Medical resource inventory model for emergency preparation with uncertain demand and stochastic occurrence time under considering different risk preferences at the airport

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Abstract

With the high accident rate of civil aviation, medical resource inventory becomes more important for emergency management at the airport. Meanwhile, medical products usually are time-sensitive and short lifetime. Moreover, we find that the optimal medical resource inventory depends on multiple factors such as different risk preferences, the material shelf life and so on. Thus, it becomes very complex in a real-life environment. According to this situation, we construct medical resource inventory decision model for emergency preparation at the airport. Our model is formulated in such a way as to simultaneously consider uncertain demand, stochastic occurrence time and different risk preferences. For solving this problem, a new programming is developed. Finally, a numerical example is presented to illustrate the proposed method. The results show that it is effective for determining the optimal medical resource inventory for emergency preparation with uncertain demand and stochastic occurrence time under considering different risk preferences at the airport.

Figures

  • Fig 1. Scenarios during a single stage of emergency preparedness.
  • Fig 2. The risk-averse optimal inventory levels when G(T) = 0.5.
  • Fig 3. The risk-averse optimal inventory levels when G(T) = 0.75.
  • Fig 4. The risk-taking optimal inventory levels when G(T) = 0.5.
  • Fig 5. The risk-taking optimal inventory levels when G(T) = 0.7.
  • Fig 6. The risk-taking optimal inventory levels when G(T) = 0.8.
  • Fig 7. Optimal expected cost when G(T) = 0.5 with different risk attitudes.
  • Fig 8. The sensitivity analysis of parameter for shortage cost s.

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CITATION STYLE

APA

Pan, W., Guo, Y., Jin, L., & Liao, S. J. (2017). Medical resource inventory model for emergency preparation with uncertain demand and stochastic occurrence time under considering different risk preferences at the airport. PLoS ONE, 12(9). https://doi.org/10.1371/journal.pone.0183472

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