Previous Articles     Next Articles

Stochastic Petri Net Based Modeling of Emergency Medical Rescue Processes During Earthquakes

SUN Huali · LIU Jiaguo · HAN Ziqiang · JIANG Juan   

  1. SUN Huali
    School of Management, Shanghai University, Shanghai 200444, China.
    LIU Jiaguo (Corresponding author)
    School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China.
    Email: liujiaguo@gmail.com.
    HAN Ziqiang
    School of Political Science and Public Administration, Shandong University, Qingdao 266237, China.
    JIANG Juan
    School of Management, Shanghai University, Shanghai 200444, China.
  • Online:2021-06-25 Published:2021-03-11

SUN Huali · LIU Jiaguo · HAN Ziqiang · JIANG Juan. Stochastic Petri Net Based Modeling of Emergency Medical Rescue Processes During Earthquakes[J]. Journal of Systems Science and Complexity, 2021, 34(3): 1063-1086.

The post-disaster emergency medical rescue (EMR) is critical for people’s lives. This paper presents a stochastic Petri net (SPN) model based on the process of the rescue structure and a Markov chain model (MC), which is applied to the optimization of the EMR process, with the aim of identifying the key activities of EMR. An isomorphic MC model is developed for measuring and evaluating the time performance of the EMR process during earthquakes with the data of the 2008 Wenchuan earthquake. This paper provides a mathematical approach to simulate the process and to evaluate the efficiency of EMR. Simultaneously, the expressions of the steady state probabilities of this system under various states are obtained based on the MC, and the variations of the probabilities are analyzed by changing the firing rates for every transition. Based on the concrete data of the event, the authors find the most time consuming and critical activities for EMR decisions. The model results show that the key activities can improve the efficiency of medical rescue, providing decision-makers with rescue strategies during the large scale earthquake.

No related articles found!
Viewed
Full text


Abstract