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Title: NEURAL NETWORK APPROACH FOR SOLVING THE TRAIN FORMATION PROBLEM
Accession Number: 00677725
Record Type: Component
Availability: Find a library where document is available Abstract: 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.
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
Monograph Title: Monograph Accession #: 01401292
Language: English
Authors: Martinelli, David RTeng, HualiangPagination: p. 38-46
Publication Date: 1994
Serial: ISBN: 0309061016
Features: Figures
(8)
; References
(9)
; Tables
(5)
TRT Terms: Old TRIS Terms: Subject Areas: Freight Transportation; Highways; Planning and Forecasting; Railroads
Files: TRIS, TRB
Created Date: May 22 1995 12:00AM
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