控制理论与应用2024，Vol.41Issue(6)：1018-1028,11.DOI:10.7641/CTA.2023.20633
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
Learning-driven optimization of energy-efficient distributed heterogeneous hybrid flow shop lot-streaming scheduling
摘要
Abstract
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.关键词
分布式异构混合流水车间/批量流调度/学习驱动的多目标进化算法/整数规划/能效优化Key words
distributed heterogeneous hybrid flow shop scheduling/lot-streaming scheduling/learning-driven multi-objective evolutionary algorithm/integer programming/energy-efficiency optimization引用本文复制引用
邵炜世,皮德常,邵仲世..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[J].控制理论与应用,2024,41(6):1018-1028,11.基金项目
国家自然科学基金项目(62003203,62103195,62262018),江苏省基础研究计划项目(BK20210558),中国博士后基金面上项目(2021M701700,2023M732166),中央高校基本业务费项目(GK202201014),大规模复杂系统数值模拟教育部重点实验室开放基金项目(202404)资助.Supported by the National Natural Science Foundation of China(62003203,62103195,62262018),the Jiangsu Natural Science Foundation(BK20210558),the China Postdoctoral Science Foundation Funded Project(2021M701700,2023M732166),the Fundamental Research Funds for the Center Universities(GK202201014)and the Open Project Fund of Key Laboratory of Numerical Simulation for Large Scale Complex Systems,Ministry of Education,China(202404). （62003203,62103195,62262018）
