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

被引：0
|
作者：
Shao W.-S. [1 ,2 ,3 ,4 ]
Pi D.-C. [1 ]
Shao Z.-S. [3 ]
机构：
[1] College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangsu, Nanjing
[2] School of Computer and Electronic Information, School of Artificial Intelligence, Nanjing Normal University, Jiangsu, Nanjing
[3] School of Computer Science, Shaanxi Normal University, Shaanxi, Xi’an
[4] Jiangsu Engineering Research Center on Information Security and Privacy Protection Technology, Jiangsu, Nanjing
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2024年 / 41卷 / 06期
基金：
中国国家自然科学基金;
关键词：
distributed heterogeneous hybrid flow shop scheduling; energy-efficiency optimization; integer programming; learning-driven multiobjective evolutionary algorithm; lot-streaming scheduling;
D O I：
10.7641/CTA.2023.20633
中图分类号：
学科分类号：
摘要：
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. © 2024 South China University of Technology. All rights reserved.
引用
收藏
页码：1018 / 1028
页数：10
相关论文
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