Adaptive Lot/Equipment Matching Strategy and GA Based Approach for Optimized Dispatching and Scheduling in a Wafer Probe Center

• Main Authors: Shen, Yi-Shiuan, 沈怡瑄
• Other Authors: Fu, Li-Chen
• Format: Others
• Language: en_US
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/58576188335353360121

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 91 === In this thesis, we propose an Adaptive Lot/Equipment Matching Strategy to optimize dispatching in a wafer probe center. In this mechanism, we use a graphical and mathematical modeling tool — Colored-Timed Petri Nets (CTPN) to model the testing flow in the wafer probe center. Moreover, we apply the genetic algorithm to help this mechanism to obtain a near-optimal solution. The main model in this thesis includes not only the detailed machine behaviors but also the equipment capabilities and the routing of lots. With this CTPN model, we can simulate the production processes, and keep track of the equipment status and the lot conditions efficiently and precisely. In addition, different scheduling policies can be evaluated via simulations, and a superior policy will then be determined. In the dispatching phase, we first present the lot based selection scheme and the equipment based selection scheme. Each of these two schemes has its own advantages, but it also has some drawbacks. Therefore, we propose a new method — Pair Generation Mechanism, which will keep both advantages but will be free of either disadvantages. In other words, we can promise a dispatching strategy that can be more optimal in the sense that both the lot-based and equipment-based viewpoints will be taken into account when lot/equipment matching needs to be done. In this thesis, we further adopt another efficient algorithm — Auction Algorithm to help us to find out the optimal solution to the lot/equipment matching problem. Besides, some adaptive factors will also be applied in this phase. At last in the scheduling phase, we apply the genetic algorithm (GA) based approach to obtain a near-optimal solution to our scheduling problem. From our experiment results, the developed CTPN based Genetic Algorithm will yield a more efficient solution than several other schedulers, so that this GA scheduler can indeed satisfy the needs for a rapidly changing environment, such as the wafer probe center.