Abstract
This paper presents a new approach to analyze the network structure in multi-commodity fixed charge network flow problems (MCFCNF). This methodology uses historical data produced from repeatedly solving the traditional MCFCNF mathematical model as input for the machine-learning framework. Further, we reshape the problem as a binary classification problem and employ machine-learning algorithms to predict network structure. This predicted network structure is further used as an initial solution for our mathematical model. The quality of the initial solution generated is judged on the basis of predictive accuracy, feasibility and reduction in solving time.
Library of Congress Subject Headings
Network analysis (Planning)--Data processing; Data mining; Machine learning
Publication Date
6-12-2015
Document Type
Thesis
Student Type
Graduate
Degree Name
Industrial and Systems Engineering (MS)
Department, Program, or Center
Industrial and Systems Engineering (KGCOE)
Advisor
Scott E. Grasman
Advisor/Committee Member
Ernest Fokoue
Recommended Citation
Ladage, Anurag A., "Hybrid Statistical Data Mining Framework for Multi-Commodity Fixed Charge Network Flow Problem" (2015). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/8715
Campus
RIT – Main Campus
Plan Codes
ISEE-MS
Comments
Physical copy available from RIT's Wallace Library at T57.85 .L34 2015