不同支配关系的多目标算法的柔性作业调度

Flexible Job Scheduling Based on Multi-objective Algorithms with Different Dominance Relationship

为了提高多目标进化算法所获得解的质量,研究者做了大量的研究,传统的基于Pareto支配关系的多目标进化算法具有一定的局限性;论文以不同的支配关系与NSGA-II算法相结合,对单机器人搬运的柔性作业车间调度的多目标优化问题进行求解,通过实验比较分析了不同方法在多目标优化问题求解中的优劣性;在此以NSGA-II为框架结合Lorenz支配关系和CDAS(control dominance area of solutions)支配关系以及传统的基Pareto支配关系的NSGA-II3种算法去研究同一优化调度问题,发现基于Lorenz支配关系和CDAS支配关系的优化算法比基于传统的Pareto支配关系的优化算法的效果更佳。

In order to improve the quality of the solution obtained by the multi-objective evolutionary algorithm,researchers have done a lot of research.The traditional multi-objective evolutionary algorithm based on the Pareto dominance relationship has certain limitations.This paper combines different dominance relationships with NSGA-II(Non-dominated Sorting Genetic Algorithm)algorithm,the multi-objective optimization problem of flexible job shop scheduling with single robot handling is solved,and different methods are used to analyze multi-objective optimization problems through experimental comparison and analysis.Pros and cons in solving.Here,using NSGA-II as the framework to combine Lorenz dominance relationship and CDAS(Control Dominance Area of Solutions)dominance relationship and traditional NSGA-II algorithms based on traditional Pareto dominance relationship to study the same optimal scheduling problem,and found that based on Lorenz dominance relationship and CDAS dominance relationship optimization algorithm performs better than the traditional Pareto dominance relationship optimization algorithm.