基于能耗的柔性作业车间调度多目标优化算法

Multi-objective optimization algorithm for flexible job shop scheduling based on energy consumption

针对柔性作业车间调度问题中的约束条件,考虑到低碳排放是制造业急需解决的问题,构建了一种基于最大完成时间和最大能耗的数学模型,提出一种改进的多目标优化算法。首先,在传统的NSGA-Ⅱ算法中融入粒子群算法的思想,提高解集的搜索能力;其次,将机器和工序部分进行分层编码,保证解集的合法性;然后,使用一种改进的密度估计方法计算平均距离,保证解集的分布性。为了验证算法的有效性,使用mk01~mk07标准测试数据对NSGA-Ⅱ算法及改进的多目标优化算法进行对比实验。结果显示,改进后算法得到的Pareto最优解集在解的多样性及收敛性方面优于传统多目标算法。

In view of the constraints in the flexible job shop scheduling problem,an urgent issue of low carbon emission to be solved in the manufacturing industry is considered,a mathematical model based on maximum completion time and maximum energy consumption is constructed,and an improved multi-objective optimization algorithm is proposed.The particle swarm optimization is integrated into the traditional NSGA-Ⅱalgorithm in order to improve the search ability of solution set.The parts of machine and process are subjected to hierarchical coding to ensure the validity of the solution set.An improved density estimation method is used to calculate the average distance to ensure the distribution of the solution set.In order to validate the validity of the algorithm,mk01~mk07 standard test data is used to perform contrast experiments to make comparison between the NSGA-Ⅱalgorithm and the improved multi-objective optimization algorithm.The results show that the Pareto optimal solution set obtained by the improved algorithm is superior to that by the traditional multi-objective algorithm in terms of diversity and convergence of solution.