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Data-driven reliability and resilience measure of transportation systems considering disaster levels

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Abstract

With the development of economic globalization and increasing international trade, the maritime transportation system (MTS) is becoming more and more complex. A failure of any supply line in the MTS can seriously affect the operation of the system. Resilience describes the ability of a system to withstand or recover from a disaster and is therefore an important method of disaster management in MTS. This paper analyzes the impact of disasters on MTS, using the data of Suez Canal "Century of Congestion" as an example. In practice, the severity of a disaster is dynamic. This paper categorizes disasters into different levels, which are then modelled by the Markov chain. The concept of a repair line set is proposed and is determined with the aim to minimize the total loss and maximize the resilience increment of the line to the system. The resilience measure of MTS is defined to determine the repair line sequence in the repair line set. Finally, a maritime transportation system network from the Far East to the Mediterranean Sea is used to validate the applicability of the proposed method.

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Notes

  1. Insurance Business Australia: https://www.insurancebusinessmag.com/au/news/marine/was-this-massive-suez-canal-ship-damages-claim-caused-by-a-drone-419988.aspx.

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Funding

This study was funded by the National Natural Science Foundation of China (Nos. 72071182, U1904211), the Key Science and Technology Program of Henan Province (No. 222102520019), the Program for Science & Technology Innovation Talents in Universities of Henan Province (No. 22HASTIT022), and the Program for young backbone teachers in Universities of Henan Province (No. 2021GGJS007).

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Contributions

Hongyan Dui and Shaomin Wu proposed the idea of this paper; Hongyan Dui and Kaixin Liu performed the experiments and analyzed the data; Hongyan Dui and Shaomin Wu revised the methodology and model; All authors have contributed to the writing, editing and proofreading of this paper.

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Correspondence to Hongyan Dui.

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The authors report no conflict of interest and have received no payment in preparation of this manuscript.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Dui, H., Liu, K. & Wu, S. Data-driven reliability and resilience measure of transportation systems considering disaster levels. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05301-w

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  • DOI: https://doi.org/10.1007/s10479-023-05301-w

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