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Focusing on service quality improvement and energy consumption reduction in an urban rail transit network, this paper proposes a multi-objective programming model to jointly optimize timetables through considering total passenger trip time and operational energy consumption.
Considering a passenger arrival distribution, the problem is formulated to optimize timetables for multiple community shuttle routes, with the objective of minimizing passenger's schedule delay cost and transfer cost.
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Therefore, in this paper, GA has been used to optimize train timetable.
Other meta-heuristic methods can be used to optimize train timetable.
Therefore, in this paper, DSS has been used to optimize train timetable.
The Islamic Republic of Iran Railways (IRIR) data and regulations have been used to optimize train timetable.
In summary, the research to date did not provide adequate scientific evidence to optimize train timetabling problem considering realistic constraints including stops for track maintenance and praying.
The GA (in the first proposed method) and DDS (in the second proposed method) have been used to optimize train timetabling problem, while a CA simulation approach has been used to model the train traffic flow and evaluate the fitness of each solution (i.e., the total delay time generated during the optimization).
To achieve a better performance on the net energy consumption, i.e., the difference between the tractive energy consumption and the utilization of regenerative energy, this paper formulates an integrated energy-efficient operation model to jointly optimize the timetable and speed profile.
This paper investigates an issue for optimizing synchronized timetable for community shuttles linked with metro service.
In addition, compared with the two-step approach optimizing the timetable and speed profile separately, the integrated approach can reduce the net energy consumption around 20%.
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