摘要
置换流水线调度问题(Permutation Flow-shop Scheduling Problem,PFSP)作为流水线调度问题的子问题,实质是一个著名的组合优化问题,其已被证明了是NP完全问题中最困难的问题之一。带学习效应的PFSP问题是一种更符合实际问题的模型,为了更好地解决此问题,在此提出了一种混合遗传算法和粒子群算法的改进和声搜索算法。对CAR1问题及其学习型调度进行了仿真实验,结果表明所提算法的可行性和有效性。
Permutation flow-shop scheduling problem, as the sub-problem of pipeline scheduling, is essentially a wellknown problem of combination optimization. It has been proved to be one of the most difficult problems in the NP-complete problem. PFSP problems with learning effect is a model which is more corresponding to practical problems. In order to resolve this problem, an improved harmony search algorithm which is the combination of a hybrid genetic algorithm and particle swarm algorithm is proposed. By doing a simulation of the CAR1 problem and its learning schedule, it turns out that the proposed algorithm is feasible and effective.
出处
《计算机工程与应用》
CSCD
北大核心
2015年第11期261-265,共5页
Computer Engineering and Applications
基金
国家自然科学基金(No.71271138)
教育部人文社会科学规划基金项目(No.10YJA630187)
上海市教育委员会科研创新项目(No.12ZS133)
上海市一流学科项目(No.S1201YLXK)
关键词
改进和声搜索算法
置换流水线调度
学习效应
improved harmony search algorithm
Permutation Flow-shop Scheduling Problem（PFSP）
learning effect
