An assembly sequence optimization oriented small world networks genetic algorithm and case study

Abstract

Purpose The assembly sequence in the product assembly process has effect on the final product quality. To solve the assembly sequence optimization problem, such as rotor blade assembly sequence optimization, this paper proposes a small world networks-based genetic algorithm (SWN_GA) to solve the assembly sequence optimization problem. The proposed approach SWN_GA consists of a combination between the standard Genetic Algorithm and the NW Small World Networks. Design/methodology/approach The selection operation and the crossover operation are improved in this paper. The selection operation remains the elite individuals that have greater fitness than average fitness and reselects the individuals that have smaller fitness than average fitness. The crossover operation combines the NW Small World Networks to select the crossover individuals and calculate the crossover probability. Findings In this paper, SWN_GA is used to optimize the assembly sequence of steam turbine rotor blades, and the SWN_GA was compared with standard GA and PSO algorithm in a simulation experiment. The simulation results show that SWN_GA cannot only find a better assembly sequence which have lower rotor imbalance, but also has a faster convergence rate. Originality/value Finally, an experiment about the assembly of a steam turbine rotor is conducted, and SWN_GA is applied to optimize the blades assembly sequence. The feasibility and effectiveness of SWN_GA are verified through the experimental result.