Optimization of Train Working Plan based on Multiobjective Bi-level Programming Model


Xiaowei HAI, Chanchan ZHAO, Journal of Information Processing Systems Vol. 14, No. 2, pp. 487-498, Apr. 2018  

https://doi.org/10.3745/JIPS.04.0066
Keywords: Bi-Level Programming, Genetic Algorithm, Multiobjective, Train Working Plan
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Abstract

The purpose of the high-speed railway construction is to better satisfy passenger travel demands. Accordingly, the design of the train working plan must also take a full account of the interests of passengers. Aiming at problems, such as the complex transport organization and different speed trains coexisting, combined with the existing research on the train working plan optimization model, the multiobjective bi-level programming model of the high-speed railway passenger train working plan was established. This model considers the interests of passengers as the center and also takes into account the interests of railway transport enterprises. Specifically, passenger travel cost and travel time minimizations are both considered as the objectives of upper-level programming, whereas railway enterprise profit maximization is regarded as the objective of the lower-level programming. The model solution algorithm based on genetic algorithm was proposed. Through an example analysis, the feasibility and rationality of the model and algorithm were proved.


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Cite this article
[APA Style]
HAI, X. & ZHAO, C. (2018). Optimization of Train Working Plan based on Multiobjective Bi-level Programming Model. Journal of Information Processing Systems, 14(2), 487-498. DOI: 10.3745/JIPS.04.0066.

[IEEE Style]
X. HAI and C. ZHAO, "Optimization of Train Working Plan based on Multiobjective Bi-level Programming Model," Journal of Information Processing Systems, vol. 14, no. 2, pp. 487-498, 2018. DOI: 10.3745/JIPS.04.0066.

[ACM Style]
Xiaowei HAI and Chanchan ZHAO. 2018. Optimization of Train Working Plan based on Multiobjective Bi-level Programming Model. Journal of Information Processing Systems, 14, 2, (2018), 487-498. DOI: 10.3745/JIPS.04.0066.