Emergence of Antifragility by Optimum Postdisruption Restoration Planning of Infrastructure Networks

Abstract

A system is antifragile if its performance improves as the result of exposure to stressors, shocks, or disruptions. This behavior is typical of complex systems and is not usually exhibited by engineered technical systems. In fact, technical systems can display antifragility when new investments are allocated, e.g., after disasters. This study proposes an optimization model for the postdisaster restoration planning of infrastructure networks, taking into account the possibility of combining the construction of new components and the repair of failed ones. The strategic goal is to determine the optimal target system structure so that the performance of the target system is maximized under the constraints of investment cost and network connectivity. The problem is formulated as mixed-integer binary linear programming (MILP), and an efficient Benders decomposition algorithm is devised to cope with the computational complexity of its solution. The proposed approach is tested on a realistic infrastructure network: the 380-kV power transmission grid of northern Italy. The results show that the restored network can achieve an improved functionality as compared to the original network if new components are constructed and some failed components are not repaired, even when the former is much more expensive than the latter. Therefore, antifragility provides an opportunity for the system to meet future service demand increases and a perspective under which disruptions can be seen as chances for system performance improvements.