Post-disaster recovery in industrial sectors: A Markov process analysis of multiple lifeline disruptions. (February 2021)

• Record Type: Journal Article
• Authors: Liu, Huan; Tatano, Hirokazu; Pflug, Georg; Hochrainer-Stigler, Stefan
• Is Part Of: Reliability engineering & system safety. Volume 206(2021)
• Publication Date: 2021-02
• Subjects: Lifeline disruption -- Recovery -- Markov process -- Economic impacts analysis -- 2011 Great East Japan earthquake; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2020.107299
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Focused on the short-term, firm-level business scale, indirect economic impact of lifeline service disruptions. Markov process model is applied in the field of post-disaster business recovery from a microeconomic perspective that can capture the uncertainty and randomness of the stochastic recovery process. Integrated the recovery of multiple lifeline services and how their recovery impact on firm's production capacity. The proposed model was applied to a real post-disaster business recovery case study. Abstract: Lifeline disruptions can represent a serious economic threat at various scales. However, firm-level empirical evidence regarding the consequences and reliability of lifeline services, and how lifeline service disruptions impact on economic losses after disasters, is still lacking. This study applies a temporal, non-homogeneous, Markov process approach that explicitly considers multiple lifeline service disruptions. This approach is used to quantitatively estimate the impacts of multiple lifeline disruptions on business production capacity losses. The model is illustrated based on the 2011 Great East Japan Earthquake case study with detailed information obtained from individual business recovery data. The result indicates that the restoration of lifeline systems during disruptions should consider each business service given that it significantly affects business production capacity recovery. For example, the case study results indicate that if electrical power, water and gas are disrupted, restoration of electrical power supply yields the highest influence on production capacity improvement, amounting to 15% per day on average, compared with 10% per day for gas restorations, and 8% per day for water restoration. The proposed model can provide information to business managers and policymakers on the optimum recovery strategy and on how to mitigate economic losses in disruption event cases.