• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 56 Issue 2
Apr.  2021
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Article Contents
ZHONG Qingwei, ZHANG Yongxiang, WANG Dian, YIN Yong, YAN Xu, PENG Qiyuan. Optimization Model and Algorithm for Train-Set Scheduling Based on Trip Sequence[J]. Journal of Southwest Jiaotong University, 2021, 56(2): 385-394. doi: 10.3969/j.issn.0258-2724.20191140
Citation: ZHONG Qingwei, ZHANG Yongxiang, WANG Dian, YIN Yong, YAN Xu, PENG Qiyuan. Optimization Model and Algorithm for Train-Set Scheduling Based on Trip Sequence[J]. Journal of Southwest Jiaotong University, 2021, 56(2): 385-394. doi: 10.3969/j.issn.0258-2724.20191140

Optimization Model and Algorithm for Train-Set Scheduling Based on Trip Sequence

doi: 10.3969/j.issn.0258-2724.20191140
  • Received Date: 11 Dec 2019
  • Rev Recd Date: 01 Mar 2020
  • Available Online: 09 Mar 2021
  • Publish Date: 15 Apr 2021
  • Usually, the train-set schedule involves several practical aspects, such as operational safety, efficiency, and cost. The quality and efficiency of the schedule have a significant impact on the operation of high-speed railways. In order to quickly obtain a high-quality train-set schedule, a mixed-integer linear programming (MILP) model that takes composition changes on the basis of the trip sequences was established with the optimization goals of reducing operating costs and total deadhead mileages. Furthermore, an iterative gap reducing algorithm is developed to solve the MILP model, which divides the whole problem into the master problem and sub-problem. The master problem provides an effective lower bound for the whole problem, and its solutions that can pass the sub-problem provide an effective upper bound for the whole problem. As a result, the algorithm can continuously reduce the gap between bounds and generate a new feasible solution towards the lower bound. The real cases show that compared with the manual method, the proposed method can generate a high quality one-day train-set schedule in a short time, which reduces the total operating costs by 10.5% and the total deadhead mileage by 23%.

     

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