Importance Degree Evaluation of Spare Parts Based on Clustering Algorithm and Back-Propagation Neural Network
Joint Authors
Zhang, Qing
Zhao, Jiang-bin
Zhang, Shoujing
Qin, Xiaofan
Hu, Sheng
Dong, Bochao
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-22
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The quantitative evaluation of the importance degree of spare parts is essential as spare parts’ maintenance is critical for inventory management.
Most of the methods used in previous research are subjective.
For this reason, an accurate method for the evaluation of the importance degree combining an improved clustering algorithm with a back-propagation neural network (BPNN) is proposed in the present paper.
First, we classified the spare parts by analyzing their historical maintenance and inventory data.
Second, we evaluated the effectiveness of classification using the Davies–Bouldin index and the Calinski–Harabasz indicator and verified it using the training data.
Finally, we used BPNN to determine the training data necessary for an accurate assessment of the importance degree of spare parts.
The previous importance evaluation methods were susceptible to subjective factors during the evaluation process.
The model established in this paper used the actual data of the company for machine learning and used the improved clustering algorithm to implement training and classification of spare parts data.
The importance value of each spare part was output, which additionally reduced the impact of subjective factors on the importance evaluation.
At the same time, the use of less data to evaluate the importance of spare parts was achieved, which improved the evaluation efficiency.
American Psychological Association (APA)
Zhang, Shoujing& Qin, Xiaofan& Hu, Sheng& Zhang, Qing& Dong, Bochao& Zhao, Jiang-bin. 2020. Importance Degree Evaluation of Spare Parts Based on Clustering Algorithm and Back-Propagation Neural Network. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1196556
Modern Language Association (MLA)
Zhang, Shoujing…[et al.]. Importance Degree Evaluation of Spare Parts Based on Clustering Algorithm and Back-Propagation Neural Network. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1196556
American Medical Association (AMA)
Zhang, Shoujing& Qin, Xiaofan& Hu, Sheng& Zhang, Qing& Dong, Bochao& Zhao, Jiang-bin. Importance Degree Evaluation of Spare Parts Based on Clustering Algorithm and Back-Propagation Neural Network. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1196556
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196556