A Matheuristic Iterative Approach for Profit-Oriented Line Planning Applied to the Chinese High-Speed Railway Network

Journal Article (2020)
Author(s)

Di Liu (Southwest Jiaotong University, Katholieke Universiteit Leuven)

Javier Durán Micco (Katholieke Universiteit Leuven)

Gongyuan Lu (Southwest Jiaotong University)

Qiyuan Peng (Southwest Jiaotong University)

Jia Ning (Southwest Jiaotong University)

Pieter Vansteenwegen (Katholieke Universiteit Leuven)

Affiliation
External organisation
DOI related publication
https://doi.org/10.1155/2020/4294195
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Publication Year
2020
Language
English
Affiliation
External organisation
Volume number
2020

Abstract

In this paper, a matheuristic iterative approach (MHIA) is proposed to solve the line planning problem, also called network design problem, and frequency setting on the Chinese high-speed railway network. Our optimization model integrates the cost-oriented and passenger-oriented objectives into a profit-oriented objective. Therefore, the passenger travel time is incorporated in the ticket price using a travel time value. As a result, transfers and detours will result in lower ticket prices and thus lower revenues for the operator. When evaluating the performance of a given line plan, the way in which passengers will travel through the network needs to be modelled. This passenger assignment is typically a time-consuming calculation. The proposed line planning approach iteratively improves the line plan using easy-to-determine indicators. During the process, a mixed integer linear programming model addresses the passenger assignment and optimizes the frequency setting in order to maximise the operational profit. Extensive computational experiments are executed to show the effectiveness of the proposed approach to deal with the real-world railway network line planning problem. Through extensive computational experiments on the small example network and real-world-based instances, the results show that the proposed model can improve the profits by 22.4% on average comparing to their initial solutions. When comparing to an alternative iterative approach, our proposed method has advantage of obtaining high quality of solutions by improving the profit 10.8% on average. For small, medium, and large size networks, the obtained results are close to the optimal solutions, when available.

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