Resilience-based network design optimization under epistemic uncertainty

Abstract

Design optimization of infrastructure networks is a crucial task due to its huge impacts in socio-economic sectors and consideration of resilience in the design phase of a network ensures a higher level of performance even after the occurrence of any disruptive event. Due to the lack of knowledge regarding the effects of future disruptive events and the exact values of the resilience parameters, uncertainty consideration is essential for this type of optimization formulation. In this research, resilience-based network design optimization models are formulated with a view to minimizing design cost, maximizing the level of performance in the post-disrupted state, and maximizing resilience. The models are formulated for both deterministic and stochastic cases. The stochastic model, formulated in this study, is able to deal with epistemic uncertainty arising from interval data of the resilience parameters. The solution methodology generates the optimal network topology and the design capacities of each link present in the optimal solution. Finally, the formulations are solved to generate the optimal network that can satisfactorily perform even after multiple disruptive events.