@article{Nicholson2016260,
title = "Optimal network flow: A predictive analytics perspective on the fixed-charge network flow problem",
journal = "Computers & Industrial Engineering",
volume = "99",
pages = "260 - 268",
year = "2016",
issn = "0360-8352",
doi = "http://dx.doi.org/10.1016/j.cie.2016.07.030",
url = "http://www.sciencedirect.com/science/article/pii/S0360835216302650",
author = "C. D. Nicholson and W. Zhang",
keywords = "Network analysis",
keywords = "Fixed charge network flow",
keywords = "Predictive modeling",
keywords = "Critical components ",
abstract = "The fixed charge network flow (FCNF) problem is a classical NP-hard combinatorial problem with wide spread applications. To the best of our knowledge, this is the first paper that employs a statistical learning technique to analyze and quantify the effect of various network characteristics relating to the optimal solution of the \{FCNF\} problem. In particular, we create a probabilistic classifier based on 18 network related variables to produce a quantitative measure that an arc in the network will have a non-zero flow in an optimal solution. The predictive model achieves 85% cross-validated accuracy. An application employing the predictive model is presented from the perspective of identifying critical network components based on the likelihood of an arc being used in an optimal solution. "
}