Genetic Algorithms (GAs) are powerful tools to solve large scale design optimization problems. The research interests in GAs lie in both its theory and application. On one hand, various modifications have been made on early GAs to allow them to solve problems faster, more accurately and more reliably. On the other hand, GA is used to solve complicated design optimization problems in different applications. The study in this thesis is both theoretical and applied in nature. On the theoretical side, an improved GA�Evolution Direction Guided GA (EDG-GA) is proposed based on the analysis of Schema Theory and Building Block Hypothesis. In addition, a method is developed to study the structure of GA solution space by characterizing interactions between genes. This method is further used to determine crossover points for selective crossover. On the application side, GA is applied to generate optimal tolerance assignment plans for a series of manufacturing processes. It is shown that the optimal tolerance assignment plan achieved by GA is better than that achieved by other optimization methods such as sensitivity analysis, given comparable computation time.

Quality control (QC) plan is an important component of manufacturing planning for mass customization. QC planning is to determine the operational tolerances and the way to control process variation for assuring the production quality against design tolerances. It includes four phases, i.e., tolerance stack-up analysis, tolerance assignment, in-process inspection design, and the procedure of error source diagnosis & process control. Previous work has been done for tolerance stack-up modeling based on the datum-machining surface relationship graph (DMG), machining error analysis, and worst-case/statistical method. In this research, the tolerance stack-up analysis is expanded with a Monte-Carlo simulation for solving the tolerance stack-up problem within multi-setups. Based on the tolerance stack-up model and process capability analysis, a tolerance assignment method is developed to determine the operation tolerance specifications in each setup. Optimal result is achieved by using tolerance grade representation and generic algorithm. Then based on a process variation analysis, a platform is established to identify the necessity of in-process inspection and design/select the inspection methods in quality control planning. Finally a general procedure is developed to diagnose the error sources and control the process variation based on the measurements.