中文摘要：

针对流水车间中产品不存在缓冲区的多目标优化问题，研究了阻塞流水车间的最大完工时间和总流程时间的最小化问题，提出了一种多目标离散差分进化（Multi-objective Discrete Differential Evolution，MDDE）算法搜索Pareto最优调度解。MDDE的变异个体通过非支配解或当前解的邻域随机产生，实验个体通过交叉操作产生，而选择过程则设计为一种多目标选择策略。此外，算法还混合了一种基于插入的Pareto局部搜索方法。基于标准测试算例的数值仿真实验表明，MDDE算法获得的非支配解集在Inverted Generational Distance、Set Coverage和Hypervolume性能指标上均有较好的表现。

英文摘要：

This paper considers the problem of the multi-objective scheduling with makespan and total flow time minimizations for blocking flow shop. A multi-objective discrete differential evolution （MDDE） is proposed for searching alternative Pareto solutions,in which mutant individual is obtained by Pareto front solution or incumbent solution, and trial individual is generated by crossover operation while selection process is designed as a multi-objective selection strategy. Moreover,an insertion-based Pareto local search procedure is hybridized in this algorithm. The computational experiments on a bunch of instances for blocking flow shop show that the proposed algorithm can attain better non-dominated solution set in term of three performance measures,i, e. ,Inverted Generational Distance, Set Coverage,and Hypervolume.