In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an artificial neural network (ANN) prediction method based on phase space reconstruction (PSR) and ant colony optimization (ACO) is presented,in which the phase space reconstruction theory is used to reconstruct the daily throughput time series,the ANN is used to construct the daily throughput prediction model,and the ACO is used to train the connection weight and bias values of the neural network prediction model.Testing with factory operation data and comparing with the traditional method show that the proposed methodology is effective.
Due to the stubborn nature of dynamic job shop scheduling problem,a novel ant colony coordination mechanism is proposed in this paper to search for an optimal schedule in dynamic environment.In ant colony coordination mechanism,the dynamic job shop is composed of several autonomous ants.These ants coordinate with each other by simulating the ant foraging behavior of spreading pheromone on the trails,by which they can make information available globally,and further more guide ants make optimal decisions.The proposed mechanism is tested by several instances and the results confirm the validity of it.
