Supply reliability-driven joint optimization of maintenance and spare parts inventory in a gas pipeline system

This work proposes a method for the joint optimization of the maintenance and spare parts inventory to maximize the reliability of gas supply in a pipeline system. The method trades off maintenance costs and risk of gas shortages. System failure probability calculation and system maximum supply capacity analysis, joint opti-mization modeling and system maintenance planning, are performed. A gas compressor station is considered as object of the analysis and a discrete Markov model is used to describe the state transition process. A supply capacity model is proposed to analyze the maximum supply capacity of the pipeline system under different failure scenarios. The joint optimization of maintenance and spare parts inventory is performed using a genetic algorithm to optimize the inspection interval and spare ordering time, considering the stochastic behavior of lead time. The effectiveness of the method is validated on a case study of a pipeline system in China. The results demonstrate that the proposed method outperforms others in identifying optimal maintenance strategies. In addition, the sensitivity analysis of critical parameters is analyzed in detail, which can further ensure the robustness of the method.

Automated summary

This work proposes a method for joint optimization of maintenance and spare parts inventory to maximize the reliability of a pipeline system's gas supply. The method considers maintenance costs and the risk of gas shortages. Various steps are performed, including system failure probability calculation, system maximum supply capacity analysis, joint optimization modeling, and system maintenance planning. A genetic algorithm is used to optimize the inspection interval and spare ordering time, taking into account the stochastic behavior of lead time.