A concurrent hierarchical evolution approach to assembly process planning

A genetic algorithm (GA) is a kind of search algorithm based on probability, which imitates the species evolution mechanism in nature, and has obvious advantages in dealing with the optimization of complex systems. In fact, assembly sequence planning is a combinatorial optimal problem with intensive geometric and physical constraints. This paper presents a gene-group-based evolution approach to optimize assembly processes rather than merely assembly sequences. A compound chromosome encode is constructed to represent the abundant assembly process information, several specific genetic operators are used to generate offspring individuals, and an integrated interference matrix is built to determine whether an assembly process is feasible geometrically. Furthermore, a new fitness function of the genetic environment for assembly cost minimization is given and used to select the next generation of individuals. In addition, the performance of different selection mechanisms is analysed. Finally, the proposed approach is applied to a hypothetical assembly and a practical controller assembly to prove its validity.