中文摘要：

针对NP-完全的无等待流水作业调度问题，改变传统求解调度序列目标函数的模式，分析并证明启发式算法基本算子的目标增量性质，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．提出将变化邻域搜索（VNS）作为一种局部搜索机制混合入遗传算法的智能算法IGA求解所考虑的问题，根据问题特点构造ISG算法产生初始种群中的一个个体，设计基于期望值的个体选择机制和进化过程交叉算子ILCS．采用110个经典Benchmark实例，将所提出的IGA算法与传统遗传算法以及求解该问题目前最好的2种算法进行比较，实验结果表明IGA算法在略有耗时的情况下，性能上明显优于其他3种算法、

英文摘要：

To solve the NP-complete no-wait flowshop problems, objective increment properties are analyzed and proved for fundamental operations of heuristics. With these properties, whether a new generated schedule is better or worse than the original one is only evaluated by objective increments, instead of completely calculating objective values as the traditional algorithms do, so that the computational time can be considerably reduced. An objective increment-based hybrid genetic algorithm （IGA） is proposed by integrating the genetic algorithm （GA） with an improved various neighborhood search （VNS）as a local search. An initial solution generation heuristic（ISG） is constructed to generate one individual of the initial population. An expectation value-based selection mechanism and a crossover operator are introduced to the mating process. The IGA is compared with the traditional GA and two best-so-far algorithms for the considered problem on 110 benchmark instances. An experimental results show that the IGA outperforms the others in effectiveness although with a little more time consumption.