4.6 Article

Development of Multi-Group Non-dominated Sorting Genetic Algorithm for identifying critical post-disaster scenarios of lifeline networks

出版社

ELSEVIER
DOI: 10.1016/j.ijdrr.2019.101299

关键词

Genetic algorithm; Post-disaster scenario; Lifeline network; Multi-objectives; Disaster risk reduction

资金

  1. Ministry of Land, Infrastructure and Transport (MOLIT) of the Korean Government [19SCIP-B119960-04]
  2. Institute of Construction and Environmental Engineering at Seoul National University

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Identifying critical post-disaster scenarios in terms of their socioeconomic consequences is of the utmost importance for disaster risk management of infrastructure networks. Recently, multi-objective genetic algorithms have been employed to find such critical post-disaster scenarios for complex systems. However, a large size of a network may hamper multi-objective genetic algorithm from obtaining final solutions that are accurate and robust against variability. To overcome this challenge, a Multi-Group Non-dominated Sorting Genetic algorithm (MG-NSGA) is proposed in this study. It is presented in the paper that MG-NSGA can effectively identify critical post-disaster scenarios by improving the diversity of sample populations. Furthermore, a concept of 'critical zone' in the solution space is proposed to determine a group of important post-disaster scenarios identified by MG-NSGA. By large-size real infrastructure network examples of EMA highway network and Jeju transportation network, the proposed MG-NSGA-based identification method is successfully demonstrated.

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