Method of reducing the time of operational deployment by the first emergency rescue department

 

Dmitry Belyuchenko

National University of Civil Defence of Ukraine

https://orcid.org/0000-0001-7782-2019

 

Denys Lovin

National University of Civil Defence of Ukraine

https://orcid.org/0000-0002-1066-0286

 

Alexandr Soshinskiy

National University of Civil Defence of Ukraine

https://orcid.org/0000-0002-7921-1294

 

Viktor Strelets

National University of Civil Defence of Ukraine

https://orcid.org/0000-0001-5992-1195

 

Igor Khmyrov

National University of Civil Defence of Ukraine

https://orcid.org/0000-0002-7958-463X

 

DOI: https://doi.org/10.52363/2524-0226-2022-35-19

 

Keywords: operational deployment, fire and rescue vehicles, modeling, ranking

 

Аnnotation

The application of experimental research planning methods showed that the obtained multifactor models of operation of the system "rescuer – rescue equipment – emergency" should be the basis of appropriate methods to reduce the time of operational deployment of fire and rescue vehicles by the first rescue unit during emergencies. The basis of this methodology is the development and verification of operational and technical recommendations in accordance with the maximum differences in one-factor models obtained in the center and at the edges of factor space for three-factor polynomial models in normalized variables. deployments of fire and rescue vehicles by the first operational and rescue unit, provides for the sequential implementation of four procedures, namely: their simulation (in that case physical modeling) in accordance with the 3x3x3 plan, taking into account the factors that characterize the person (personnel of the rescue unit), equipment (fire and rescue vehicles and their equipment, rescue equipment, etc.) and environment (operational conditions rescuers' activities); expert substantiation of recommendations for implementation; selection of operational and technical recommendations for implementation in regulatory documents based on the results of statistical assessments of how effective their implementation is. At the same time, it should be borne in mind that to apply the chosen approach it is necessary to obtain a large amount of source data. In addition, a significant limitation of the developed approach is the need to involve highly qualified experts at all stages of the methodology.

 

References

  1. On approval of the Charter of emergency actions of governing bodie sand units of the operational and rescue service of civil protection and the Charter of actions of governing bodies and units of the operational and rescue service of civil protection in fighting fires. Order of the Ministry of Emergency Situations of Ukraine 04.26.2018 № 340. Information and documentation. https://zakon.rada.gov.ua/laws/show/z0801-18#Text
  2. Prysyazhniuk, V., Yakimenko, M., Kukharishin, S. (2013). Analysis of the current state of the fleet of fire and rescue vehicles in Ukraine and the effectiveness of fire and rescue units. Scientific Bulletinof the Ukr RIFS, 1(27), 68–74. Retrieved from: http://firesafety.at.ua/visnyk/2013_No_1-27/15_Prisyazhnyuk_Jakimenko_Kukharishyn.pdf
  3. Firefighting and rescue service vehicles. Common requirements. Safety and performance. BS EN 1846-2:2009+A1:2013 Information and documentation. doi: 10.3403/30233210
  4. Emergency Incident Rehabilitation. February. (2018). URL: www.usfa.fema.gov/downloads/pdf/publications/fa_314.pdf Information and documentation.
  5. Ming, J., Richard, JP. P., Qin, R. (2022). Distributionally robust optimization for fire station location under uncertainties. Sci Rep 12, doi: 10.1038/s41598-022-08887-6
  6. Standard on Fire Department Occupational Safety and Health Program. NFPA 1500. (2012). Edition. URL: www.fsans.ns.ca/pdf/research/nfpa1500.pdf Information and documentation
  7. Nowicki, T. (2017). Optimization of equipment deploymenton firetrucks. MATEC WebConf, 125, 02016. doi: 10.1051/matecconf/201712502016
  8. Duncan, M. D., Littau, S. R., Kurzius-Spencer, M. (2014). Development of Best Practice Standard Operating Procedures for Prevention of Fireground Injuries. Fire Technol, 50, 1061–1076. doi: 1007/s10694-013-0342-9
  9. Belyuchenko, D., Strelets, V., (2020). Multivariate as sessment of the effectiveness of the operational development of fire trucks in the face of industrial emergencies. Municipal Economy of Cities, Series: Engineering science and architecture, 3, 156, 204–211. doi: 10.33042/2522-1809-2020-3-156-204-211
  10. Zelnio, H., Fendley, M. (2018). Human performance modelling for image analyst decision support design. International Journal of Human Factors Modelling and Simulation, 6, 2–3, 184–202. doi : 10.1504/IJHFMS.2018.093183
  11. Klasyfikator nadzvychainykh sytuatsii DK 019:2010. 10.11.2010 Edition. 457. Information and documentation.
  12. Kamyshentsev, H. V., Soloviov, I. I., Belyuchenko, D. Yu., Strelets, V. М. (2020). Information and technical method for preventing emergency situations by the integrated use of acoustic control systems in the context process of functioning of the system "emergency situation – rescue operations – rescuer". Engineering of nature management, 3(17), 133–139. doi: 10.37700/enm.2020.3(17).133-139
  13. Voznesenskiy, V. A. (1981). Statisticheskiye metody planirovaniya eksperimenta v tekhniko-ekonomicheskikh issledovaniyakh. Finansy i statistika, 263.
  14. Strelets, V., Borody`ch, P. (2004). Multifactorial assessment of fire and rescue operations at metro stations. Problems of Fire Safety, 15, 208–214.
  15. Vasil`ev, M., Strelec, V., Trigub, V. (2013). Analysis of multifactor model of the system "rescuers – protection and emergency response – emergency release of hazardous chemicals". Problems of Emergency Situations, 18, 22–33.
  16. Soloviov, I., Strelets, V., Lovin, D. (2021). Multifactor model of excavation op an explosive subject dive. Problems of Emergency Situations, 2(34), 272–294. doi: 10.52363/2524-0226-2021-34-20
  17. Bealt, J., Shaw, D., Smith, M., López-Ibáñez, M. (2019). Peer reviews for making cities resilient. International Journal of Emergency Management (IJEM), 15, 4, 334 – 359. doi: 10.1504/IJEM.2019.104201