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<p>Efficient weekly scheduling, including task assignments, maintenance, washing, and parking, for urban rail transit vehicles is essential for reducing operational costs and ensuring smooth depot operations. However, optimizing these components separately may cause inefficiencies, conflicts, and suboptimal vehicle utilization, ultimately compromising system performance and reliability. This study presents an integrated optimization model to minimize mileage variance, driving distances, and shunting operations. To address the multiobjective nature of the problem while satisfying operational constraints, a novel utility-balancing simulated annealing (U-SA) algorithm is developed. In this algorithm, the utility-balancing mechanism is embedded in the SA framework and guided by fuzzy programming to coordinate multiple objectives effectively. The algorithm utilizes three distinct operators—mileage-balancing operator, distance-minimization operator, and shunting-optimization operator—to steer the optimization toward an efficient solution. The model and algorithm are validated through a real-world case study of Beijing Subway’s Line 12. Computational results demonstrate that the approach effectively balances objectives, reduces operational costs, and enhances resource utilization. Sensitivity analysis reveals that adding parking tracks reduces shunting operations, while earlier washing windows helps shorten driving distances. This method offers a practical decision-support tool to improve the efficiency of urban rail transit operations.</p>

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Integrated Optimization of Weekly Scheduling for Urban Rail Transit Vehicles: Task Assignment, Maintenance, Washing, and Parking

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月20日

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  • Yaqiong Zhao,
  • Dewei Li,
  • Peng Geng,
  • Meng Xiao

摘要

Efficient weekly scheduling, including task assignments, maintenance, washing, and parking, for urban rail transit vehicles is essential for reducing operational costs and ensuring smooth depot operations. However, optimizing these components separately may cause inefficiencies, conflicts, and suboptimal vehicle utilization, ultimately compromising system performance and reliability. This study presents an integrated optimization model to minimize mileage variance, driving distances, and shunting operations. To address the multiobjective nature of the problem while satisfying operational constraints, a novel utility-balancing simulated annealing (U-SA) algorithm is developed. In this algorithm, the utility-balancing mechanism is embedded in the SA framework and guided by fuzzy programming to coordinate multiple objectives effectively. The algorithm utilizes three distinct operators—mileage-balancing operator, distance-minimization operator, and shunting-optimization operator—to steer the optimization toward an efficient solution. The model and algorithm are validated through a real-world case study of Beijing Subway’s Line 12. Computational results demonstrate that the approach effectively balances objectives, reduces operational costs, and enhances resource utilization. Sensitivity analysis reveals that adding parking tracks reduces shunting operations, while earlier washing windows helps shorten driving distances. This method offers a practical decision-support tool to improve the efficiency of urban rail transit operations.