MCDA-based Genetic Algorithms for Developing Disaster Resilient Designs of Critical Supply Networks

Abstract

Critical Infrastructures are infrastructures that provide vital or critical services for the population such as supply of electricity, drinking water, food, and health care. Interruptions of these services may have a direct or indirect adverse impact on the population's welfare. I.e. failures of critical services can lead to injuries or even losses of life, property damages, social and economic disruptions and thus drastically reduce the overall security of supply. We propose a new resilience measure that can be applied together with other known graph-related measures in order to achieve resilient, efficient, and economic feasible critical supply networks. In contrast to pure graph measures, the resilience measure we propose, takes spatial information of infrastructures and their criticalities w.r.t. to the availability of e.g. power or water into account. To enable a comprehensive evaluation of network topologies, a composite resilience metric is introduced, which uses these aforementioned measures as weighted building blocks. For evaluating critical network designs, the proposed methodology applies this composite resilience metric and Multi-Criteria Decision Analysis. Based on this, a genetic algorithm is proposed for identifying disaster resilient water supply networks.