Robust Optimization for Tree-Structured Stochastic Network Design
  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.
