NEURAL NETWORK APPROACH FOR SOLVING THE TRAIN FORMATION PROBLEM
The train formation plan is one of the important elements of railroad system operations. Whereas mathematical programming formulations and algorithms are available for solving the train formation problem (TFP), the long CPU time required for convergence makes it difficult to solve the problems in a reasonably short time. At the same time, shorter decision intervals are becoming necessary, given the highly competitive operating climate of the railroad industry. A novel approach is presented for quickly obtaining good solutions to the TFP. A neural network model is developed for efficiently solving the TFP. Following a training process for neural network development, a testing process indicates that the neural network model will likely be both sufficiently fast and accurate in producing train formation plans under on-line conditions.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/0309061016
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Supplemental Notes:
- This paper appears in Transportation Research Record No. 1470, Railroad Research Issues. Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved
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Authors:
- Martinelli, David R
- Teng, Hualiang
- Publication Date: 1994
Language
- English
Media Info
- Features: Figures; References; Tables;
- Pagination: p. 38-46
- Monograph Title: Railroad research issues
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Serial:
- Transportation Research Record
- Issue Number: 1470
- Publisher: Transportation Research Board
- ISSN: 0361-1981
Subject/Index Terms
- TRT Terms: Accuracy; Computer models; Decision making; Emergency response time; Neural networks; Reaction time; Train makeup
- Old TRIS Terms: On line systems; Train formation problem
- Subject Areas: Freight Transportation; Highways; Planning and Forecasting; Railroads;
Filing Info
- Accession Number: 00677725
- Record Type: Publication
- ISBN: 0309061016
- Files: TRIS, TRB
- Created Date: May 22 1995 12:00AM