Mostra el registre d'ítem simple
A PSO-GRNN model for railway freight volume prediction: empirical study from China
dc.contributor.author | Sun, Yan |
dc.contributor.author | Lang, Maoxiang |
dc.contributor.author | Wang, Danzhu |
dc.contributor.author | Liu, Linyun |
dc.date.accessioned | 2014-05-28T15:15:46Z |
dc.date.available | 2014-05-28T15:15:46Z |
dc.date.issued | 2014-05 |
dc.identifier.citation | Sun, Yan [et al.]. A PSO-GRNN model for railway freight volume prediction: empirical study from China. "Journal of Industrial Engineering and Management", Maig 2014, vol. 7, núm. 2, p. 413-433. |
dc.identifier.issn | 2013-0953 |
dc.identifier.uri | http://hdl.handle.net/2099/14705 |
dc.description.abstract | Purpose: The purpose of this paper is to propose a mathematical model for the prediction of railway freight volume, and therefore provide railway freight resource allocation with an accurate direction. With an accurate railway freight volume prediction, railway freight enterprises can integrate the limited resources and organize transport more reasonably. Design/methodology/approach: In this paper, a PSO-GRNN model is proposed to predict the railway freight volume. In this model, GRNN is applied to carry out the nonlinear regression analysis and output the prediction value, PSO algorithm is applied to optimize the GRNN model by searching the best smoothing parameter. In order to improve the performance of PSO algorithm, time linear decreasing inertia weight algorithm and time varying acceleration coefficient algorithm are applied in the paper. Originality/value: A railway freight volume prediction index system containing seventeen indexes from five aspects is established in this paper. And PSO-GRNN model constructed in this paper are applied to predict the railway freight volume from 2007 to 2011. Finally, an empirical study is given to verify the feasibility and accuracy of the PSO-GRNN model by comparing with RBFNN model and BPNN model. The result shows that PSO-GRNN model has a good performance in reducing the prediction error, and can be applied in actual production easily |
dc.format.extent | 21 p. |
dc.language.iso | eng |
dc.publisher | OmniaScience |
dc.rights | Attribution-NonCommercial 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Economia i organització d'empreses::Direcció d'operacions::Modelització de transports i logística |
dc.subject.lcsh | Business logistics -- Mathematical models |
dc.subject.lcsh | Freight and freightage -- Mathematical models |
dc.subject.lcsh | Railroads -- China |
dc.subject.other | Railway freight volume |
dc.subject.other | Prediction model |
dc.subject.other | Neural network |
dc.subject.other | GRNN |
dc.subject.other | Smoothing parameter |
dc.subject.other | PSO algorithm |
dc.title | A PSO-GRNN model for railway freight volume prediction: empirical study from China |
dc.type | Article |
dc.subject.lemac | Logística (Indústria) -- Models matemàtics |
dc.subject.lemac | Transport de mercaderies -- Models matemàtics |
dc.subject.lemac | Ferrocarrils -- China |
dc.identifier.dl | B-28744-2008 |
dc.description.peerreviewed | Peer Reviewed |
dc.rights.access | Open Access |
local.citation.author | Sun, Yan; Lang, Maoxiang; Wang, Danzhu; Liu, Linyun |
local.citation.publicationName | Journal of Industrial Engineering and Management |
local.citation.volume | 7 |
local.citation.number | 2 |
local.citation.startingPage | 413 |
local.citation.endingPage | 433 |
Fitxers d'aquest items
Aquest ítem apareix a les col·leccions següents
-
2014, vol. 7, núm. 2 [15]
Special Issue – LISS 2013