Assembly sequence planning of complex products is apt to get into a hobble of combinatorial explosion because of large numbers of parts. Contrasted with the conventional methods, intelligent algorithms reveal their advantages in throwing off the vexatious situations. This paper presents a particle swarm optimization (PSO) approach to tackle the generation of optimal assembly sequences of complex products. Six kinds of assembly process constraints related to the assembly cost are analyzed at first. Then, an optimization model of assembly sequences is constructed. The mapping rules between the optimization model and the PSO model are clarified. The common operators in PSO algorithm are transformed into the velocity operators (VOs), which are used to adjust the orders of parts to generate the optimal assembly sequences. The proposed method is validated with an illustrative example and the solutions are compared with those obtained using simulated annealing (SA) algorithm under the same conditions.

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