Defense resource allocation in road dangerous goods transportation network: A Self-Contained Girvan-Newman Algorithm and Mean Variance Model combined approach

Wencheng Huang, Linqing Li, Hongyi Liu, Rui Zhang and Minhao Xu

Reliability Engineering and System Safety, 2021, vol. 215, issue C

Abstract: A Self-Contained Girvan-Newman Algorithm and Mean Variance Model combined approach is proposed to allocate the defense resource in road dangerous goods transportation network. Firstly, the weighted physical network without direction and its weighted service network with direction are established. The Self-Contained Girvan-Newman Algorithm is applied to separate the whole weighted physical network into several communities. Next, based on the service network, the covariance matrix of each separated community is established, the Mean Variance Model is used to allocate the defense resource for each community, which focuses on selecting the option with the lowest probability of loss caused by the dangerous goods transportation accident/risk. The case study is conducted by using the road network and the dangerous goods transportation volume of Dalian, China as the background. When the whole network is separated into 6 communities, the defense resource capability in the whole network is the best. The Power Function Allocation Model (PFAM) is applied as the comparison approach, the overall rescue capability of the network is defined and applied to evaluate the defense resource allocation schemes. The results show that, the approach proposed in this paper has better effectiveness than PFAM, especially when the whole network is separated into 6 communities.

Keywords: Defense resource allocation; Road dangerous goods transportation network; Community partition; Self-Contained Girvan-Newman Algorithm; Mean Variance Model (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021004178

DOI: 10.1016/j.ress.2021.107899

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