# Optimizing Resilience in Large Scale Networks

Xiaojian Wu, Daniel Sheldon, and Shlomo Zilberstein.
Optimizing Resilience in Large Scale Networks.
*Proceedings of the Thirtieth Conference on Artificial
Intelligence* (AAAI), Phoenix, Arizona, 2016.

## Abstract

We propose a decision making framework to optimize the resilience of road networks to natural disasters such as floods. Our model generalizes an existing one for this problem by allowing roads with a broad class of stochastic delay models. We then present a fast algorithm based on the sample average approximation (SAA) method and network design techniques to solve this problem approximately. On a small existing benchmark, our algorithm produces near-optimal solutions and the SAA method converges quickly with a small number of samples.We then apply our algorithm to a large real-world problem to optimize the resilience of a road network to failures of stream crossing structures to minimize travel times of emergency medical service vehicles. On medium-sized networks, our algorithm obtains solutions of comparable quality to a greedy baseline method but is 30–60 times faster. Our algorithm is the only existing algorithm that can scale to the full network, which has many thousands of edges.

### Bibtex entry:

@inproceedings{WSZaaai16, author = {Xiaojian Wu and Daniel Sheldon and Shlomo Zilberstein}, title = {Optimizing Resilience in Large Scale Networks}, booktitle = {Proceedings of the Thirtieth Conference on Artificial Intelligence}, year = {2016}, pages = { }, address = {Phoenix, Arizona}, url = {http://rbr.cs.umass.edu/shlomo/papers/WSZaaai16.html} }shlomo@cs.umass.edu