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

Dui, Hongyan, Liu, Kaixin, Wu, Shaomin (2023) Data-driven reliability and resilience measure of transportation systems considering disaster levels. Annals of Operations Research, . ISSN 0254-5330. (doi:10.1007/s10479-023-05301-w) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:100764)

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

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.

Item Type: Article
DOI/Identification number: 10.1007/s10479-023-05301-w
Uncontrolled keywords: Reliability, Resilience, Markov process, Importance measure, Repair analysis
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Funders: National Natural Science Foundation of China (https://ror.org/01h0zpd94)
Depositing User: Shaomin Wu
Date Deposited: 05 Apr 2023 13:06 UTC
Last Modified: 10 Jul 2023 15:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/100764 (The current URI for this page, for reference purposes)

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