Graph Evaluation and Review Technique for Emergency Logistics Distribution in Complex Environment

摘要

Emergency logistics is one of the important measures to solve the unconventional sudden disaster. The research on the distribution design of emergency logistics project has an important supporting role for the whole emergency rescue system. Therefore, it is necessary to analyze and plan the emergency logistics project scientifically in order to better serve the emergency rescue system. In view of the defect that the current emergency logistics model cannot completely describe the whole emergency logistics process, this paper proposes a new emergency logistics distribution model by using GERT random network. From the perspective of system, this model fully considers the possible situation in the process of emergency material transportation and puts forward the corresponding countermeasures, so as to improve the transportation efficiency of rescue materials in the actual disaster relief activities to speed up disaster relief. Based on the properties of moment generating function and the calculation method of Mason formula, this paper fully considers the success probability, risk probability, delivery time, and other factors of the distribution route and puts forward the transshipment scheme of each logistics site to the disaster sites. After that, the effectiveness of GERT stochastic network in the process of emergency distribution is verified by an example of a logistics distribution route, which provides decision-making basis for relevant departments. Compared with the literature, this paper uses GERTnetwork model to intuitively and clearly express the overall process of emergency material distribution, breaks through the thinking mode of simply choosing the distribution path in the distribution process, combines with the dynamic changes of the distribution environment, and comprehensively considers the timeliness and traffic capacity of emergency material distribution in sudden disasters, so as to provide reference for emergency management in complex and random sit