文献类型：期刊文献

中文题名：改进粒子群算法在柔性车间调度问题的研究

英文题名：Research on Improved Particle Swarm Optimization Algorithm in Flexible Job-Shop Scheduling Problem

作者：曲鹏举 唐向红

第一作者：曲鹏举

机构：[1]贵州理工学院工程训练中心,贵州贵阳550000;[2]贵州大学现代制造教育部重点实验室,贵州贵阳550000

第一机构：贵州理工学院工程训练中心

年份：2024

期号：7

起止页码：227-231

中文期刊名：机械设计与制造

外文期刊名：Machinery Design & Manufacture

收录：CSTPCD;;北大核心:【北大核心2023】；

基金：贵州省教育厅青年科技人才成长项目(黔教合KY字[2018]243)。

语种：中文

中文关键词：改进粒子群算法;柔性作业车间调度;前景理论;惯性权重幂函数;加工时间

外文关键词：Improved Particle Swarm Optimization;FJSP;Prospect Theory;Inertia Weight Power Function;Processing Time

摘要：为了减少柔性作业车间调度问题加工时间,通过前景理论构建柔性作业车间加工满意度数学模型,以降低加工时间为目标,设计了一种惯性权重幂函数调节的改进粒子群算法(PPSO),该算法惯性权重能够幂函数自适应调节,学习因子能够发生相应的递减或递增变化平衡算法的全局探索和局部开发能力。选取BRdata与Kacem经典算例,通过与人工免疫算法(AIA)和整合模拟退火算法(ISA)的进行仿真比较,PPSO加工时间较短;在工件数J=25、可用机器数M=16、迭代次数600情况下比较BRdata算例,PPSO加工时间相较AIA、ISA算法分别缩短了21.61%和4.32%,验证结果表明PPSO算法在柔性车间调度问题中降低产品加工时间的有效性。
In order to reduce the processing time of the flexible job-shop scheduling problem(FJSP),a mathematical model of the processing satisfaction of the FJSP is constructed through the prospect theory,an improved particle swarm adjusted by inertia weight power function is designed to reduce the processing time,the inertia weight of the algorithm can be adaptively adjusted by the power function,and the learning factor can be decreased or increased accordingly to balance the global exploration and local development capabilities of the algorithm.Selecting the classical examples of BRdata and Kacem,and comparing the PPSO algorithm with the artificial immune algorithm(AIA)and the integrated simulated annealing algorithm(ISA),the processing time of PPSO is shorter;In the example of BRdata,when the number of work pieces is J=25,the number of available machines is M=16,and the number of iterations is 600,compared with the AIA algorithm and the ISA algorithm,the processing time of the PPSO is shortened by 21.61%and 4.32%respectively.The verification results show that the PPSO algorithm is effective in reducing processing time in the flexible job-shop scheduling problem.