International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Health Care Optimization by Maximizing the Air-Ambulance Operation Time

  • Melhim, Loai Kayed B. (Department of Health Information Management and Technology, College of Applied Medical Sciences University of Hafr Al Batin)
  • Received : 2022.02.05
  • Published : 2022.02.28
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Abstract

Employing the available technologies and utilizing the advanced means to improve the level of health care provided to citizens in their various locations. Citizens have the right to get a proper health care services despite the location of their residency or the distance from the health care delivery centers, a goal that can be achieved by utilizing air ambulance systems. In such systems, aircrafts and their life spans are the essential component, the flight duration of the aircraft during its life span is determined by the maintenance schedule. This research, enhances the air ambulance systems by presenting a proposal that maximizes the aircraft flight duration during its life span. The enhancement will be reached by developing a set of algorithms that handles the aircraft maintenance problem. The objective of these algorithms is to minimize the maximum completion time of all maintenance tasks, thus increasing the aircraft operation time. Practical experiments performed to these algorithms showed the ability of these algorithms to achieve the desired goal. The developed algorithms will manage the maintenance scheduling problem to maximize the uptime of the air ambulance which can be achieved by maximizing the minimum life of spare parts. The developed algorithms showed good performance measures during experimental tests. The 3LSL algorithm showed a higher performance compared to other algorithms during all performed experiments.

Keywords

Acknowledgement

The authors would like to thank the Deanship of Scientific Research, University of Hafr Al-Batin for funding this work under Project Number No. G-114-2020.

References

  1. Allchin, F.R. and G. Erdosy, The archaeology of early historic South Asia: the emergence of cities and states. 1995: Cambridge University Press.
  2. Page, S.E., On the emergence of cities. Journal of Urban Economics, 1999. 45(1): p. 184-208. https://doi.org/10.1006/juec.1998.2091
  3. Ur, J., Households and the emergence of cities in ancient Mesopotamia. Cambridge Archaeological Journal, 2014. 24(2): p. 249-268. https://doi.org/10.1017/S095977431400047X
  4. Rosenstock, I.M., Why people use health services. The Milbank Quarterly, 2005. 83(4).
  5. Clim, A., et al., Health services in smart cities: Choosing the big data mining based decision support. International Journal of Healthcare Management, 2020. 13(1): p. 79-87. https://doi.org/10.1080/20479700.2019.1650478
  6. Albrecht, R., et al., Transport of COVID-19 and other highly contagious patients by helicopter and fixed-wing air ambulance: a narrative review and experience of the Swiss air rescue Rega. Scandinavian journal of trauma, resuscitation and emergency medicine, 2020. 28: p. 1-6. https://doi.org/10.1186/s13049-019-0696-1
  7. Roislien, J., et al., Exploring optimal air ambulance base locations in Norway using advanced mathematical modelling. Injury prevention, 2017. 23(1): p. 10-15. https://doi.org/10.1136/injuryprev-2016-041973
  8. Duchateau, F.-X., et al., Coronavirus Disease 2019: The Impact of Coronavirus Disease 2019 on the Use of Air Ambulances for International Medical Assistance. Air Medical Journal, 2021.
  9. Jemmali, M., Budgets balancing algorithms for the projects assignment. International Journal of Advanced Computer Science and Applications (IJACSA), 2019. 10(11): p. 574-578.
  10. Kaplan, L., D. Walsh, and R.E. Burney, Emergency aeromedical transport of patients with acute myocardial infarction. Annals of emergency medicine, 1987. 16(1): p. 55-57. https://doi.org/10.1016/S0196-0644(87)80287-1
  11. Rosenberg, B.L., et al., Aeromedical service: how does it actually contribute to the mission? Journal of Trauma and Acute Care Surgery, 2003. 54(4): p. 681-688. https://doi.org/10.1097/01.TA.0000054684.83538.45
  12. Delorenzo, A.J., et al., Characteristics of fixed wing air ambulance transports in Victoria, Australia. Air medical journal, 2017. 36(4): p. 173-178. https://doi.org/10.1016/j.amj.2017.02.008
  13. Anand, P., et al., Drone Ambulance Support System. International Journal of Engineering and Techniques, 2018. 4(2).
  14. Schwabe, D., et al., Long- Distance Aeromedical Transport of Patients with COVID-19 in Fixed-Wing Air Ambulance Using a Portable Isolation Unit: Opportunities, Limitations and Mitigation Strategies. Open Access Emergency Medicine: OAEM, 2020. 12: p. 411. https://doi.org/10.2147/OAEM.S277678
  15. Veldman, A., et al., Long-distance transport of ventilated patients: advantages and limitations of air medical repatriation on commercial airlines. Air medical journal, 2004. 23(2): p. 24-28. https://doi.org/10.1016/j.amj.2003.12.009
  16. Plant, J.R., D.B. MacLeod, and J. Kortbeek, Limitations of the prehospital index in identifying patients in need of a major trauma center. Annals of emergency medicine, 1995. 26(2): p. 133-137. https://doi.org/10.1016/S0196-0644(95)70142-7
  17. Bazargan, M., Airline operations and scheduling. 2016: Routledge.
  18. Jemmali, M., Projects Distribution Algorithms for Regional Development. 2021.
  19. Jemmali, M., Intelligent algorithms and complex system for a smart parking for vaccine delivery center of COVID-19. Complex & Intelligent Systems, 2021: p. 1-13.
  20. Jemmali, M., An optimal solution for the budgets assignment problem. RAIRO: Recherche Operationnelle, 2021. 55: p. 873. https://doi.org/10.1051/ro/2021043
  21. Alquhayz, H. and M. Jemmali, Max-Min Processors Scheduling. Information Technology and Control, 2021. 50(1): p. 5-12. https://doi.org/10.5755/j01.itc.50.1.25531
  22. Jemmali, M., Approximate solutions for the projects revenues assignment problem. Communications in Mathematics and Applications, 2019. 10(3): p. 653-658.
  23. al Fayez, F., L.K.B. Melhim, and M. Jemmali. Heuristics to Optimize the Reading of Railway Sensors Data. in 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). 2019. IEEE.
  24. Jemmali, M., L.K.B. Melhim, and M. Alharbi. Randomized-variants lower bounds for gas turbines aircraft engines. in World Congress on Global Optimization. 2019. Springer.
  25. Haouari, M. and M. Jemmali, Maximizing the minimum completion time on parallel machines. 4OR, 2008. 6(4): p. 375-392. https://doi.org/10.1007/s10288-007-0053-5
  26. Epstein, L. and R. van Stee, Maximizing the minimum load for selfish agents. Theoretical Computer Science, 2010. 411(1): p. 44-57. https://doi.org/10.1016/j.tcs.2009.08.032
  27. Jemmali, M., M.M. Otoom, and F. al Fayez. Max-Min Probabilistic Algorithms for Parallel Machines. in Proceedings of the 2020 International Conference on Industrial Engineering and Industrial Management. 2020.
  28. Alquhayz, H., M. Jemmali, and M.M. Otoom, Dispatching-rule variants algorithms for used spaces of storage supports. Discrete Dynamics in Nature and Society, 2020. 2020.
  29. Alharbi, M. and M. Jemmali, Algorithms for investment project distribution on regions. Computational Intelligence and Neuroscience, 2020. 2020.
  30. Jemmali, M., et al., Lower bounds for gas turbines aircraft engines. Communications in Mathematics and Applications, 2019. 10(3): p. 637-642.