Optimization of annual planned rail maintenance

Oudshoorn, Menno; Koppenberg, Timo; Yorke-Smith, Neil

doi:10.1111/mice.12764

Cited by 8 publications

(5 citation statements)

References 33 publications

(46 reference statements)

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“…Additionally, they evaluated the computational cost using exact (branch-and-bound) or heuristic solutions for solving networks with up to 25 work zones. Furthermore, Oudshoorn et al [34] investigated a real railway maintenance scheduling problem for a one-year preventive maintenance period. They developed and compared three heuristic/metaheuristic approaches: evolutionary strategy, greedy heuristic, and a combination of both.…”

Section: Optimization Of Railway Line Maintenance Schedulingmentioning

confidence: 99%

Combined Optimization of Maintenance Works and Crews in Railway Networks

Gkonou,

Nisyrios,

Gkiotsalitis

2023

Applied Sciences

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This study develops optimal maintenance schedules for train lines, a critical endeavor ensuring the safety, efficiency, and reliability of railway networks. The study addresses the combined scheduling problem of maintenance works and crews on the railway networks. The baseline scheduling model is initially established with the primary objective of allocating maintenance tasks efficiently while adhering to pertinent constraints, encompassing task grouping and cost minimization. Subsequently, this baseline model is enhanced through the integration of crew scheduling, wherein work crews are strategically assigned to execute predefined tasks, thereby facilitating effective workload distribution. The combined maintenance work and crew scheduling problem is mathematically formulated as a binary linear programming model, enabling the attainment of globally optimal solutions. Comparing the outcomes of our enhanced model, which incorporates both maintenance works and crew schedules, with the baseline model that solely addresses maintenance works, we reveal that task grouping in accordance with predefined conditions leads to reduced overall costs by minimizing maintenance duration during various periods. Additionally, the judicious distribution of workload among the crews ensures comprehensive coverage of all essential tasks. These findings underscore the significance of our proposed approach in enhancing the operational efficacy and economic viability of railway maintenance scheduling, thereby offering valuable insights for practical implementation and future research endeavors.

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Section: Optimization Of Railway Line Maintenance Schedulingmentioning

confidence: 99%

Combined Optimization of Maintenance Works and Crews in Railway Networks

Gkonou,

Nisyrios,

Gkiotsalitis

2023

Applied Sciences

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“…First, the consideration of the monthly planning model and window scheduling model in two stages makes it difficult to achieve overall optimization. Second, the maintenance plan is established according to a fixed cycle, without considering the equipment conditions (Oudshoorn et al., 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Segment‐condition‐based railway track maintenance schedule optimization

Chang

Liu

Tang

2022

Computer aided Civil Eng

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Reasonable maintenance plans are important for ensuring safe train operation and prolonging the service life of tracks. However, previous studies on the scheduling and optimization of railway track maintenance plans possess the following limitations. First, scheduling optimization models generally operate at the planning level with months as the time units rather than at the operation scheduling level with days as the time units. Second, they fail to consider the bidirectional feedback and dynamic impact between predicted condition and maintenance scheduling. Third, actual on‐site operation strategies (e.g., opportunistic and centralized maintenance) are neglected. To address these issues, this paper proposes a novel operation‐level maintenance scheduling framework based on segment condition considering both opportunistic and centralized maintenance strategies. According to the problem characteristics, ae hybrid multi‐objective optimization algorithm based on a quantum‐behaved particle swarm optimization was designed. Furthermore, by utilizing actual practical cases, the effectiveness of the model and algorithm was verified via comparison with MOPSO, NSGA‐II, NSGA‐III, and Gurobi in different scenarios.

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“…Both biology-and physics-based algorithms have been used in railway engineering and planning scenarios, thus demonstrating their effectiveness. Examples include optimizing rail alignment with the ant colony algorithm (Roy & Maji, 2022) and deep learning (Gao et al, 2022), determining rail maintenance by designing an evolution strategy and greedy metaheuristic (Oudshoorn et al, 2022), optimizing rail alignments and station locations using particle swarm-based algorithm (Song et al, 2022), and performing rail surface detection and estimating railway track longitudinal irregularities by applying deep learning (Li et al, 2022;Wu et al, 2022). With regard to relevant studies of line planning, Szeto and Jiang ( 2014 The widely applied GA shows excellent adaptability to the LPP, in that, the crossover and mutation process of chromosomes is a good formulation of the updating process of trains in line plans or timetables.…”

Section: Related Workmentioning

confidence: 99%

Weekly line planning to improve the match between rail supply and passenger demand

Nie

2022

Computer aided Civil Eng

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Traditional railway line planning approaches are used to determine the best train lines during specified periods (e.g., 1 day, 1 h, or several hours). However, mismatches occur relative to the spatiotemporal changes in travel demands over longer time periods (e.g., 1 week). To more effectively match rail supply with passenger demand, a weekly line planning (WLP) model considering both peak and nonpeak passenger demand over an entire week and during all periods of each day is proposed. This model maximizes the defined supply-demand matching utility and minimizes train operational cost. Important constraints to ensure proper daily and hourly train repetition over multiple days are included. A customized genetic algorithm (GA) is developed, which outperforms the CPLEX solver in real-world cases of high-speed railways in China. Compared with practical and single-day planning, WLP shows clear improvement in terms of operational cost, passenger travel speed, and supply-demand matching.

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Optimization of annual planned rail maintenance

Cited by 8 publications

References 33 publications

Combined Optimization of Maintenance Works and Crews in Railway Networks

Combined Optimization of Maintenance Works and Crews in Railway Networks

Segment‐condition‐based railway track maintenance schedule optimization

Weekly line planning to improve the match between rail supply and passenger demand

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