High precision assembly processes using industrial robots require the process parameters to be tuned in order to achieve desired performance such as cycle time and First Time Through (FTT) rate. Some researchers proposed methods such as Design-of-Experiment (DOE) to obtain optimal parameters. However, these methods only discuss how to find the optimal parameters if the part and/or workpiece location errors are in a certain range. In real assembly processes, the part and/or workpiece location errors could be different from batch to batch. Therefore the existing methods have some limitations. In this paper, the parameter optimization process based on DOE with different part and/or workpiece location errors are investigated. Experimental results demonstrate that the optimal parameters for different initial conditions are different and larger initial part and/or workpiece location errors will cause longer cycle time. Therefore, in order to improve the assembly process performance, the initial part and/or workpiece location errors should be compensated first and the optimal parameters in production should be changed once the initial tool position is compensated. An online parameter optimization method is also proposed. Experiments were performed to validate the proposed method and the results show that the proposed method is very promising in reducing the cycle time in assembly processes.