Application of Evolutionary Algorithm on a Transportation Scheduling Problem - The Mass Rapid Transit

Kwan, C.M.; Chang, C.S.

doi:10.1109/cec.2005.1554798

Cited by 20 publications

(14 citation statements)

References 16 publications

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“…Kwan and Chang (2005) applied a heuristic-based evolutionary algorithm to optimize the frequency (or headway) between trains, where the operation costs and the passenger dissatisfaction are included in the performance index. Liebchen (2006Liebchen ( , 2008 formulated the train scheduling problem as a periodic event-scheduling problem based on a graph model and obtained periodic schedules for the Berlin subway system using genetic algorithms and integer programming.…”

Section: Introductionmentioning

confidence: 99%

Passenger-demands-oriented train scheduling for an urban rail transit network

Wang

Tang

Ning

et al. 2015

Transportation Research Part C: Emerging Technologies

192

106

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Section: Introductionmentioning

confidence: 99%

Passenger-demands-oriented train scheduling for an urban rail transit network

Wang

Tang

Ning

et al. 2015

Transportation Research Part C: Emerging Technologies

192

106

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“…In general, when the passenger arrival process at stations follows some particular probability distributions, such as uniform and Poisson distributions, a regular schedule with fixed headway between consecutive vehicles can reduce the total passenger waiting time effectively [5,6]. However, using regular schedules may result in longer passenger waiting time and travel time during over-saturated periods [7]. Some studies [7,8] paid attention to semi-regular or half-regular timetables.…”

Section: Introductionmentioning

confidence: 99%

Timetable Design for Minimizing Passenger Travel Time and Congestion for a Single Metro Line

Shen

Ren

Liu

2018

PROMET

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This paper brings a proposal for a timetable optimization model for minimizing the passenger travel time and congestion for a single metro line under time-dependent demand. The model is an integer-programming model that systemically considers the passenger travel time, the capacity of trains, and the capacity of platforms. A multi-objective function and a recursive optimization method are presented to solve the optimization problem. Using the model we can obtain an efficient timetable with minimal passenger travel time and minimal number of congestion events on platforms. Moreover, by increasing the number of dispatches, the critical point from congestion state to free-flow state and the optimal timetable with minimal cost for avoiding congestion on platforms can be obtained. The effectiveness of the model is evaluated by a real example. A half-regular timetable with fixed headways in each operation period and an irregular timetable with unfixed headway are investigated for comparison.

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“…In contrast, Pareto optimality method is more practical, which can provide more information to operators when determining an optimal train timetable, that is, list more compromise solutions to let the operator choose according to the circumstances of urban rail system. Kwan and Chang 15 introduced Pareto optimality, but the variables in their model are relatively simplified, which limits its application. In this article, therefore, a Pareto optimal urban rail train scheduling problem, which is more realistic and helpful, is proposed, in which the PTTT and the number of used train stocks are chosen as objectives to represent the service quality and operation cost, respectively.…”

Section: Introductionmentioning

confidence: 99%

Pareto optimal train scheduling for urban rail transit using generalized particle swarm optimization

Chu

Zhang

Chen

et al. 2016

Advances in Mechanical Engineering

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In urban rail transport, train timetable plays a crucial role, whose quality determines the whole system's performance to a large extent. In practical urban rail operation, two contradictive aspects-service quality and operation cost-should be considered during train scheduling. A good train timetable should achieve considerable service quality with as little operation cost as possible. Previously, many studies have been conducted specific to urban rail train scheduling, although most of them do not put enough emphasis on its multi-objective nature. In this article, therefore, Pareto optimal urban rail train scheduling which can give more instruction to practical operation is studied. First, referring to some existing studies, the problem is reasonably defined, which takes time-dependent origin-destination demand as the input and aims at minimizing the passengers' total travel time and the number of used train stocks. Then, an efficient iteration algorithm and a valid train stock assignment procedure are designed to calculate the passengers' total travel time and required train stock number, respectively. On that basis, the studied problem is reasonably formulated as a bi-objective optimization model and a Pareto-based particle swarm optimization procedure is designed to solve it. Finally, with two different scaled urban rail lines, the whole methodology is illustrated and the algorithm is tested.

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Application of Evolutionary Algorithm on a Transportation Scheduling Problem - The Mass Rapid Transit

Cited by 20 publications

References 16 publications

Passenger-demands-oriented train scheduling for an urban rail transit network

Passenger-demands-oriented train scheduling for an urban rail transit network

Timetable Design for Minimizing Passenger Travel Time and Congestion for a Single Metro Line

Pareto optimal train scheduling for urban rail transit using generalized particle swarm optimization

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