An Intelligent Heuristic Algorithm for a Multi-Objective Optimization Model of Urban Rail Transit Operation Plans

Urban rail transit (URT) systems frequently face operational challenges arising from temporal and spatial imbalances in passenger demand, resulting in inefficiencies in train scheduling and resource utilization. To address these issues, this study proposes a multi-objective optimization model that j...

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Vydáno v:Sustainability Ročník 17; číslo 10; s. 4617
Hlavní autoři: Han, Weisong, Shi, Zhihan, Lv, Xiaodong, Zhang, Guangming
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.05.2025
Témata:
Accuracy
Algorithms
Efficiency
Energy consumption
Flexibility
Foraging behavior
Genetic algorithms
Heuristic
Light rail transit
Linear programming
Mathematical functions
Mathematical optimization
Optimization algorithms
Passengers
Planning
Quality of service
Scheduling
China
ISSN:2071-1050, 2071-1050
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Abstract Urban rail transit (URT) systems frequently face operational challenges arising from temporal and spatial imbalances in passenger demand, resulting in inefficiencies in train scheduling and resource utilization. To address these issues, this study proposes a multi-objective optimization model that jointly plans short-turn and full-length train services. The objectives of the model are to minimize total passenger waiting time and train mileage while improving passenger load distribution across the rail line, subject to practical constraints such as departure frequency limitations, rolling stock availability, and coverage of short-turn services. To efficiently solve this model, an improved Pelican Optimization Algorithm (POA) is developed, incorporating techniques such as Tent chaotic mapping, nonlinear weight adjustment, Cauchy mutation, and the sparrow alert mechanism, significantly enhancing convergence accuracy and computational efficiency. A real-world case study based on Nanjing Metro Line 1 demonstrates that the proposed framework substantially reduces average passenger waiting times and overall train mileage, achieving a more balanced distribution of passenger loads. In addition, the study reveals that flexible-ratio dispatching strategies, representing theoretically optimal solutions, outperform integer-ratio dispatching schemes that reflect real-world operational constraints. This finding underscores that investigating the practical feasibility and optimization potential of flexible-ratio scheduling strategies constitutes a valuable direction for future research. The outcomes of this study provide a scalable and intelligent decision-support framework for train scheduling in URT systems, effectively contributing to the sustainable and intelligent development of rail operations.
AbstractList Urban rail transit (URT) systems frequently face operational challenges arising from temporal and spatial imbalances in passenger demand, resulting in inefficiencies in train scheduling and resource utilization. To address these issues, this study proposes a multi-objective optimization model that jointly plans short-turn and full-length train services. The objectives of the model are to minimize total passenger waiting time and train mileage while improving passenger load distribution across the rail line, subject to practical constraints such as departure frequency limitations, rolling stock availability, and coverage of short-turn services. To efficiently solve this model, an improved Pelican Optimization Algorithm (POA) is developed, incorporating techniques such as Tent chaotic mapping, nonlinear weight adjustment, Cauchy mutation, and the sparrow alert mechanism, significantly enhancing convergence accuracy and computational efficiency. A real-world case study based on Nanjing Metro Line 1 demonstrates that the proposed framework substantially reduces average passenger waiting times and overall train mileage, achieving a more balanced distribution of passenger loads. In addition, the study reveals that flexible-ratio dispatching strategies, representing theoretically optimal solutions, outperform integer-ratio dispatching schemes that reflect real-world operational constraints. This finding underscores that investigating the practical feasibility and optimization potential of flexible-ratio scheduling strategies constitutes a valuable direction for future research. The outcomes of this study provide a scalable and intelligent decision-support framework for train scheduling in URT systems, effectively contributing to the sustainable and intelligent development of rail operations.
Audience Academic
Author Lv, Xiaodong
Shi, Zhihan
Han, Weisong
Zhang, Guangming
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SubjectTerms Accuracy
Algorithms
Efficiency
Energy consumption
Flexibility
Foraging behavior
Genetic algorithms
Heuristic
Light rail transit
Linear programming
Mathematical functions
Mathematical optimization
Optimization algorithms
Passengers
Planning
Quality of service
Scheduling
Title An Intelligent Heuristic Algorithm for a Multi-Objective Optimization Model of Urban Rail Transit Operation Plans
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