Optimizing of Railway Maintenance Activities Using Fuzzy Logic: An Intelligent Approach for Improved Reliability
DOI:
https://doi.org/10.15837/ijccc.2026.1.7209Keywords:
Fuzzy logic, railway maintenance, reliability, predictive maintenance, Colored Petri Nets, frugal exploitationAbstract
Transport system efficiency is a fundamental and strategic issue for all transport companies. The ability to adapt transport networks reliably is crucial as demand fluctuates, specifications shift and traffic specificities cannot be neglected. Uncertainty, ubiquitous in rail transport networks, complicate this task even further. These uncertainties can manifest themselves in a variety of ways: unexpected fluctuations in journey times, rolling stock failures or the emergence of additional traffic tasks that could not have been anticipated in the initial scheduling process. Each type of uncertainty creates a potential risk related to system imbalance, which requires rapid and complex adjustments to guarantee rail traffic availability and stability. These maintenance scheduling issues in rail transport systems demand planning approaches that extend beyond traditional techniques. It is crucial to evolve maintenance scheduling tools able to manage scheduling under stable conditions, as well as to effectively respond to unexpected disruptions and quickly shifting traffic conditions. This paper addresses these challenges problem and proposes a reliable and robust maintenance policy taking account of tasks imprecision and human expertise. The maintenance model is designed to assist decision making systems to increase traffic safety significantly, while saving time and money. To resolve this problem, a fuzzy inference system is used to appropriately deal with uncertainties using Colored Petri nets and fuzzy logic. The findings indicate that the adaptive fuzzy model developed has an excellent ability to precisely learn and predict traffic constraints and lead to significant changes in decision making and the incorporation of feedback into the management and support system.
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