製造系統的整合排程與決策方法之發展

Integrated Scheduling and Decision Making for Manufacturing Systems

陳恬穎(Tien-Ying Chen)

指導教授 ： 黃漢邦

國立臺灣大學/工學院/機械工程學研究所/碩士(2006年)

https://doi.org/10.6342/NTU.2006.10230

摘要

無資料

關鍵字

即時重排程系統 ； 知識管理 ； 決策支援系統

並列摘要

In this thesis, various data mining methods are integrated to construct the on-line rescheduling system and the decision support system for manufacturing environment.
For on-line rescheduling system, an interval variant rescheduling mechanism is proposed. In order to deploy different dispatching rules to different intrabays, k-means is used for clustering the intrabays of the fab. Then genetic algorithm (GA) is employed for searching dispatching rule sets which promote better performance. In terms of the system conditions corresponding to dispatching rules, features can be extracted through generalized discriminant analysis (GDA) and two kinds of classifiers, KNN (K-Nearest Neighbors) classifier and SVM (Support Vector Machine) classifier, are constructed as schedulers. In addition, the ANFIS (Adaptive Neuro-Fuzzy Inference System) prediction model is built for the sake of on-line deciding the scheduling intervals. The experiment results indicate that applying the proposed mechanism to obtaining dispatching strategies is an effective method considering the complexity and variation of semiconductor wafer fabrication systems.
The decision support system communicates with the two fab models (SEMATECH model and TRC model) and contains three subsystems. They are rush order handling subsystem, diagnosis and maintenance subsystem, and knowledge management subsystem. In particular, the methods for knowledge extraction, learning, and update are provided. Also, four scenarios are provided to support decision making. The first offers decision makers to decide product mix ratio with the concept of TOC (Theory of Constraints). The second can control job arrival rate through the monitoring of WIP (Work In Progress). And then the third one decides dispatching rules in terms of the knowledge. The last scenario aids preventive maintenance with the information of each machine’s PM schedule. All these scenarios are through web pages to achieve knowledge sharing.

並列關鍵字

On-Line Rescheduling ； Knowledge Management ； Decision Support System

參考文獻

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