Intelligent approach to vehicle routes planning base on artificial neural networks prediction model
Authors:
- Daniel Kubek,
- Paweł Więcek
Abstract
The aim of the article is to present an approach to robust optimization of vehicles routes within the urban area, based on the driving times short-term predictions in the selected area of the urban road network. The forecasted values of the travel times were determined by the use of artificial neural network prediction model, taking into account the certain degree of forecast uncertainty, expressed by a given prediction error. The effectiveness of the proposed in the work solution has been verified on the basis of the results obtained by the classic optimization process wherein the optimization parameters are certain and accurate. The received simulation results indicate that use of forecasting techniques with robust optimization models has a positive impact on the quality of final solutions.
- Record ID
- CUT513cba1d85ad4c68a5888233a7381e0b
- Publication categories
- ; ;
- Author
- Pages
- 232-245
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 243-245; Bibliografia (liczba pozycji) - 40; Oznaczenie streszczenia - Abstr.
- Substantive notes
- Dostęp po zalogowaniu
- Book
- Valenzuela Olga, Olga Valenzuela Rojas Fernando, Fernando Rojas Pomares Héctor Héctor Pomares [et al.] (eds.): ITISE 2017 : International Work-Conference on Time Series, Granada, September, 18-20 2017 : proceedings. Vol. 1, 2017, [S.l.], Godel Impresiones Digitales, ISBN 978-84-17293-01-7
- Keywords in English
- short-term forecasting, artificial neural networks, robust vehicle routing problem with time windows
- URL
- http://itise.ugr.es/ Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 5
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT513cba1d85ad4c68a5888233a7381e0b/
- URN
urn:pkr-prod:CUT513cba1d85ad4c68a5888233a7381e0b
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.