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A novel Lagrangian relaxation level approach for scheduling steelmaking-refining-continuous casting production

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文摘 A 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.
来源 Journal of Central South University ,2017,24(2):467-477 【核心库】
DOI 10.1007/s11171-017-3449-9
关键词 steelmaking-refining-continuous casting ; Lagrangian relaxation (LR) ; approximate subgradient optimization
地址

1. Huazhong University of Science and Technology, State Key Laboratory of Digital Manufacturing Equipment and Technology, Wuhan, 430074  

2. School of Automation, Shenyang Institute of Engineering, Shenyang, 110136  

3. Northeastern University, State Key Laboratory of Synthetical Automation for Process Industries, Shenyang, 110819

语种 英文
文献类型 研究性论文
ISSN 2095-2899
学科 一般工业技术
基金 国家自然科学基金 ;  中国博士后科学基金 ;  supported by Liaoning Province Education Administration, China
文献收藏号 CSCD:5986060

参考文献 共 30 共2页

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