Ant Colony Optimization approach for solving rolling stock planning for passenger trains

Yasutaka Tsuji, Masahiro Kuroda, Yukiya Kitagawa, Yoshitaka Imoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

Railway rolling stock planning is a basic scheduling in railway transport, which assigns physical train units to given time table services and determines a roster of the train units. This planning also involves a scheduling of periodical inspection for the train units. We have proposed an Ant Colony Optimization (ACO) based approach to solve this planning problem. In this paper, local search methods are introduced to enhance the proposed ACO's performance for tackling a large-scale problem. The effectiveness of the enhanced ACO is demonstrated through numerical experiments with instance problems made from real railway lines.

Original languageEnglish
Title of host publication2012 IEEE/SICE International Symposium on System Integration, SII 2012
Pages716-721
Number of pages6
DOIs
Publication statusPublished - 2012
Event2012 IEEE/SICE International Symposium on System Integration, SII 2012 - Fukuoka, Japan
Duration: Dec 16 2012Dec 18 2012

Other

Other2012 IEEE/SICE International Symposium on System Integration, SII 2012
CountryJapan
CityFukuoka
Period12/16/1212/18/12

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All Science Journal Classification (ASJC) codes

  • Information Systems

Cite this

Tsuji, Y., Kuroda, M., Kitagawa, Y., & Imoto, Y. (2012). Ant Colony Optimization approach for solving rolling stock planning for passenger trains. In 2012 IEEE/SICE International Symposium on System Integration, SII 2012 (pp. 716-721). [6427319] https://doi.org/10.1109/SII.2012.6427319