摘要
针对考虑运输时间的柔性作业车间调度问题,构建了以最大完工时间最小化、最大机器负载最小化和总机器负载最小化为目标的多目标优化模型,并提出一种小生境粒子群优化算法求解该模型.首先,在粒子群的初始化过程中动态选择完工时间最短的机器,提高初始解的质量,加快算法的收敛速度.其次,针对传统粒子群算法的不稳定性和早熟问题,引入邻域搜索算法增强粒子群算法的局部搜索能力,提出一种无须共享半径的小生境技术,将该小生境技术与粒子群算法相结合避免了粒子群算法的早熟问题.最后,通过实验验证小生境粒子群算法在求解考虑运输时间的多目标FJSP的可行性和有效性.
In order to solve the flexible job-shop scheduling problem considering transportation time,a multi-objective optimization model with the objectives of minimizing the maximum completion time,minimizing the maximum machine load and minimizing the total machine load was constructed,and a niche particle swarm optimization algorithm was proposed to solve the model.Firstly,the machine with the shortest completion time is selected dynamically in the process of particle swarm initialization to improve the quality of the initial solution and accelerate the convergence speed of the algorithm.Secondly,in view of the instability and precocity of traditional particle swarm optimization(pso),neighborhood search algorithm is introduced to enhance the local search capability of pso,and a new niche technology without sharing radius is proposed,which is combined with pso to avoid the precocity problem of pso.Finally,the feasibility and effectiveness of the niche particle swarm optimization algorithm in solving multi-objective FJSP considering transport time are verified by experiments.
作者
陈魁
毕利
CHEN Kui;BI Li(School of Information Engineering,Ningxia University,Yinchuan 750021,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2021年第5期946-952,共7页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(61662058)资助
西部一流大学科研创新项目(ZKZD2017005)资助.
关键词
柔性作业车间调度
小生境技术
粒子群算法
运输时间
多目标优化
flexible job shop scheduling problem
niche technology
particle swarm optimization
transportation time
multiobjective optimization
