Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method

Spare parts management is a critical issue in the industrial field, alongside planning maintenance and logistics activities. For accurate classification in particular, the decision-makers can determine the optimal inventory management strategy. However, problems such as criteria selection, rules exp...

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Bibliographic Details
Published in:Applied sciences 2021-08, Vol.11 (15), p.7088
Main Authors: Yang, Ke, Wang, Yongjian, Fan, Shidong, Mosleh, Ali
Format: Article
Language:English
Subjects:
Algorithms
Classification
collaborative maintenance
Critical components
Data envelopment analysis
Decision making
Decision trees
deep learning
Fuzzy logic
Image classification
Inventory management
Logistics
Maintenance
Multiple criterion
Neural networks
Optimization
Spare parts
Structural hierarchy
Support vector machines
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Summary:Spare parts management is a critical issue in the industrial field, alongside planning maintenance and logistics activities. For accurate classification in particular, the decision-makers can determine the optimal inventory management strategy. However, problems such as criteria selection, rules explanatory, and learning ability arise when managing thousands of spare parts for modern industry. This paper presents a deep convolutional neural network based on graph (G-DCNN) which will realize multi-criteria classification through image identification based on an explainable hierarchical structure. In the first phase, a hierarchical classification structure is established according to the causal relationship of multiple criteria; in the second phase, nodes are colored according to their criteria level status so that the traditional numerical information can be visible through graph style; in the third phase, the colored structures are transferred into images and processed by structure-modified convolutional neural network, to complete the classification. Finally, the proposed method is applied in a real-world case study to validate its effectiveness, feasibility, and generality. This classification study supplies a good decision support to improve the monitor-focus on critical component and control inventory which will benefit the collaborative maintenance.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11157088