Application of memetic algorithm in assembly sequence planning

Assembly sequence planning (ASP) plays an important role in the product design and manufacturing. A good assembly sequence can help to reduce the cost and time of the manufacturing process. However, ASP is known as a classical hard combinatorial optimization problem. With the increasing of the quantity of product components, ASP becomes more difficult and the traditional graph-based algorithm cannot solve it effectively. In this paper, the memetic algorithm (MA), which has been successfully applied in many areas, is used to solve the ASP problem. MA combines the parallel global search nature of evolutionary algorithm with local search to improve individual solutions. It can balance global search ability and local search ability very well. To improve the optimization performance of the approach, efficient genetic representation and operator schemes have been developed. To verify the feasibility and performance of the proposed approach, case study has been conducted and comparison has been made between memetic algorithm and genetic algorithm. The result shows that the proposed approach has achieved significant improvement.