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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
30.04.2024
DOI
https://doi.org/10.7307/ptt.v36i2.313
ISSUE
2024 (Vol 36), Issue 2
LICENSE
Copyright (c) 2024 Bowen Ma, Yuguang Wei, Bo Fang, Chunyi Li
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This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Integrated Optimisation Model of Daily Freight Train Scheduling and Dynamic Railcar Routing Based on a Two-Layer Space-Time Network

Authors:

Bowen Ma
School of Traffic and Transportation, Beijing Jiaotong University

Yuguang Wei
School of Traffic and Transportation, Beijing Jiaotong University

Bo Fang
School of Traffic and Transportation, Beijing Jiaotong University

Chunyi Li
China Railway Publishing House

Keywords:railroad freight transportation, freight train scheduling, dynamic railcar routing, space-time network

Abstract

This paper focuses on daily freight train scheduling and dynamic railcar routing problems for rail freight transportation at the operational level.  Two mixed integer linear programming models that adopted different strategies were formulated based on a continuous two-layer time-space network. We simultaneously considered the benefits of railroad company and service quality when setting the objective function. By solving the  models, we can distribute the dynamic railcar flows to the train paths in the basic train timetable to obtain the daily train operation plan over a short time horizon (e.g. a day), which will be helpful for dispatchers to make decisions such as the empty railcar distribution and car routing (trip planning). Finally, we compared two models on a part of the Chinese railroad network. The results show that the second model can effectively improve the efficiency of railroad freight transportation.

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How to Cite
Ma, B. (et al.) 2024. Integrated Optimisation Model of Daily Freight Train Scheduling and Dynamic Railcar Routing Based on a Two-Layer Space-Time Network. Traffic&Transportation Journal. 36, 2 (Apr. 2024), 232-248. DOI: https://doi.org/10.7307/ptt.v36i2.313.

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