As cities grow, intercity railways are becoming increasingly popular for short trips between neighboring areas. These railways cater well to commuters and travelers, making reliable and cost-effective maintenance crucial. Timely access to spare parts is essential for ensuring the smooth operation of intercity railways. Traditionally, intercity railways lack failure probability data for spare parts, which hampers the support for spare parts ordering decisions, resulting in spare parts management primarily relying on manual experience. This approach often leads to problems like excessive inventory levels and high management costs. To enhance the reliability of intercity railway operations and reduce spare parts management costs, this paper employs the Zebra Optimization Algorithm-Least Squares Support Vector Machine (ZOA-LSSVM) to analyze the reliability of the important Weibull distribution spare parts of the intercity railway and fit the parameters of the reliability function for spare parts. Based on the failure rate, an inventory control model for intercity railway spare parts is established, aiming to minimize total costs while considering constraints such as order point, order quantity, and equipment availability. A genetic algorithm is designed to solve this model. To verify the effectiveness of the model, we select the contact network insulators of Chinese J Intercity Railway as the case study subject. By comparing the fitting performance of several methods, including ZOA-LSSVM, Genetic Algorithm (GA)-LSSVM, LSSVM, and Least Squares Regression (LSR), the effectiveness of ZOA-LSSVM is validated. The experimental results indicate that ZOA-LSSVM can provide better prediction accuracy. Based on this fitting method, spare parts inventory management is conducted. By comparing it with the traditional manual experience method, it is found that the approach proposed in this paper not only ensures the stable operation of intercity railways but also significantly reduces costs by approximately 13.6%. This result fully demonstrates the superiority of the optimization model established in this paper in practical applications and provides new ideas and methods for the management of spare parts for other intercity railways.
Wang et al. (Wed,) studied this question.