Importance Degree Evaluation of Spare Parts Based on Clustering Algorithm and Back-Propagation Neural Network
المؤلفون المشاركون
Zhang, Qing
Zhao, Jiang-bin
Zhang, Shoujing
Qin, Xiaofan
Hu, Sheng
Dong, Bochao
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-22
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1196556
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر