.png){kind=icon}
.png){kind=icon}
Return
Optimal network flow: A predictive analytics perspective on the fixed-charge network flow problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cie.2016.07.030.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Network analysis
Fixed charge network flow
Predictive modeling
Critical components
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
1.0W
Citations:
3.8W