Condition-Based Preventive Maintenance Planning of Railway Tracks: A Genetic Algorithm-Based Mathematical Model

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Industrial Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran.

2 Associate Prof., Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran.

3 Associate Prof., Department of Civil Engineering, Faculty of Civil Engineering, Isfahan University of Technology, Isfahan, Iran.

Abstract

Objective
Railway tracks are among the most critical and costly components of any railroad. Optimal and timely maintenance allocation ensures system reliability and effective resource utilization. This article presents a mathematical model designed for condition-based preventive maintenance of railway tracks. The objective is to minimize total maintenance costs while maintaining system availability and reliability. The cost function considers maintenance and renewal expenses, track possession costs, and unplanned failure costs during the planning horizon. The network is divided into segments, with the standard deviation of the longitudinal level calculated as the track quality index. Prediction and recovery models are developed to assess variations in the track quality index over the planning period. Preventive operations, including inspections, tamping, and ballast replacement, are assigned to track segments based on the allowable index limits and other constraints. The expected number of random failures is estimated using the non-homogeneous Poisson process, and preventive operations aim to reduce the incidence of such failures.
 
Methods
Given the complexity and non-linearity of the mathematical model, a meta-heuristic method based on a genetic algorithm was developed in MATLAB software. Initial random solutions were generated using a heuristic method. As a case study, input parameters were collected and modeled for one kilometer of railway in the Arak region. The efficiency of the proposed genetic algorithm was then compared with the results obtained using GAMS software.
 
Results
For the case study, the mathematical model was solved for various problem dimensions using both methods. When GAMS software was able to achieve the optimal solution, both methods produced identical results. When GAMS could only report a feasible solution, the genetic algorithm outperformed it. For higher dimensions where GAMS provided a solution, the genetic algorithm produced results with objective function values that were 40% better.
 
Conclusion
The planning of preventive maintenance operations for railway tracks during the project period is achieved through solving the proposed mathematical model. This allocation is based on the quality index of the tracks and considers the reducing effect of preventive operations on the expected number of random failures, ensuring the required minimum availability and reliability of the system. The variability of the model's input parameters allows for consideration of influencing heterogeneous factors based on the tracks, traffic, and environment, enabling adaptation to different conditions or the implementation of various strategies. Additionally, the proposed genetic algorithm can efficiently solve different dimensions of problem instances.

Keywords

Main Subjects

References

 
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Industrial Management Journal
Volume 17, Issue 1
2025
Pages 1-33

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