In the car industry, companies want to use the smallest number of different kinds of components in a series of cars and retain high design freedom of shared components. This thesis will quantify these two goals and look at this problem, named commonality optimization, from two perspectives: One directly as a bi-objective optimization and one as a bi-level optimization. For the former, Rook model is used to encode the configuration. Then Iterative Elitist Genetic Algorithm (IEGA), Niched Elitist Genetic Algorithm (NEGA) and Niched Pareto Genetic Algorithm-II (NPGA-II) are introduced for solving the problem. As to the latter, this thesis focus on the lower level of the bi-level optimization. For it, dynamic objective genetic algorithm (DOGA) and Monte Carlo Tree Search(MCTS) method is used after mapping the optimization problem into a deepest node search problem. Those algorithms are tested in two small cases and observed that NSGA-II and MCTS with the Hasse diagram as its underlying model (MCTS-HD) are the most suitable algorithms for industrial use. In the end, the result of implementing them in a real case is shown, in which it is impossible to enumerate all possible solutions to find the optimum.    «

In the car industry, companies want to use the smallest number of different kinds of components in a series of cars and retain high design freedom of shared components. This thesis will quantify these two goals and look at this problem, named commonality optimization, from two perspectives: One directly as a bi-objective optimization and one as a bi-level optimization. For the former, Rook model is used to encode the configuration. Then Iterative Elitist Genetic Algorithm (IEGA), Niched Elitist...    »