Genetic Algorithm Based on Duality Principle for Bilevel Programming Problem in Steel-making Production

Shuo Lin; Fangjun Luan; Zhonghua Han; Xisheng Lü; Xiaofeng Zhou; Wei Liu

doi:10.1016/j.cjche.2014.05.006

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Genetic Algorithm Based on Duality Principle for Bilevel Programming Problem in Steel-making Production

PROCESS SYSTEMS ENGINEERING | Updated：2026-01-06

- Genetic Algorithm Based on Duality Principle for Bilevel Programming Problem in Steel-making Production
- Chinese Journal of Chemical Engineering Vol. 22, Issue 7, Pages: 742-747(2014)
- Affiliations：
  
  1. Information & Control Engineering Faculty, Shenyang Jianzhu University,Shenyang,China,110168
  2. Shenyang Institute of Automation, Chinese Academy of Sciences,Shenyang,China,110016
  3. School of Information Science & Engineering, Northeastern University,Shenyang,China,110004
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2014.05.006
  CLC：
- Published：2014
- Accepted：
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Shuo Lin, Fangjun Luan, Zhonghua Han, Xisheng Lü, Xiaofeng Zhou, Wei Liu. Genetic Algorithm Based on Duality Principle for Bilevel Programming Problem in Steel-making Production[J]. , 2014, 22(7): 742-747.
DOI：

Shuo Lin, Fangjun Luan, Zhonghua Han, Xisheng Lü, Xiaofeng Zhou, Wei Liu. Genetic Algorithm Based on Duality Principle for Bilevel Programming Problem in Steel-making Production[J]. , 2014, 22(7): 742-747. DOI： 10.1016/j.cjche.2014.05.006.

摘要

Abstract

Steel-making and continuous/ingot casting are the key processes of modern iron and steel enterprises. Bilevel programming problems (BLPPs) are the optimization problemswith hierarchical structure. In steel-making production

the plan is not only decided by the steel-making scheduling

but also by the transportation equipment. This paper proposes a genetic algorithmto solve continuous and ingot casting scheduling problems. Based on the characteristics of the problems involved

a genetic algorithm is proposed for solving the bilevel programming problem in steel-making production. Furthermore

based on the simplex method

a new crossover operator is designed to improve the efficiency of the genetic algorithm. Finally

the convergence is analyzed. Using actual data the validity of the proposed algorithm is proved and the application results in the steel plant are analyzed.

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