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Train repathing in emergencies based on fuzzy linear programming.

Xuelei Meng¹, Bingmou Cui¹

¹School of Traffic and Transportation, Lanzhou Jiaotong University, P.O. Box 405, Anning West Road, Anning District, Lanzhou, Gansu 730070, China.

Thescientificworldjournal

|August 15, 2014

Summary

This study addresses emergency train pathing by developing a fuzzy linear programming model. The model effectively determines optimal train routes considering costs and constraints, proving efficient for real-world railway emergencies.

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Area of Science:

Operations Research
Transportation Science
Railway Engineering

Background:

Train pathing, assigning train trips to rail segments, is crucial for efficient railway operations.
Emergency situations present unique challenges for train pathing due to uncertainties and complex constraints.
Existing models may not adequately address the fuzzy nature of costs and capabilities during emergencies.

Purpose of the Study:

To develop a robust model for determining train paths during railway emergencies.
To incorporate influencing factors like transferring cost, running cost, and social adverse effect cost.
To handle uncertainties in cost coefficients and capability constraints using fuzzy logic.

Main Methods:

Formulated a fuzzy linear programming model for emergency train pathing.
Designed fuzzy membership functions to represent fuzzy coefficients.
Introduced contraction-expansion factors to manage uncertainty in coefficient ranges.
Transformed the fuzzy model into a determinate linear model using triangular fuzzy coefficients.

Main Results:

Successfully solved the fuzzy linear programming model for emergency train pathing.
Demonstrated the model's availability and the algorithm's efficiency using real data from the Beijing-Shanghai Railway.
Validated the model's capability to handle segment and station capability constraints.

Conclusions:

The proposed fuzzy linear programming model is effective for emergency train pathing.
The method provides an efficient algorithm for solving complex train pathing problems under uncertainty.
This approach enhances railway operational resilience during emergencies.