標題: Design of BOM configuration for reducing spare parts logistic costs
作者: Wu, Muh-Cherng
Hsu, Yang-Kang
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: bill of material;spare parts;stocking policy;genetic algorithm;neural network
公開日期: 4-五月-2008
摘要: This paper proposes an approach to reduce the total operational cost of a spare part logistic system by appropriately designing the BOM (bill of material) configuration. A spare part may have several vendors. Parts supplied by different vendors may vary in failure rates and prices - the higher the failure rate, the lower the price. Selecting vendors for spare parts is therefore a trade-off decision. Consider a machine where the BOM is composed of s critical parts and each part has k vendors. The number of possible BOM configurations for the machine is then k(s). For each BOM configuration, we can use OPUS10 (proprietary software) to calculate an optimum inventory policy and its associated total logistic cost. Exhaustively searching the solution space by OPUS10 can yield an optimal BOM configuration; however, it may be formidably time-consuming. To remedy the time-consuming problem, this research proposes a GA-neural network approach to solve the BOM configuration design problem. A neural network is developed to efficiently emulate the function of OPUS 10 and a GA (genetic algorithm) is developed to quickly find a near-optimal BOM configuration. Experiment results indicate that the approach can obtain an effective BOM configuration efficiently. (c) 2007 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2007.04.001
http://hdl.handle.net/11536/9349
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2007.04.001
期刊: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 34
Issue: 4
起始頁: 2417
結束頁: 2423
顯示於類別:期刊論文


文件中的檔案:

  1. 000253521900019.pdf

若為 zip 檔案,請下載檔案解壓縮後,用瀏覽器開啟資料夾中的 index.html 瀏覽全文。