A mixed-integer linear programming method for time-dependent line planning in passenger railway systems
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| Argitaratua izan da: | PLoS One vol. 20, no. 5 (May 2025), p. e0322394 |
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Public Library of Science
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| Sarrera elektronikoa: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1371/journal.pone.0322394 |2 doi | |
| 035 | |a 3212656170 | ||
| 045 | 2 | |b d20250501 |b d20250531 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Shi, Xin | |
| 245 | 1 | |a A mixed-integer linear programming method for time-dependent line planning in passenger railway systems | |
| 260 | |b Public Library of Science |c May 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a China | |
| 653 | |a Trains | ||
| 653 | |a Time dependence | ||
| 653 | |a Passenger trains | ||
| 653 | |a Linear programming | ||
| 653 | |a Integer programming | ||
| 653 | |a Operating costs | ||
| 653 | |a Graph theory | ||
| 653 | |a Planning | ||
| 653 | |a Optimization | ||
| 653 | |a Railways | ||
| 653 | |a Railway networks | ||
| 653 | |a Algorithms | ||
| 653 | |a Mixed integer | ||
| 653 | |a Railroads | ||
| 653 | |a Nonlinear programming | ||
| 653 | |a Passengers | ||
| 653 | |a Case studies | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Zhou, Wenliang | |
| 700 | 1 | |a Li, Xiang | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 5 (May 2025), p. e0322394 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3212656170/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3212656170/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3212656170/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |