不同支配关系的NSGA-III算法在机器人制造单元调度问题中的应用

随着经济的发展,机器人制造单元对制造行业的生产效率和生产质量有很大提高.相对于传统的柔性制造单元,带机器人搬运的车间的调度问题还考虑了加工物料的搬运环节.因此,生产调度所面临的问题越来越复杂.针对Pareto支配关系在高维多目标优化中的支配能力不足,本文将Lorenz支配和CDAS支配分别与NSGA-III算法相结合,并首次应用到带机器人制造单元的高维多目标车间调度问题上来.考虑到现代生产过程的复杂化,本文提出对最大完工时间、加工总能耗、交货期提前量、延迟量、生产总成本等多个目标同时进行优化,用于确定机器人工作时操作状态和搬运顺序,提高生产效率.通过实验发现基于Lorenz支配和CDAS支配的NSGA-III算法在该生产调度问题上比传统的NSGA-III在解的收敛性和均匀性上表现更优.

Application of NSGA-III Algorithm Based on Different Dominance Relations in Robotic Cell Scheduling Problem

With the development of economy, robotic of economy, robotic cells have greatly improved the production efficiency and quality of the manufacturing industry. Compared with that of the traditional flexible manufacturing cells, the scheduling problem of shops with robot handling also involves material handling. As a result, the production scheduling problem is becoming increasingly complex. In view of the lack of dominance of the Pareto dominance relation in high-dimensional multi-objective optimization, the Lorenz domination and CDAS domination are combined, respectively, with the non-dominated sorting genetic algorithm-III (NSGA-III) algorithm in this study and applied for the first time to the high-dimensional multi-objective scheduling of shops with robotic cells. Considering the complexity of modern production processes, this study proposes to optimize multiple objectives, such as maximum completion time, total processing energy consumption, delivery lead time, delivery delay time, and total production cost, at the same time to determine the operating state and handling sequence of the robot and improve production efficiency. Experiments show that on the abovementioned production scheduling problem, the NSGA-III algorithm based on Lorenz domination or CDAS domination performs better than the traditional NSGA-III algorithm in the solution convergence and uniformity.