Resilience in the design of critical infrastructure: applications in power grid and logistic systems

• Autor(a) principal: DINIZ, Helder Henrique Lima
• Data de Publicação: 2017
• Tipo de documento: Tese
• Idioma: eng
• Título da fonte: Repositório Institucional da UFPE
• dARK ID: ark:/64986/0013000004v73
• Texto Completo: https://repositorio.ufpe.br/handle/123456789/29576
• topic: Engenharia de Produção; Resiliência; Projeto; Decisões pré-evento; Infraestrutura crítica; Fornecimento de energia; Logística

Due to the great need to meet demand and remain competitive, companies are seeking to become more resilient, and thus systems must withstand, adapt to, and rapidly recover from the effects of undesired events. Resilience can be considered as the capacity of an entity to recover from a disruption, involving the ability to reduce effectively both magnitude and duration of the deviation from the nominal performance. This thesis proposes an optimization model, using Mixed-Integer Linear Programming (MILP), to support decisions related to making investments in the design of infrastructure critical systems that experience interruptions in supplying their customer demands due to disruptive events. In this approach, by considering the probabilities of the occurrence of a set of such disruptive events, the model minimizes the overall expected costs by determining an optimal strategy involving pre- and post-event actions. The pre-event actions, which are considered during the initial design phase, take into account the resilience capacity (absorption, adaptation and restoration). Although, according to the literature, pre-event resilience actions are faster in recovering the system, more useful and more cost-effective, especially when they are implemented during system design, most of research papers about resilience have focused on post-event policies. Therefore, in this work, in addition to post-event recovery actions, we corroborate with literature and consider pre-event actions so as to reduce recovery costs and increase recovery speed. The optimization model is thus developed and applied in two contexts: power grids serving industrial clients and a logistics distribution network. The results demonstrate that higher investments during the design phase, when optimally allocated, have the potential to improve infrastructure performance and still reduce overall costs.