Combining decision tree algorithm for planning crew schedule for complex lines

Xing Ao; Jinjin Tang

doi:10.1117/12.2645535

23 November 2022 Combining decision tree algorithm for planning crew schedule for complex lines

Xing Ao, Jinjin Tang

Proceedings Volume 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022); 123023X (2022) https://doi.org/10.1117/12.2645535
Event: Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 2022, Guangzhou, China

Abstract

In this paper, a heuristic FIFO algorithm combined with decision tree algorithm is proposed for the problem of urban rail transit crew planning. First, when cutting the crew carriage working diagram, we select the minimum fragment cutting method based on the crew lounge to ensure that the cutting method does not affect the decision result. After that, the FIFO algorithm is used to solve the problem, combined with the decision tree algorithm to correct the defects generated by the FIFO, heuristically search for the locally optimal solution. We use the Depth First Search in the search process to reduce the amount of calculation. When the termination condition of the search algorithm is satisfied, if no feasible solution for the connection of the crew shift is found, the target value will be reduced by one to restart. The above process is repeated until a feasible solution is found. Computational experiments based on four operating diagram data of a subway line show that compared with the traditional manual schedule compilation of operating companies, this algorithm can obtain better results. In the future, in the face of more complex urban rail transit lines, this method can show more advantages.

Citation Download Citation

Xing Ao and Jinjin Tang "Combining decision tree algorithm for planning crew schedule for complex lines", Proc. SPIE 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 123023X (23 November 2022); https://doi.org/10.1117/12.2645535

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