Abstract
Risk analysis is an important fundamental basis of the decision-making process, and it has been applied in many fields. In order to improve the risk management of logistic, a new model based on particle swarm optimization (PSO) is proposed, which is a stochastic optimization method based on population. Through a comparison of performance with a Fuzzy Comprehensive Measurement Method (FCMM), the findings indicated that PSO can predict the logistic risk more accurately. The experimental results show that the model of logistic risk analysis and identification based on PSO algorithm is superior to FCMM model.
Highlights
With the promoting of the national logistic industry, the planning and establishing of logistic construction projects have incurred an upsurge in major cities of China [1]
It is shown that model of risk analysis and identification of logistic risk based on particle swarm optimization (PSO) algorithm is superior to Fuzzy Comprehensive Measurement Method (FCMM) model
Based on the theory of particle swarm optimization and Fuzzy Comprehensive Measurement Method, we went through these two different models, and compared their differences and advantages
Summary
With the promoting of the national logistic industry, the planning and establishing of logistic construction projects have incurred an upsurge in major cities of China [1]. Min et al analyzed the fuzzy comprehensive evaluation method applied to the virtual logistic organization risk assessment in the middle [11].
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