摘要： 面向多工艺式布局车间，针对不同工艺之间存在共有工序会导致资源浪费的问题，建立以最小化最大完工时间、最小化总加工成本以及最小化总加工能耗为目标的多车间作业任务协同调度多目标优化模型，旨在提高车间资源利用率，实现降本增效。提出一种新的禁忌搜索与快速非支配排序遗传融合算法TSNSGA-Ⅱ，将遗传算法交叉后的染色体采用禁忌搜索变异策略产生新个体，增强搜索空间的探索能力，最后采用层次分析法从工厂角度权衡三个目标以选择最优调度方案。实验部分首先在模拟数据集上验证了TSNSGA-Ⅱ算法的有效性，然后在不同规模的标准数据集上对比了TSNSGA-Ⅱ算法与MOGWO、ENSGA-Ⅱ元启发式算法的性能，并与单独的NSGA-Ⅱ和单独的TS模块进行消融对比。结果表明，该算法在以总加工成本为最高优先级时，该算法在Brandimarte中90%的mk数据集上都获得了最低的总加工成本，与ENSGA-Ⅱ算法相比求解时间更短，与改进前的NSGA-II算法相比提高了1.6%；在以最大完工时间为最高优先级的情况下，该算法在80%数据集上获得了最小的最大完工时间，与改进前的NSGA-Ⅱ算法相比提高了2.2%。

Abstract: Aiming at the problem of resource waste caused by the existence of common operations between different processes in a multi-process layout workshop, a multi-objective optimization model for collaborative scheduling of multi-workshop job tasks is established with the goals of minimizing the makespan, minimizing the total processing cost, and minimizing the total processing energy consumption, so as to improve the utilization rate of workshop resources and achieve cost reduction and efficiency improvement. A new genetic fusion algorithm TSNSGA-Ⅱ, which combines tabu search and fast non-dominated sorting, is proposed. The chromosomes after the crossover of the genetic algorithm are used to generate new individuals using the tabu search mutation strategy to enhance the exploration ability of the search space. Finally, the hierarchical analysis method is used to weigh the three objectives from the perspective of the factory to select the optimal scheduling solution. The experimental part first verifies the effectiveness of the TSNSGA-Ⅱ algorithm on a simulated data set, and then compares the performance of the TSNSGA-Ⅱ algorithm with the MOGWO and ENSGA-Ⅱ metaheuristic algorithms on standard data sets of different sizes, and performs ablation comparison with a single NSGA-Ⅱ and a single TS module. The results show that when the total processing cost is the highest priority, the algorithm obtains the lowest total processing cost on 90% of the mk data sets in Brandimarte, and the solution time is shorter than that of the ENSGA-Ⅱ algorithm, which is 1.6% higher than that of the NSGA-II algorithm before improvement. When the makespan is the highest priority, the algorithm obtains the minimum makespan on 80% of the data sets, which is 2.2% higher than that of the NSGA-Ⅱ algorithm before improvement.