中文摘要：

将NP-难的最小化最大完工时间无等待流水调度问题等价转化为最小化总空闲时间的问题,改变传统求解调度序列目标函数的模式,通过目标函数变化量判断新解的优劣,大大降低算法所需计算时间.分析启发式算法基本操作和进化算子的总空闲时间增量性质,设计基本总空闲时间增量法以快速评估新产生解的质量.提出混合遗传算法I HGA（increment based hybrid genetic algorithm）求解该问题,构造相应初始种群生成方法和进化算子,提出进化概率动态更新策略和种群收敛判断与再生机制;算法混合了迭代改进局部搜索以进一步提高解的质量.基于120个经典Benchmark实例,将I HGA与目前求解该问题的有效算法RAJ,GR,SA2,TSM和FCH进行比较.实验结果表明：I HGA在性能方面优于其他,计算效率方面优于SA2和TSM,略逊于GR,RAJ和FCH.

英文摘要：

No-wait flowshops with makespan minimization has been proved to be a kind of NP-hard combinatorial optimization problem.To solve this problem,it is equivalently transferred into the problem on total-idle-time minimization in this paper.Different from traditional methods in which objectives are completely computed for a new generated schedule,total-idle-time increment methods are presented in this paper.Whether a new schedule is better or worse than the original one is judged just by the total-idle-time increment,which can reduce computational time considerably.Total-idle-time increment properties are analyzed for fundamental operations of heuristics and evolutional operators.Based on the properties,a fundamental method is introduced for fast evaluating schedules.IHGA（increment based hybrid genetic algorithm）is proposed for the considered problem.In IHGA,the different initialization methods and evolution operators are constructed.The dynamic upgrade strategy on evolution operators＇ probability,the population convergence judgment and also regeneration mechanism are designed to improve the effectiveness of the algorithm.In addition,an iterative improvement procedure is integrated in IHGA as a local search method to further improve the eminent solution in population.IHGA is compared with the best-so-far algorithms RAJ,GR,SA2,TSM and FCH on 120 classical benchmark instances.Computational results show that IHGA outperforms the others in effectiveness.IHGA is better than SA2 and TSM while a little worse than GR,RAJ and FCH in efficiency.