Production Planning Decision Based on the Optimized Fuzzy Time-series Clustering
Dynamic market uncertainties lead to the complexity of production planning in semiconductor manufacturing factory.Interrelationships instead of independency among different products planning significantly impact the decision-making process and results. Moreover, such interaction-based production planning almost stays at quantitatively study. To well characterize such interactions caused by market dynamic uncertainties, clustering method is proposed for quantitative analysis, which paves the way for a low risk production planning. This paper presents an optimized Fuzzy Short Time-series (FSTS) Clustering method to study the tendency of production data, where Fuzzy Subtractive Clustering is introduced to identify the categories numbers, Genetic Algorithm (GA) is applied to confirm the initialization of fuzzy central matrix, and Weight-FSTS Clustering is employed for better trend description. Numerical experimental data in a semiconductor manufacturing factory show the feasibility and effectiveness of this optimized FSTS Clustering method.
Production planning Classification Clustering analysis FSTS Optimization
Junping Li Bo Li Limei Xu Shamin A Shirodkar
Institute of Astronautics & Aeronautics University of Electronic Science and Technology of China Che Planning department Intel Products (Chengdu) Ltd Chengdu 611731, Sichuan Province, China
国际会议
2007 IEEE International Conference on Automation and Lofistics
山东济南
英文
2007-08-18（万方平台首次上网日期，不代表论文的发表时间）