An Assembly Sequence Planning Method Based on Discrete Difference Genetic Algorithm

The assembly sequence of products directly affects the assembly quality and assembly cost of products. Artificial planning assembly sequence can no longer meet the increasingly complex requirements of assembly. More and more intelligent algorithms are applied to the field of sequence planning. In order to improve product assembly efficiency, the paper proposes an assembly sequence planning method based on the discrete difference genetic algorithm. Differential evolution algorithm is a heuristic search algorithm that can be programmed with real numbers and has a simple structure and is easy to implement. According to the characteristics of assembly sequence planning, the operations of variation, crossover and mutation were redefined, and the fitness function model with the feasibility, stability, assembly direction change times and assembly tool change times as the evaluation index was established. The algorithm is analyzed by the vise assembly example. The feasibility and stability of the algorithm are verified. The comparison with genetic algorithm proves: the method is more efficient; the convergence speed is faster; the quality and quantity dependence of the initial population is smaller.

[1]  BBVL Deepak,et al.  Assembly sequence planning using soft computing methods: A review , 2019 .

[2]  S. G. Ponnambalam,et al.  Energy efficient modeling and optimization for assembly sequence planning using moth flame optimization , 2019, Assembly Automation.

[3]  Yan Cao,et al.  Assembly sequence planning method based on particle swarm algorithm , 2017, Cluster Computing.

[4]  Simeng Liu,et al.  Assembly sequence planning for reflector panels based on genetic algorithm and ant Colony optimization , 2016, The International Journal of Advanced Manufacturing Technology.

[5]  Lixin Tang,et al.  An Improved Differential Evolution Algorithm for Practical Dynamic Scheduling in Steelmaking-Continuous Casting Production , 2014, IEEE Transactions on Evolutionary Computation.

[6]  G. Štumberger,et al.  Differential Evolution-Based Identification of the Nonlinear Kaplan Turbine Model , 2014, IEEE Transactions on Energy Conversion.

[7]  Yanfeng Xing,et al.  Assembly sequence planning based on a hybrid particle swarm optimisation and genetic algorithm , 2012 .

[8]  Ning Wang,et al.  A novel hybrid differential evolution approach to scheduling of large-scale zero-wait batch processes with setup times , 2012, Comput. Chem. Eng..

[9]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[10]  Jianhua Ma,et al.  Research on Assembly Sequence Planning Based on Firefly Algorithm , 2013 .

[11]  Jing He,et al.  A novel differential evolution algorithm for joint replenishment problem under interdependence and its application , 2012 .

[12]  Fengchan Wang,et al.  Multi Station Assembly Sequence Planning Based on Particle Swarm Optimization Algorithm , 2012 .

[13]  Li Na,et al.  Multi Station Assembly Sequence Planning Based on Particle Swarm Optimization Algorithm , 2012 .

[14]  Xing Li Assembly sequence planning based on correlation function for complex product , 2011 .

[15]  Yuan Hui,et al.  Assembly Sequence Planning Based on Particle Swarm Optimization Algorithm for Complex Product , 2010 .

[16]  Li Yuan,et al.  Assembly sequence planning optimization for aircraft assembly based on GA , 2006 .