Initially, the use of network-centric approaches in process management appeared in the military sphere, and then spread to public administration and other sectors of the economy, including healthcare. The introduction of network-centric approaches in the interaction of emergency response processes places additional requirements on interoperability at the organizational, semantic and technical levels. Therefore, the optimization of interaction between participants in the elimination of the consequences of an emergency situation remains an urgent problem. The paper presents a stochastic model of emergency response, analyzes various states of the model, and provides recommendations for improving the effectiveness of both the process itself as a whole and information interaction between participants in the process. Recommendations are presented on the key parameters of the stochastic model of emergency response to support decision-making in conditions of limited time and resources.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Application of Stochastic Petri Nets in a Network-Centric Approach to the Organization of the Emergency Response Process

  • Viktor A. Drogovoz

摘要

Initially, the use of network-centric approaches in process management appeared in the military sphere, and then spread to public administration and other sectors of the economy, including healthcare. The introduction of network-centric approaches in the interaction of emergency response processes places additional requirements on interoperability at the organizational, semantic and technical levels. Therefore, the optimization of interaction between participants in the elimination of the consequences of an emergency situation remains an urgent problem. The paper presents a stochastic model of emergency response, analyzes various states of the model, and provides recommendations for improving the effectiveness of both the process itself as a whole and information interaction between participants in the process. Recommendations are presented on the key parameters of the stochastic model of emergency response to support decision-making in conditions of limited time and resources.