# Robust Optimization for Tree-Structured Stochastic Network Design

arXiv: https://arxiv.org/abs/1612.00104 · HTML: https://mlsanity.com/paper/1612.00104/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2016.

## Built on by (most cited first, 1 of 1)

- [Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration With Provable Guarantees](https://mlsanity.com/paper/2309.08883/) (2023)

## Abstract (arXiv)

Stochastic network design is a general framework for optimizing network connectivity. It has several applications in computational sustainability including spatial conservation planning, pre-disaster network preparation, and river network optimization. A common assumption in previous work has been made that network parameters (e.g., probability of species colonization) are precisely known, which is unrealistic in real- world settings. We therefore address the robust river network design problem where the goal is to optimize river connectivity for fish movement by removing barriers. We assume that fish passability probabilities are known only imprecisely, but are within some interval bounds. We then develop a planning approach that computes the policies with either high robust ratio or low regret. Empirically, our approach scales well to large river networks. We also provide insights into the solutions generated by our robust approach, which has significantly higher robust ratio than the baseline solution with mean parameter estimates.