摘要
文中针对把最小化总流动时间作为基准(Fm|fmls,Splk,prmu|∑Cj)的流水车间序列依赖组调度问题(FSDGS),研究了一种新的粒子群优化算法(PSO)。并基于排序值(Ranked Order Value,ROV)开发了一种编码方案,这种方案能将PSO算法中粒子的连续位置值转化成作业和组排列。文中用了一种称为个体增益(IE)的邻域矩阵搜索策略来保证提高搜索的质量并在深度和广度上做出平衡。新算法的性能被拿来与当前文献中提到的已知最好的元启发式算法即蚁群算法(ACO)进行对比,基于常用测试测试问题,结果显示新算法性能较诸ACO算法更加优越。
A Particle Swarm Optimization （PSO） algorithm for a Flow Shop Sequence Dependent Group Scheduling （FSDGS） problem,with minimization of total flow time as the criterion （Fm｜fmls,Splk,prmu｜∑Gj）, is proposed in this research. An encoding scheme based on Ranked Order Value （ROV） is developed, which converts the continuous position value of particles in PSO to job and group permutations. A neighborhood search strategy, called Individual Enhancement （IE）, is fused to enhance the search and to balance the exploration and exploitation. The performance of the algorithm is compared with the best available meta-heuristic algorithm in literature, i.e. the Ant Colony Optimization （ACO） algorithm, based on available test problems. The results show that the proposed algorithm has a superior performance to the ACO algorithm.
出处
《电子设计工程》
2014年第10期10-13,共4页
Electronic Design Engineering
基金
河南省科技厅基础与前沿技术研究项目(142300410188)
关键词
成组调度
流水线调度
粒子群
序列依赖
group scheduling
flow shop scheduling
particle swarm
sequence dependent
