• ISSN 0258-2724
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Volume 55 Issue 4
Jul.  2020
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Journal of Southwest Jiaotong University  > 2020  >  55(4): 882-888.
XU Chang’an, NI Shaoquan, CHEN Dingjun. Collaborative Optimization for Timetable and Maintenance Window Based on Two-Stage Algorithm[J]. Journal of Southwest Jiaotong University, 2020, 55(4): 882-888. doi: 10.3969/j.issn.0258-2724.20180577
Citation: XU Chang’an, NI Shaoquan, CHEN Dingjun. Collaborative Optimization for Timetable and Maintenance Window Based on Two-Stage Algorithm[J]. Journal of Southwest Jiaotong University, 2020, 55(4): 882-888. doi: 10.3969/j.issn.0258-2724.20180577
shu

Collaborative Optimization for Timetable and Maintenance Window Based on Two-Stage Algorithm

doi: 10.3969/j.issn.0258-2724.20180577
  • Received Date: 09 Jul 2018
  • Rev Recd Date: 14 Dec 2018
    • Available Online: 19 Dec 2018
  • Publish Date: 01 Aug 2020
    Abstract
  • There is mutual coupling between train timetable generation and maintenance window setting. To achieve the purpose of optimizing the train timetable structure and reasonably configuring the railway transportation capacity. According to the dynamic analysis of train timetable generation and maintenance window setting, the minimum impact of maintenance windows setting on train timetable planning is used as the objective function, and a mixed integer programming (MIP) model is built to realize the collaborative optimization of train timetable and maintenance window. To solve this complex problem, a two-stage solving algorithm including preliminary optimization and comprehensive optimization is designed. In the preliminary optimization stage, a heuristic algorithm based on experts’ experience is used to obtain the general framework of the train timetable. In the comprehensive optimization stage, the tabu search algorithm is used to obtain the global optimal solution. Finally, a case study based on Baoji−Chengdu railway line (Yangpingguan−Chengdu section) was conducted to verify the model. The results show that compared with the timetable compiled by human-computer interaction, the proposed method can effectively reduce the total residence time of all passenger and freight trains at stations by 6.19%, a total reduction of 1 355 min, of which the passenger trains and freight trains station residence time are decreased by 3.08% and 7.40%, with the total reduction time of 189 min and 1 166 min, respectively.

     

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