A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants

Jianyu Long, Zhong Zheng, Xiaoqiang Gao and Panos M Pardalos
Additional contact information
Jianyu Long: Chongqing University
Zhong Zheng: Chongqing University
Xiaoqiang Gao: Chongqing University
Panos M Pardalos: University of Florida

Journal of the Operational Research Society, 2016, vol. 67, issue 9, 1184-1199

Abstract: Abstract The hot metal is produced from the blast furnaces in the iron plant and should be processed as soon as possible in the subsequent steel plant for energy saving. Therefore, the release times of hot metal have an influence on the scheduling of a steel plant. In this paper, the scheduling problem with release times for steel plants is studied. The production objectives and constraints related to the release times are clarified, and a new multi-objective scheduling model is built. For the solving of the multi-objective optimization, a hybrid multi-objective evolutionary algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. In the hybrid multi-objective algorithm, an efficient decoding heuristic (DH) and a non-dominated solution construction method (NSCM) are proposed based on the problem-specific characteristics. During the evolutionary process, individuals with different solutions may have a same chromosome because the NSCM constructs non-dominated solutions just based on the solution found by DH. Therefore, three operations in the original NSGA-II process are modified to avoid identical chromosomes in the evolutionary operations. Computational tests show that the proposed hybrid algorithm based on NSGA-II is feasible and effective for the multi-objective scheduling with release times.

Keywords: scheduling; steel plant; multi-objective optimization; non-dominated sorting genetic algorithm; release time; heuristic (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1057/jors.2016.17

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