Stochastic Network Design for River Networks

Xiaojian Wu, Daniel Sheldon, and Shlomo Zilberstein. Stochastic Network Design for River Networks. NIPS Workshop on Machine Learning for Sustainability, Lake Tahoe, Nevada, 2013.

Abstract

Stochastic network design techniques can be used effectively to solve a wide range of planning problems in ecological sustainability. We propose a novel approximate algorithm based on the sample average approximation (SAA) and mixed integer programming (MIP) to efficiently address the problem of using a limited budget to remove instream barriers, which prevent fish from accessing their natural habitat. In comparison with a dynamic programming (DP) benchmark algorithm, the advantage of our algorithm is the ability to produce a near optimal solution much faster, particularly when the budget is large and the DP based algorithm becomes intractable. Furthermore, while the DP based algorithm can only solve tree-structured stream networks, our algorithm is applicable to networks with a more general directed acyclic graph structure.

Bibtex entry:

@inproceedings{WSZnips13ws,
  author	= {Xiaojian Wu and Daniel Sheldon and Shlomo Zilberstein},
  title		= {Stochastic Network Design for River Networks},
  booktitle     = {NIPS Workshop on Machine Learning for Sustainability},
  year		= {2013},
  pages		= { },
  address       = {Lake Tahoe, Nevada},
  url		= {http://rbr.cs.umass.edu/shlomo/papers/WSZnips13ws.html}
}

shlomo@cs.umass.edu