A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem

doi:10.1109/TCYB.2022.3192112

	A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem
	Zhao, Fuqing1 ; Di, Shilu 1; Wang, Ling 2
	2023-05
发表期刊	IEEE Transactions on Cybernetics
ISSN	2168-2267
卷号	53 期号:5 页码:1-14
摘要	Carbon peaking and carbon neutrality, which are the significant national strategy for sustainable development, have attracted considerable attention from production enterprises. In this study, the energy consumption is considered in the distributed blocking flow shop scheduling problem (DBFSP). A hyperheuristic with $Q$ -learning (HHQL) is presented to address the energy-efficient DBFSP (EEDBFSP). $Q$ -learning is employed to select an appropriate low-level heuristic (LLH) from a predesigned LLH set according to historical information fed back by LLH. An initialization method, which considers both total tardiness (TTD) and total energy consumption (TEC), is proposed to construct the initial population. The $\varepsilon$ -greedy strategy is introduced to utilize the learned knowledge while retaining a certain degree of exploration in the process of selecting LLH. The acceleration operation of the job on the critical path is designed to optimize TTD. The deceleration operation of the job on the noncritical path is designed to optimize TEC. The statistical and computational experimentation in an extensive benchmark testified that the HHQL outperforms the other comparison algorithm regarding efficiency and significance in solving EEDBFSP. IEEE
关键词	Carbon Energy efficiency Heuristic algorithms Job shop scheduling Machine shop practice Planning > $Q$ -learning Blocking flow shops Distributed blocking flow shop scheduling Energy-consumption Flow-shop scheduling Heuristics algorithm Hyper-heuristics Index Job-Shop scheduling Optimisations Production facility Q-learning Total energy Total energy consumption Total tardiness Xmlns:mml=" Xmlns:xlink=" Xmlns:xsi="
DOI	10.1109/TCYB.2022.3192112
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000846431900001
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20223712722154
EI主题词	Energy utilization
EI分类号	525.2 Energy Conservation ; 525.3 Energy Utilization ; 604.2 Machining Operations ; 723.1 Computer Programming ; 804 Chemical Products Generally ; 912.2 Management ; 921.5 Optimization Techniques
来源库	WOS
引用统计	被引频次：40[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/159766
专题	国际合作处（港澳台办）
通讯作者	Zhao, Fuqing; Wang, Ling
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China; 2.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Zhao, Fuqing,Di, Shilu,Wang, Ling. A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem[J]. IEEE Transactions on Cybernetics,2023,53(5):1-14.
APA	Zhao, Fuqing,Di, Shilu,&Wang, Ling.(2023).A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem.IEEE Transactions on Cybernetics,53(5),1-14.
MLA	Zhao, Fuqing,et al."A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem".IEEE Transactions on Cybernetics 53.5(2023):1-14.