机电工程技术2025，Vol.54Issue(10)：17-21,99,6.DOI:10.3969/j.issn.1009-9492.2025.10.004
基于改进粒子群优化算法的柔性车间作业调度研究
Research on FJSP Problem Based on Improved PSO
摘要
Abstract
An enhanced Particle Swarm Optimization(PSO)algorithm is presented aiming at the Flexible Job Shop Scheduling Problem(FJSP),with the primary objective of minimizing the maximum completion time.To overcome challenges such as slow convergence and susceptibility to local optima,an adaptive inertia weight mechanism and a crossover search process are introduced.These additions increase the diversity of solutions and the algorithm's ability to explore the global solution space.The improved PSO outperforms both the standard PSO and the Adaptive Genetic Algorithm(AGA)across various test cases,particularly excelling in small-scale scenarios.Experiments confirm its superior performance in reducing maximum completion times and improving convergence speed and optimization capabilities.The research underscores the improved PSO's potential as an effective and reliable solution for FJSP,offering substantial benefits for the efficiency and quality of scheduling in intelligent manufacturing.关键词
车间作业调度/柔性车间/粒子群优化算法/自适应惯性权重/交叉搜索Key words
job-shop scheduling/flexible workshop/particle swarm optimization algorithm/adaptive inertia weighting/cross search分类
计算机与自动化引用本文复制引用
屈新怀,万之栩,丁必荣,孟冠军..基于改进粒子群优化算法的柔性车间作业调度研究[J].机电工程技术,2025,54(10):17-21,99,6.基金项目
国家重点研发计划资助项目(2019YFB1705303) （2019YFB1705303）
