Research on transfer schedule optimization of high-speed railway based on benders decomposition algorithm

Wentao Dong

doi:10.1117/12.2636519

6 May 2022 Research on transfer schedule optimization of high-speed railway based on benders decomposition algorithm

Wentao Dong

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 121760K (2022) https://doi.org/10.1117/12.2636519
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

With the rapid construction of high-speed railway network and the rise of the urban agglomeration, the passenger volume of large-scale railway passenger transport hubs continuous increases. However, due to the limitation of the through train operation scheme, only part of Origin-Destination(OD) pairs can be served, resulting in a large number of transfer passenger flows in the railway network. Adjusting the train arrival and departure schedule in large passenger transport hubs can safely and effectively organize the transfer passenger flow at the station and significantly improve the passenger satisfaction. Therefore, a 0-1 mixed integer linear high-speed rail hub train connection optimization model is established in this article which make the overall invalid passenger waiting time minimum as the optimization function, considering maintenance time constraints, station operation time constraints, reception-departure track capacity constraints, and transfer time constraints, and using the Benders decomposition algorithm for solving. The results show that this technique can obtain the exact optimal solution of the model, and has significant competitiveness in terms of solution quality and solution efficiency compared with other optimization algorithms of the same problem.

Citation Download Citation

Wentao Dong "Research on transfer schedule optimization of high-speed railway based on benders decomposition algorithm", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 121760K (6 May 2022); https://doi.org/10.1117/12.2636519

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KEYWORDS

Optimization (mathematics)

Genetic algorithms

Surface plasmons

Evolutionary algorithms

Analytical research

Computer programming

Data modeling

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