Risk control of the cascading failure of multimodal transport network considering uncertain disturbance factors1

Guo, Jingni; Xu, Jing; Liao, Wei

doi:10.3233/jifs-200968

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…e idea of this algorithm is to gradually improve the accuracy of weak classification through multi-level training. It mainly obtains sample subsets by changing the sample set distribution, and then the sample subsets are trained by the weak classification algorithm to generate n subclassifiers [22].…”

Section: Classification Algorithm Of Machine Learningmentioning

confidence: 99%

Application of Machine Learning in Enterprise Risk Management

Jia

2022

Security and Communication Networks

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With the development of China’s economic globalization, more and more enterprises have realized the importance of risk management. As a new technology, machine learning has injected new vitality into enterprise risk management. This paper analyzes the factors of enterprise risk from the perspective of risk management, explores and analyzes the application of machine learning in enterprise risk management, and points out the direction for scientific enterprise risk management. This paper first establishes a set of credit evaluation factors and then builds a model based on this evaluation factor set, which ensures the accuracy of the evaluation and also adds pertinence. With the increase of the number of samples, the accuracy of the prediction results also increases. Also, basically, with the increase of training samples and realistic samples, the accuracy rate continues to increase. In the data of the experimental results, it is shown in the process of increasing the number of samples from 400 to 600. The accuracy of the traditional method is 0.81 and 0.8316, respectively. The accuracy of the improved method is 0.842 and 0.905, respectively. This paper studies the process of enterprise risk assessment from the perspective of risk management and attempts to build an enterprise risk management model based on industry risk coefficients, which have certain practical significance.

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Section: Classification Algorithm Of Machine Learningmentioning

confidence: 99%

Application of Machine Learning in Enterprise Risk Management

Jia

2022

Security and Communication Networks

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“…e single network was widely studied for a long time, and scholars have obtained many valuable research ndings. But this kind of isolated network cannot describe the relationship between multiple systems [2]. However, in practice, many systems interact and relate with each other [3,4].…”

Section: Introductionmentioning

confidence: 99%

Study on Invulnerability of Material Emergency Transportation System Based on Three-Layer Interdependent Network

Feng

Lin

Zhang

et al. 2022

Mathematical Problems in Engineering

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The material transportation capacity under emergency conditions is an important guarantee for the country to deal with war, epidemic outbreak, and other crisis situations. Under emergency conditions, some nodes of the material transportation system may fail to work normally, which may lead to the collapse of the whole system. Based on analyzing the characteristics of the material emergency transportation system, this article builds a three-layer interdependent network model and uses the improved M-L model to describe the failure propagation mechanism of node damage in the three-layer network. Then, the network model is attacked randomly, and the relationship between invulnerability of the three-layer network and the three main indexes of network flow, average degree, and probability of interdependence are studied. Afterwards, the propagation of cascading failure among three subnetworks in the interdependent network is analyzed and compared. This article provides a theoretical basis for building an efficient and robust material emergency transportation system.

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“…Multimodal transport combines waterway transport, highway transport, and railway transport, and selects the appropriate transport mode for the goods at different intermediate points in the whole logistic transport process, improving transport efficiency and reducing the cost. Based on the road impedance function of the public road bureau, Guo et al (2020) improved the conditional value-at-risk, established a nonlinear programming model with generalized travel time as the objective function, and used the cellular ant colony algorithm to solve the model. The results of the empirical analysis verified the applicability of the proposed load redistribution method to such areas and the effectiveness of the algorithm.…”

mentioning

confidence: 99%

Rail Consignment Path Planning Based on Multimodal Transport: Considering the Time Uncertainty Condition

2023

EPPM-Journal

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Multimodal transport has emerged and applied to logistics work as single-mode transport was unable to meet the growing demand for logistics. Due to the difference in cost and time consumption between different modes of transportation, the path planning of multimodal transport is different from that of single-mode transport. This paper firstly established a multimodal railroad consignment path optimization model under the condition of transportation time uncertainty and then optimized the path and transportation mode with the particle swarm optimization (PSO) algorithm according to the path optimization model. In addition, the PSO algorithm was optimized by a genetic algorithm to avoid the optimization process of the PSO algorithm from falling into the locally optimal solution. Finally, simulation experiments were carried out on the improved PSO algorithm, and it was compared with the traditional PSO and genetic algorithms. The results showed that the improved PSO algorithm converged to stability after about 200 iterations, the traditional PSO algorithm converged to stability after about 240 iterations, and the genetic algorithm converged to stability after about 400 iterations; the total transport cost and time of railway transportation were USD 114,300 and 52.5 h; the total transport cost and time of the path obtained by the genetic algorithm were USD 60,400 and 35.6 h; the total transport cost and time of the path obtained by the traditional PSO algorithm were USD 37700 and 24.3 h; the total transport cost and time of the path obtained by the improved PSO algorithm were USD 3,1900 and 23.4 h.

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Risk control of the cascading failure of multimodal transport network considering uncertain disturbance factors1

Cited by 4 publications

References 11 publications

Application of Machine Learning in Enterprise Risk Management

Application of Machine Learning in Enterprise Risk Management

Study on Invulnerability of Material Emergency Transportation System Based on Three-Layer Interdependent Network

Rail Consignment Path Planning Based on Multimodal Transport: Considering the Time Uncertainty Condition

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