纺织工程学报2025，Vol.3Issue(3)：88-96,9.
基于改进PSO算法的织机车间柔性作业调度方法研究
Research on flexible operation scheduling method in loom workshops based on improved PSO algorithm
摘要
Abstract
The scheduling issue in large-scale weaving machine workshop operations is a typical production planning and scheduling,which is a special form of workshop scheduling operation with high complexity.It in-volves how to reasonably allocate and arrange multiple looms to process different production tasks to optimize certain objectives while satisfying multiple constraints.Based on this,an adaptive multi objective hybrid parti-cle swarm optimization(AMOHPSO)algorithm is proposed to address the problem of the algorithm being prone to getting stuck in local optima.The algorithm integrates the crossover and mutation operations of Genet-ic Algorithm(GA)to enhance its performance and optimization capabilities.To address the issue of slow conver-gence,a hybrid local search and global search approach is combined to enable particle swarm optimization(PSO)to quickly explore the global space and finely search the local space in the later stages.For dealing with multi-objective scheduling problems,the MOPSO framework is adopted to improve the fitness calculation meth-od of PSO,enabling it to generate Pareto frontier solutions for multiple different objectives.The improved PSO algorithm in this article obtained a global optimal solution of 0.0501,which improved the optimization accuracy by 18.2%compared to genetic algorithm and by 10%compared to particle swarm algorithm;The experimental results show that the improved algorithm proposed has high solution accuracy and fast convergence speed when solving flexible operation scheduling problems.关键词
织机车间/改进PSO/车间调度问题/柔性作业/自适应多目标优化算法Key words
loom workshop/improved particle swarm optimization(PSO)/job shop scheduling problem(JSSP)/flexible operations/adaptive multi-objective optimization algorithm分类
机械制造引用本文复制引用
操宇,江维,聂骏杰,陈振,李红军..基于改进PSO算法的织机车间柔性作业调度方法研究[J].纺织工程学报,2025,3(3):88-96,9.基金项目
数字化纺织装备湖北省重点实验室开放课题资助项目(DTL2023013). （DTL2023013）
