Assembly Sequence Planning Method Based On Particle Swarm Optimization And Its Application Research

As one of the best-known production scheduling problem, the purpose of the assembly sequence planning is to find the optimal sequence of processing jobs in the product assembly line, to save the product assembly cost, or shorten the assembly time. Assembly sequence planning has been proved to be a strongly NP-hard problem, and assembly sequence planning is a typical combinatorial explosion problem with the increase in the number of components in products.To derive an effective assembly sequence planning approach, a discrete particle swarm optimization (DPSO) algorithm is firstly proposed to solve the assembly sequence planning problem. To make the discrete particle swarm optimization algorithm effective for solving assembly sequence planning, some key technologies including a special coding method of the position and velocity of particles and corresponding operators for updating the position and velocity of particles are proposed and defined. The evolution performance of the discrete particle swarm optimization algorithm with different setting of control parameters is investigated. The comparison results between the discrete particle swarm optimization approach and the genetic algorithm approach are presented in this work, and the performance of the proposed discrete particle swarm optimization algorithm to solve the assembly sequence planning problem is verified through a case study.To further increase the performance of discrete particle swarm optimization algorithm for solving assembly sequence planning problem, a hybrid discrete particle swarm optimization and simulated annealing algorithm is proposed. The performance of the proposed hybrid discrete particle swarm optimization and simulated annealing algorithm (DPSO-SA) is compared with the existing discrete particle swarm optimization algorithm. Case study shows that the hybrid discrete particle swarm optimization and simulated annealing approach can be more efficient to generate optimal assembly sequences, and can significantly increase the search capability and perform better than the discrete particle swarm optimization algorithm.Finally, an enhanced connection support matrix is proposed considering the stability of the assembly process in practical assembly condition, to conclude more practical and feasible assembly sequences.