Ukraine for Emergency Situations (2021). REPORT on the main results of activity. Retrieved from https://www.kmu.gov.ua/storage/app/sites/1/17-civik-2018/zvit2021/zvit2021-dns.pdf.

3. Komyak, V. M., Sobol, A. N., Danilin, A. N., Komyak, V. V., & Kyazimov, K. T. (2020). Optimization of Partitioning the Domain into Subdomains According to Given Limitation of Space. Journal of Automation and Information Sciences, 52(2), 13-26. https://doi.org/10.1615/JAutomatInfScien.v52.i2.20.

4. Abdelgawad, H., & Abdulhai, B. (2013). Emergency evacuation planning as a network design problem: a critical review. Transportation Letters, 1(1), 41-58. https://doi.org/10.3328/TL.2009.01.01.41-58.

5. Dhamala, T. N. (2015). A survey on models and algorithms for discrete evacuation planning network problems. Journal of industrial and management optimization, 11(1), 265-289. https://doi.org/10.3934/jimo.2015.11.265.

6. Qin, L., Xu, W., Zhao, X., & Ma, Y. (2020). Typhoon track change-based emergency shelter location-allocation model: a case study of Wenchang in Hainan province. China. Injury Prevention, 26(3), 196-203. https://doi.org/10.1136/injuryprev-2018-043081.

7. Zhao, X., Coates, G., & Wei, X. (2019). A hierarchical mathematical model of the earthquake shelter location-allocation problem solved using an interleaved MPSO–GA. Geomatics, Natural Hazards and Risk, 10(1), 1712-1737. https://doi.org/10.1080/19475705.2019.1609605.

8. Hong, X., Lejeune, M.A., & Noyan, N. (2015). Stochastic network design for disaster preparedness. IIE Transactions, 47, 329-357. https://doi.org/10.1080/0740817X.2014.919044.

9. Han, L., Gong, C., Gu, L., Qiao, H., Zhang, A., & Liu, M. (2021). A Multi-Zone Staged Indoor Emergency Evacuation Algorithm Based on Time Equalization. ISPRS International Journal of Geo-Information, 10, 499. https://doi.org/10.3390/ijgi10080499.

10. Hezam, I. M., & Nayeem, Mk. (2021). A Systematic Literature Review on Mathematical Models of Humanitarian Logistics. Symmetry, 13(1), 11. https://doi.org/10.3390/sym13010011.

11. Bayram, V., & Yaman, H. (2018). A stochastic programming approach for Shelter location and evacuation planning. RAIRO Operations Research, 52, 779-805. https://doi.org/10.1051/ro/2017046.

12. Kınay, O. B., Kara, B. Y., Saldanha-da-Gama, F., & Correia, I. (2018). Modelling the shelter site location problem using chance constraints: a case study for Istanbul. European Journal of Operational Research, 270(1), 132-145. https://doi.org/10.1016/j.ejor.2018.03.006.

13. Oksuz, M. K., & Satoglu, S. I. (2020). A two-stage stochastic model for location planning of temporary medical centers for disaster response. International Journal of Disaster Risk Reduction, 44, 101426. https://doi.org/10.1016/j.ijdrr.2019.101426.

14. Yahyaei, M., & Bozorgi-Amiri, A. (2019). Robust reliable humanitarian relief network design: An integration of shelter and supply facility location. Annals of Operations Research, 283, 897-916. https://doi.org/10.1007/s10479-018-2758-6.

15. Boonmee, C., Arimura, M., & Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485-498. https://doi.org/10.1016/j.ijdrr.2017.01.017.

16. Mostajabdaveh, M., Gutjahr, W. J., & Salman, F. S. (2019). Inequity-averse shelter location for disaster preparedness. IISE Transactions, 51(8), 809-829. https://doi.org/10.1080/24725854.2018.1496372.

17. Farahani, M., Chaharsooghi, S. K., Woensel, T. V., & Veelenturf, L. P. (2018). Capacitated network-flow approach to the evacuation-location problem. Computers & Industrial Engineering, 115, 407-426. https://doi.org/10.1016/j.cie.2017.11.026.

18. Theeb, N. A., & Murray, C. (2017). Vehicle routing and resource distribution in postdisaster humanitarian relief operations. International Transactions in Operational Research, 24, 1253-1284. https://doi.org/10.1111/itor.12308.

19. Bozorgi-Amiri, A., & Khorsi, M. (2016). A dynamic multi-objective location–routing model for relief logistic planning under uncertainty on demand, travel time, and cost parameters. International Journal of Advanced Manufacturing Technology, 85, 1633-1648. https://doi.org/10.1007/s00170-015-7923-3.

20. Baharmand, H., Comes, T., & Lauras, M. (2019). Bi-objective multi-layer location–allocation model for the immediate aftermath of sudden-onset disasters. Transportation Research Part E: Logistics and Transportation Review, 127, 86-110. https://doi.org/10.1016/j.tre.2019.05.002.

21. Hammad, A. W. A. (2019). A Bilevel Multiobjective Optimisation Approach for Solving the Evacuation Location Assignment Problem. Advances in Civil Engineering, 2019, 11. https://doi.org/10.1155/2019/6052931.

22. Seraji, H., Tavakkoli-Moghaddam, R., & Soltani, R. (2019). A two-stage mathematical model for evacuation planning and relief logistics in a response phase. Journal of Industrial and Systems Engineering, 12, 129-146. Retrieved from https://www.jise.ir/article_76547_bd807e75811b38b778046f4cd5b19b96.pdf.

23. Hong, Y., Li, D., Wu, Q., & Xu, H. (2018). Dynamic Route Network Planning Problem for Emergency Evacuation in Restricted-Space Scenarios. Hindawi Journal of Advanced Transportation, 2018, 13. https://doi.org/10.1155/2018/4295419.

24. Yin, D., Wang, S., & Ouyang, Y. (2020). ViCTS: A novel network partition algorithm for scalable agent-based modeling of mass evacuation. Computers Environment and Urban Systems, 80(1287), 101452. https://doi.org/10.1016/j.compenvurbsys.2019.101452.

25. Rahman, M., Chen, N., Islam, M. M., Dewan, A., Pourghasemi, H. R., Washakh, R. M. A., …, & Ahmed, N. (2021). Location-allocation modeling for emergency evacuation planning with GIS and remote sensing: A case study of Northeast Bangladesh[J]. Geoscience Frontiers, 12(3), 101095. https://doi.org/10.1016/j.gsf.2020.09.022.

26. Us, S., Koriashkina, L., & Stanina, O. (2019). An optimal two-stage allocation of material flows in a transport-logistic system with continuously distributed resource. Radio Electronics, Computer Science, Control, (1). https://doi.org/10.15588/1607-3274-2019-1-24.

27. Bulat, A., Dziuba, S., Minieiev, S., Koriashkina, L., & Us, S. (2020). Solution of the problem to optimize two-stage allocation of the material flows. Mining of Mineral Deposits, 14(1), 27-35. https://doi.org/10.33271/mining14.01.027.

28. Kiseleva, E. M., & Koriashkina, L. S. (2015). Theory of continuous optimal set partitioning problems as a universal mathematical formalism for constructing Voronoi diagrams and their generalizations. II. Algorithms for constructing Voronoi diagrams based on the theory of optimal set partitioning. Cybernetics and Systems Analysis, 51(4), 489-499. https://doi.org/10.1007/s10559-015-9740-y.

29. Stukalo, N., Lytvyn, M., Petrushenko, Y., & Omelchenko, Y. (2020). The achievement of the country’s sustainable development in the conditions of global threats. E3S Web of Conferences, (211), 01029. https://doi.org/10.1051/e3sconf/202021101029.

30. Naumov, V., Taran, I., Litvinova, Y., & Bauer, M. (2020). Optimizing resources of multimodal transport terminal for material flow service. Sustainability (Switzerland), 12(16). https://doi.org/10.3390/su12166545.

• < Prev
• Next >