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 well-known 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.
展开▼
