Collaborative Optimization of Car-flow Organization for Freight Trains Based on Adjacent Technical Stations

  • Yijing Yang Beijing Jiaotong University, Beijing, China
  • Xu Wu Beijing Jiaotong University, Beijing, China
  • Haonan Li Beijing Jiaotong University, Beijing, China
Keywords: collaborative optimization, car-flow organization, genetic algorithm, taboo search algorithm, active scheduling

Abstract

This paper proposes a collaborative optimization model of car-flow organization for freight trains based on adjacent technical stations to minimize the average dwell time of train cars in a yard. To solve the car-flow organization problems, a priority-based hump sequence, which depends on the cars available in two adjacent technical stations, is adopted. Furthermore, a meta-heuristic algorithm based on the genetic algorithm and the taboo search algorithm is adopted to solve the model, and the introduction of the active scheduling method improves the efficiency of the algorithm. Finally, the model is applied to the car-flow organization problem of two adjacent technical stations, and the results are compared with those from a single technical station without collaboration. The results demonstrate that collaborative car-flow organization between technical stations significantly reduces the average dwell time at the stations, thereby improving the utilization rate of railroad equipment. In addition, the results indicate that the hybrid genetic algorithm can rapidly determine the train hump and marshalling schemes.

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Published
2021-01-31
How to Cite
1.
Yang Y, Wu X, Li H. Collaborative Optimization of Car-flow Organization for Freight Trains Based on Adjacent Technical Stations. Promet - Traffic&Transportation. 2021;33(1):117-28. DOI: 10.7307/ptt.v33i1.3601
Section
Articles