Developing organizational and technical method of resource suplying formation for emergency situations

Maryna Novozhylova

M. Beketov National University of Urban Economy in Kharkiv

https://orcid.org/0000-0002-9977-7375

Yuliia Mykhailovska

National University of Civil Defenсe of Ukraine

http://orcid.org/0000-0003-1090-5033)

DOI: https://doi.org/10.5281/zenodo.4400160

Keywords: emergency situation due to ammunition explosions, optimization of resource provision, liquidation of emergency situations, forecasting, mobile assistance center

Abstract

The organizational and technical method of formation of resource provision of response to emergencies of technogenic character of regional level, in particular, arising as a result of explosions of ammunition on arsenals and warehouses is developed. The new scientific result consists of a number of mathematical models developed within the framework of the ideology of the scenario approach to forecasting the amount of resource response to emergencies due to munitions explosions, taking into account the uncertainty of their parameters at the stage of strategic planning. Proposed and introduced the concept of a mobile assistance center, which expands the concept of mobile control points, which allows the development of organizational framework for emergency response to ammunition explosions. Components of the organizational and technical method under consideration are the method of determining the parameters of the affected area of a possible emergency situation at the regional level due to ammunition explosions, which allows to take into account the spatial distribution of its consequences, the need for life emergency zone infrastructure. It is shown that when building a strategic emergency response plan at the regional level due to munitions explosions, based on the study of available statistics, by making operational management decisions may be reasonable formation of characteristics of the distribution of parameters of a possible emergency, which is input to formalized scenario approach. The paper builds an information environment and presents the results of numerous experiments to determine the set of scenarios on the example of an emergency situation due to explosions of ammunition in the military arsenal of ammunition in Balaklia, Kharkiv region, March 23, 2017.

References

1. Lyubinsky, A. (2015). Current state and prospects of modernization of the civil defense system of Ukraine. Efficiency of public administration, 43, 104–109.
2. Sebatli, А., Cavdur, F., Kose-Kucuk, M. (2017). Determination of relief supplies demands and allocation of temporary disaster response facilities. Transportation Re-search Procedia, 22, 245–254.
3. Brito, I. Jr., Leiras, A., Yoshizaki, H. (2015). Stochastic optimization applied to the pre-positioning of disaster relief supplies in Brazil. Simpósio Brasileiro de Pesquisa Operacional, Porto de Galinhas, PE. https://www.researchgate.net/publication/ 303939071
4. Ozdamar, L., Demir, O. A. (2012). hierarchical clustering and routing proce-dure for large scale disaster relief logistics planning. Transportation Research Part E: Logistics and Transportation Review, 48(3), 591–602.
5. Xiang, Li, Yongjian, Li. (2012). A Model on Emergency Resource Dispatch under Random Demand and Unreliable Transportation Systems. Engineering Procedia, 5. 248–253.
6. Wang, W., Huang, L., Guo, Z. (2017). Optimization of emergency material dis-patch from multiple depot locations to multiple disaster sites. Sustainability, 9, 1978–1991.
7. Zheng, Y.-J., Ling, H.-F. (2012). Emergency transportation planning in disaster relief supply chain management: a cooperative fuzzy optimization approach. Soft Com-puting, 17(7), 1301–1314.
8. Salman, F. S., Gul, S. (2014). Deployment of field hospitals in mass casualty incidents. Computers & Industrial Engineering, 74, 37–51.
9. Gormez, N., Koksalan, M., Salman, F. S. (2011). Locating disaster response fa-cilities in Istanbul. Journal of the Operational Research Society, 62(7), 1239–1252.
10. Zhang, M.-X., Zhang, B., Zheng, Yu-J. (2014). Bio-Inspired Meta-Heuristics for Emergency. Transportation Problems Algorithms, 7. 15–31.
11. Yu-Jun, Zheng, Sheng-Yong, Chen, Hai-Feng, Ling. (2015). Evolutionary optimi-zation for disaster relief operations: A survey. Applied Soft Computing, 27, 553–566.
12. 12 Chiyoshi, F., Iannoni, A. P., Morabito, R. A tutorial on hypercube queueing models and some practical applications in Emergency Service Systems. Pesquisa Operacional, 2011, 31(2), 271–299.
13. Levterov, O. A., Shevchenko, R. I. (2019). Hardware and software implemen-tation of modern approaches to the prevention of natural emergencies. Problems of emergencies, 1(29), 47–61.
14. Guojun, Ji, Caihong, Zhu. (2012). A Study on Emergency Supply Chain and Risk Based on Urgent Relief Service in Disasters. Systems Engineering Procedia, 5, 313–325.
15. Novozhilova, M. V., Chub, O. I., Mykhailovska, Y. V., Gudak, R. V, Melezhik, R. S. (2019). Development of a hierarchical strategy to increase the level of man-made safety of the territory. Problems of emergencies, 30, 164–177.