中文摘要：

针对阻塞流水车间调度问题（BFSP）,提出了一种新颖的量子差分进化（NQDE）算法,用于最小化最大完工时间。该算法将量子进化算法（QEA）与差分进化（DE）相结合,设计一种新颖的量子旋转机制控制种群进化方向,增强种群多样性;采用高效的基于变邻域搜索的量子进化算法（QEA-VNS）协同进化策略增强算法的全局搜索能力,进一步提高解的质量。基于Taillard＇s benchmark实例仿真,结果表明,所提算法在最优解数量上明显高于目前较好的启发式算法——INEH,改进了110个实例中64个实例的当前最优解;在性能上也优于目前有效的元启发式算法——新型蛙跳算法（NMSFLA）和混合量子差分进化（HQDE）,产生最优解的平均百分比偏差（ARPD）均下降约6%。NQDE算法适合大规模阻塞流水车间调度问题。

英文摘要：

A Novel Quantum Differential Evolutionary （NQDE） algorithm was proposed for the Blocking Flowshop Scheduling Problem （BFSP） to minimize the makespan. The NQDE algorithm combined Quantum Evolutionary Algorithm （QEA） with Differential Evolution （DE） algorithm, and a novel quantum rotating gate was designed to control the evolutionary trend and increase the diversity of population. An effective Quantum-inspired Evolutionary Algorithm-Variable Neighborhood Search （QEA-VNS） co-evolutionary strategy was also developed to enhance the global search ability of the algorithm and to further improve the solution quality. The proposed algorithm was tested on the Taillard＇s benchmark instances, and the results show that the number of optimal solutions obtained by NQDE is bigger than the current better heuristic algorithm--Improved Nawaz-Enscore-Ham Heuristic （INEH） evidently. Specifically, the optimal solutions of 64 instances in the 110 instances are improved by NQDE. Moreover, the performance of NQDE is superior to the valid meta-heuristic algorithm--New Modified Shuffled Frog Leaping Algorithm （NMSFLA） and Hybrid Quantum DE （HQDE）, and the Average Relative Percentage Deviation （ARPD） of NQDE algorithm decreases by 6% contrasted with the latter ones. So it is proved that NQDE algorithm is suitable for the large scale BFSP.