Research on the Influence of Inventory Optimization Level on System Reliability

This paper aims to investigate the impact of spare parts management on system reliability in complex equipment systems, primarily focusing on two factors: the system's continuous operational duration and the total cost associated with spare parts storage. By establishing a multi-objective optim...

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Bibliographic Details
Published in2023 3rd International Conference on Electronic Information Engineering and Computer Science (EIECS) pp. 937 - 942
Main Authors Wang, Hongyan, Xiang, Gang, Lin, Ruishi, Li, Dongpeng, Peng, Yu, Zhou, Gongcheng, Cui, Langfu, Zhang, Qingzhen, Li, Liang
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.09.2023
Subjects
complex systems
Computer science
Costs
Focusing
Gyroscopes
maintenance support
multi-objective optimization
Optimization methods
Reliability engineering
Servomotors
spare parts management
system reliability
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Summary:This paper aims to investigate the impact of spare parts management on system reliability in complex equipment systems, primarily focusing on two factors: the system's continuous operational duration and the total cost associated with spare parts storage. By establishing a multi-objective optimization model, we have identified an optimal spare parts storage strategy that strikes a balance between the total cost and the system's continuous operational duration. The experimental results indicate that, within the assumed 3-year effective operational cycle of the complex equipment system, when equipping it with 2, 2, and 3 spare parts for the gyroscope, accelerometer, and servo motor respectively, the system's longest continuous operating duration is 37,300 hours, and the minimum cost is 84,000 RMB. Compared to the single-objective optimization model, this model not only reduces the total cost but also ensures that the system will not be interrupted due to spare parts shortages within its normal lifecycle. This study provides valuable insights and optimization methods for improving the reliability of complex systems.
DOI:10.1109/EIECS59936.2023.10435405