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- Title
多站协同的技术站配流优化研究.
- Authors
李星宇; 宋瑞
- Abstract
Technical station is an important part of railway network. Its operation organization level directly determines the production efficiency of railway transportation. The traditional wagon-flow allocation problems of technical stations usually consider the optimization of internal operation, such as the hump and assemble sequence and the assignment of classification track, so as to reduce the total residence time of wagon at stations. To strengthen the interconnection between stations and promote orderly wagon-flow, the concept of multi-station collaboration based on freight train marshalling plan was proposed. The model M1 of dynamic wagon-flow allocation considering multiple shunting locomotives was established to minimize the total residence time of wagon at stations. The model M2 of multi-station collaborative optimization was to maximize the sum of reduce residence time and minimize the running cost of newly added train flow after collaboration. The model M1 belongs to NP-hard problem. Lagrange relaxation algorithm was designed to decompose the original model into three sub-problems. Model M2 can be solved and verified by GUROBI solver combined with the results obtained from model M1 and multi-station collaborative concept. It can be seen from the case analysis that within the planned time limit, there are six stations carrying out collaborative allocation in a given network. By changing the marshalling direction of under-loaded trains, the wagon-flow supply of adjacent stations is realized. The residence time is reduced by 645.1 h, which is 6.57% lower than before the collaboration, and 6 trains departing on time are added. The result shows that the proposed model and algorithm can compress the total residence time, improve the reliability of stage plan, and enhance the production efficiency of railway freight transportation.
- Subjects
FREIGHT &; freightage; STATION wagons; NP-hard problems; RAILROAD trains; JOINT use of railroad facilities; WAGONS
- Publication
Journal of Railway Science & Engineering, 2023, Vol 20, Issue 3, p846
- ISSN
1672-7029
- Publication type
Article
- DOI
10.19713/j.cnki.43-1423/u.T20220769