Adaptive lot/equipment matching strategy and GA based approach for optimized dispatching and scheduling in a wafer probe center

In this paper, we use a graphical and mathematical modeling tool colored-timed Petri nets (CTPN) to model the testing flow in the wafer probe center. 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 the dispatching phase, we present the lot-based and the equipment-based selection schemes. Each of these two schemes has its own advantages, but also some drawbacks. Therefore, we propose a new approach pair generation mechanism and adaptive lot/equipment matching strategy, which can promise a dispatching strategy that can be more optimal in the sense that both lot-based and equipment-based viewpoints are taken into account simultaneously. In this paper, we further adopt an efficient algorithm auction algorithm to help us to find out the optimal solution to the internally generated lot/equipment matching problem. Besides, some adaptive factors are also applied. Lastly 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 yields a more efficient solution than several other schedulers.