IMR OpenIR
A novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production
Alternative TitleA novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production
Pang Xinfu1; Gao Liang1; Pan Quanke1; Tian Weihua2; Yu Shengping3
2017
Source PublicationJOURNAL OF CENTRAL SOUTH UNIVERSITY
ISSN2095-2899
Volume24Issue:2Pages:467-477
AbstractA Lagrangian relaxation (LR) approach was presented which is with machine capacity relaxation and operation precedence relaxation for solving a flexible job shop (FJS) scheduling problem from the steelmaking-refining-continuous casting process. Unlike the full optimization of LR problems in traditional LR approaches, the machine capacity relaxation is optimized asymptotically, while the precedence relaxation is optimized approximately due to the NP-hard nature of its LR problem. Because the standard subgradient algorithm (SSA) cannot solve the Lagrangian dual (LD) problem within the partial optimization of LR problem, an effective deflected-conditional approximate subgradient level algorithm (DCASLA) was developed, named as Lagrangian relaxation level approach. The efficiency of the DCASLA is enhanced by a deflected-conditional epsilon-subgradient to weaken the possible zigzagging phenomena. Computational results and comparisons show that the proposed methods improve significantly the efficiency of the LR approach and the DCASLA adopting capacity relaxation strategy performs best among eight methods in terms of solution quality and running time.
Other AbstractA Lagrangian relaxation (LR) approach was presented which is with machine capacity relaxation and operation precedence relaxation for solving a flexible job shop (FJS) scheduling problem from the steelmaking-refining-continuous casting process. Unlike the full optimization of LR problems in traditional LR approaches, the machine capacity relaxation is optimized asymptotically, while the precedence relaxation is optimized approximately due to the NP-hard nature of its LR problem. Because the standard subgradient algorithm (SSA) cannot solve the Lagrangian dual (LD) problem within the partial optimization of LR problem, an effective deflected-conditional approximate subgradient level algorithm (DCASLA) was developed, named as Lagrangian relaxation level approach. The efficiency of the DCASLA is enhanced by a deflected-conditional epsilon-subgradient to weaken the possible zigzagging phenomena. Computational results and comparisons show that the proposed methods improve significantly the efficiency of the LR approach and the DCASLA adopting capacity relaxation strategy performs best among eight methods in terms of solution quality and running time.
KeywordAPPROXIMATE SUBGRADIENT METHODS HYBRID FLOWSHOP ALGORITHM STEEL TIME OPTIMIZATION CONVERGENCE SYSTEM steelmaking-refining-continuous casting Lagrangian relaxation (LR) approximate subgradient optimization
Indexed ByCSCD
Language英语
Funding Project[National Natural Science Foundation of China] ; [Postdoctoral Science Foundation of China] ; [Liaoning Province Education Administration, China]
CSCD IDCSCD:5986060
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/142248
Collection中国科学院金属研究所
Affiliation1.Huazhong University Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China
2.中国科学院金属研究所
3.东北大学
Recommended Citation
GB/T 7714
Pang Xinfu,Gao Liang,Pan Quanke,et al. A novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production[J]. JOURNAL OF CENTRAL SOUTH UNIVERSITY,2017,24(2):467-477.
APA Pang Xinfu,Gao Liang,Pan Quanke,Tian Weihua,&Yu Shengping.(2017).A novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production.JOURNAL OF CENTRAL SOUTH UNIVERSITY,24(2),467-477.
MLA Pang Xinfu,et al."A novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production".JOURNAL OF CENTRAL SOUTH UNIVERSITY 24.2(2017):467-477.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Pang Xinfu]'s Articles
[Gao Liang]'s Articles
[Pan Quanke]'s Articles
Baidu academic
Similar articles in Baidu academic
[Pang Xinfu]'s Articles
[Gao Liang]'s Articles
[Pan Quanke]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Pang Xinfu]'s Articles
[Gao Liang]'s Articles
[Pan Quanke]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.