- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
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| Citation: | CHEN Zhexuan, CHEN Shaokuan, FENG Jia, MI Qi, BAI Yun. Optimization Model for Rolling Stock Operation Scheduling in Urban Rail Transit Under Flexible Maintenance Strategy[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20250443
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To address the conflict between high-intensity operation and frequent maintenance of urban rail transit trains under the traditional maintenance strategy, an optimization method for multi-day operation scheduling of urban rail transit rolling stock considering a flexible maintenance strategy was studied under a decentralized execution mode of maintenance tasks. Firstly, an optimization model was constructed to minimize the sum of the fixed maintenance cost, the over-maintenance cost, and the insufficient-maintenance cost of rolling stock caused by unbalanced maintenance, considering constraints such as transportation task execution, maintenance task execution, depot maintenance capacity, rolling stock maintenance demand, and standby train arrangement. Secondly, in view of the characteristics of the multi-day operation plan, such as large scale, complex constraints, and strong correlation, a rolling stock space-time connection network with transportation tasks and maintenance tasks as nodes was established, and an improved depth-first search algorithm was designed to solve the constructed model. Finally, a case study was conducted taking an urban rail transit line as an example. The results indicate that compared with the traditional maintenance strategy, the flexible maintenance strategy reduces the over-maintenance and insufficient-maintenance mileages of rolling stock by 59.42% and 49.75% on average, respectively, decreases the total maintenance cost of rolling stock by 6.60% on average under scenarios with different numbers of operating rolling stock, and saves the minimum number of rolling stock required for the operation plan by one train; under the scenario with the same number of operating rolling stock, the decentralized execution of maintenance tasks reduces the standard deviation of the operation mileage of all rolling stock by 7.08% and reduces the number of maintenance rolling stock by two trains; the insufficient-maintenance cost of rolling stock under the same operation mileage level increases with the decrease of the failure rate parameter, and thus the rolling stock tends to execute maintenance before reaching the maintenance mileage standard; the proposed method can flexibly adjust the maintenance locations and timings of rolling stock under the conditions of limited depot maintenance capacity and increased transportation tasks during holidays, which verifies the robustness of the model in extreme scenarios.
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