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http://hdl.handle.net/10397/101284
| Title: | Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty | Authors: | Yang, X Chen, A Ning, B Tang, T |
Issue Date: | Jan-2017 | Source: | Transportation research. Part E, Logistics and transportation review, Jan. 2017, v. 97, p. 22-37 | Abstract: | For optimization of timetables in metro systems with regular cyclic operation, this paper develops a bi-objective programming approach addressed to minimization of net energy consumption and total travel time with provision for dwell time uncertainty. Firstly, we formulate the bi-objective timetable optimization problem as an expected value model with speed profile control. Secondly, we use the ɛ-constraint method within a genetic algorithm framework to determine the Pareto optimal solutions. Finally, numerical examples based on the real-life operation data from the Beijing Metro Yizhuang Line are presented in order to illustrate the practicability and effectiveness of the approach developed in the paper. | Keywords: | Bi-objective Energy consumption Metro systems Timetable optimization |
Publisher: | Pergamon Press | Journal: | Transportation research. Part E, Logistics and transportation review | ISSN: | 1366-5545 | EISSN: | 1878-5794 | DOI: | 10.1016/j.tre.2016.10.012 | Rights: | © 2016 Elsevier Ltd. All rights reserved. © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ The following publication Yang, X., Chen, A., Ning, B., & Tang, T. (2017). Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty. Transportation Research Part E: Logistics and Transportation Review, 97, 22-37 is available at https://doi.org/10.1016/j.tre.2016.10.012. |
| Appears in Collections: | Journal/Magazine Article |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| Chen_Bi-objective_Programming_Approach.pdf | Pre-Published version | 1.13 MB | Adobe PDF | View/Open |
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