Joint pre- and post-disaster planning to enhance the resilience of critical infrastructures. (March 2023)

• Record Type: Journal Article
• Authors: Jia, Chuanzhou; Zhang, Chi; Li, Yan-Fu; Li, Quan-Lin
• Is Part Of: Reliability engineering & system safety. Volume 231(2023)
• Publication Date: 2023-03
• Subjects: Disaster management -- Resilience -- Joint optimization -- Geographical distribution -- Lagrangian decomposition; 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.2022.109023
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Propose a two-stage stochastic optimization model. Jointly optimize pre- and post-disaster planning for restoration tasks. Explicitly consider two layers of interdependent infrastructures. Propose an efficient Lagrangian Relaxation algorithm. Abstract: Pre-disaster planning and post-disaster scheduling are common strategies to increase the resilience of critical infrastructure. In this paper, we propose a new two-stage stochastic optimization model to simultaneously determine the locations for building stations of restoration teams before disasters and their routing of conducting restoration tasks after disasters. Two layers of interdependent infrastructures are explicitly considered in the proposed model: The first layer for the transmission of flows to satisfy customers' demands (e.g., a power transmission or telecommunication network) and the second layer for the transportation of restoration crews (i.e., the transportation nework). We seek to minimize the total cost, including the station building cost and the penalty cost incurred by performance loss. To deal with the computational complexity of the proposed problem, we propose a Column-generation-based Lagrangian Relaxation algorithm with a dynamic programming algorithm embedded to solve the included pricing problem. Extensive computational experiments, including the real-world infrastructures (i.e., French natural gas network), are conducted to illustrate the merits of the proposed approach.