学习驱动的分布式异构混合流水车间批量流能效调度优化

本文研究了分布式异构混合流水车间批量流能效调度问题, 其中每个工厂的加工效率不同, 工件可以分割成若干子批进入加工系统. 以最大完成时间和总能耗为优化目标, 建立了混合整数规划模型. 本文提出了一种学习驱动的多目标进化算法, 包括学习驱动的全局搜索和局部搜索. 引入Q学习作为学习引擎, 以种群和非支配解集的评价作为环境反馈信号, 通过不断的学习来动态指导搜索操作的选择; 基于问题特征, 设计了算法的状态集、动作集和奖励机制. Q学习的引入能够及时感知当前搜索的状态, 减少搜索操作的盲目性, 提高搜索的效率. 通过对仿真数据集的测试, 表明所提出算法能够有效地求解分布式异构混合流水车间批量流能效调度问题.

Learning-driven optimization of energy-efficient distributed heterogeneous hybrid flow shop lot-streaming scheduling

This paper studies an energy-efficient distributed heterogeneous hybrid flow shop lot-streaming scheduling problem, where the processing efficiency of each factory is different and the jobs can be split into several sub-lots to access the manufacturing system. The mixed integer programming model is built with the makespan and total energy consumption objectives. A learning-driven multi-objective evolutionary algorithm is proposed, which includes learning-driven global search and local search. Q-learning is introduced as a learning engine, and the evaluation of population and non-dominated solution sets is used as an environmental feedback signal to dynamically guide the selection of search operations through continuous learning. Based on the characteristics of the problem, the state set, action set and reward mechanism of the algorithm are designed. The introduction of Q-learning can sense the current search state in time, reduce the blindness of search operations, and improve the efficiency of search. From the testing results on simulation data set, it is shown that the proposed algorithm can effectively solve the energy-efficient distributed heterogeneous hybrid flow shop lot-streaming scheduling problem.