Research on cutting method of wood panels based on improved particle swarm algorithm
Wood-based panels is widely used in the furniture industry and the research on optimized cutting patterns for saving materials and energy has become a scientific research hotspot.The cutting patterns of wood panels have re-ceived increasing attention.At present,many furniture companies still use manual cutting pattern to produce furniture panel parts,which often takes a long time and causes serious waste of materials.Due to the diversity of customer needs,it is necessary to arrange a reasonable cutting plan to make full use of the wood-based panels,improving the economic efficiency of the enterprise and save natural resources.The optimization of the panel sawing pattern is a two-dimensional rectangular blanking problem with high computational complexity.This study mainly focused on the blan-king of rectangular parts for single-size wood-based panels.In the case that the length and width of panels were greater than the length and width of parts,the standard particle swarm optimization(PSO)algorithm,variable neighborhood search algorithm and particle swarm optimization(PSO)hybrid variable neighborhood search algorithm were used to solve the problem by establishing a two-dimensional mathematical model of blanking.Cutting examples were analyzed and calculated based on the data collected from several furniture enterprises.Firstly,the standard particle swarm algo-rithm was used to solve the single-size panel blanking problem.Secondly,the variable neighborhood search algorithm was used to solve the single-size panel blanking problem.To obtain the local optimal solution,the neighborhood struc-ture was changed,and then local search was performed to find new local optimal solution.This process was repeated until the termination condition of the algorithm was met.Finally,the particle swarm variable neighborhood search hy-brid algorithm was used to solve the single-size plank blanking problem.Because the particle swarm algorithm has the drawbacks of poor localization ability,premature convergence and large tolerance,the idea of variable neighborhood search was integrated into the particle swarm algorithm to make the result closer to the global optimum.The results showed that,compared with the particle swarm optimization(PSO)and variable neighborhood search algorithm,the hybrid algorithm of the particle swarm optimization(PSO)significantly improved the utilization rate of wood-based panels for the furniture enterprises,enhancing the economic benefits for these enterprises.
国家科技期刊平台
NSTL
万方数据
维普
