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
邵炜世 1皮德常 2邵仲世3
作者信息
- 1. 南京航空航天大学计算机科学与技术学院,江苏南京 211106;南京师范大学计算机与电子信息学院/人工智能学院,江苏南京 210023;江苏省信息安全保密工程中心,江苏南京 210023
- 2. 南京航空航天大学计算机科学与技术学院,江苏南京 211106
- 3. 陕西师范大学计算机科学学院,陕西西安 710119
- 折叠
摘要
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.
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
基金项目
国家自然科学基金项目(62003203)
国家自然科学基金项目(62103195)
国家自然科学基金项目(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)
中国博士后基金面上项目(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)
出版年
2024
国家科技期刊平台
NSTL
万方数据