Content area

Abstract

This paper addresses a line planning problem (LPP) that simultaneously optimizes both train and passenger times in passenger railway systems, considering time-dependent origin-destination-period demand and passenger train choice. The problem is clearly and flexibly modeled in a physical infrastructure-based directed graph, which efficiently integrates the train operation choice and the passenger train choice. The problem is first formulated as a mixed-integer, non-concave, and non-linear programming model aimed at minimizing both the total operating cost of trains and the total travel cost of passengers. To solve the problem, an extended time-dimension method is proposed to transform the non-concave and non-linear model into a mixed-integer linear programming (MILP) model that can be solved using a commercial solver. Additionally, a set of simplification strategies is introduced to reduce the computational complexity while ensuring the global optimality of the linear model. A case study of a busy Chinese railway line demonstrates that the optimized time-dependent line plan enhances operational efficiency and accommodates the diversified travel preferences driven by time-dependent demand.

Details

1009240
Business indexing term
Location
Title
A mixed-integer linear programming method for time-dependent line planning in passenger railway systems
Publication title
PLoS One; San Francisco
Volume
20
Issue
5
First page
e0322394
Publication year
2025
Publication date
May 2025
Section
Research Article
Publisher
Public Library of Science
Place of publication
San Francisco
Country of publication
United States
e-ISSN
19326203
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Milestone dates
2025-01-12 (Received); 2025-03-19 (Accepted); 2025-05-27 (Published)
ProQuest document ID
3212656170
Document URL
https://www.proquest.com/scholarly-journals/mixed-integer-linear-programming-method-time/docview/3212656170/se-2?accountid=208611
Copyright
© 2025 Shi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-05-28
Database
ProQuest One Academic