Ricerc@Sapienza

The pandemic generated by Covid-19 caused social and economic consequences that constituted a global challenge for all countries. Italy was one of the first nations to be affected by the pandemic, especially in the heart of its production system and in the most densely populated area: The Lombardy Region. Starting in February 2020, there was a progressive slowdown until a total lockdown that paralyzed almost all social and economic activities, until a partial resumption of normal activities in May and a further increase in mid-June.

This paper addresses progressive network recovery under uncertain knowledge of damages. We formulate the problem as a mixed integer linear programming (MILP), and show that it is NP-Hard. We propose an iterative stochastic recovery algorithm (ISR) to recover the network in a progressive manner to satisfy the critical services. At each optimization step, we make a decision to repair a part of the network and gather more information iteratively, until critical services are completely restored.

After a massive scale failure, the assessment of damages to communication networks requires local interventions and remote monitoring. While previous works on network recovery require complete knowledge of damage extent, we address the problem of damage assessment and critical service restoration in a joint manner. We propose a polynomial algorithm called Centrality based Damage Assessment and Recovery (CeDAR) which performs a joint activity of failure monitoring and restoration of network components.