Multi-objective order planning model and algorithm for integrated steel production

Chun-yue, Yu; Rong-xia, QU

doi:10.1109/icmse.2008.4668946

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…These slabs are grouped together to form charge design, which will be scheduled to be produced in the converter. After that, liquid steel from the converter is transformed to real slabs in the continuous caster, finally steel coil products will be delivered to the user after hot rolling and cold rolling [1][2][3] .…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization for Steel Grade Selection Based on Hierarchical Genetic Algorithm

Liu

et al. 2012

AMR

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In steel-making process, when a furnace is charged, there are many optional steel grades for each slab. It is a difficult problem to select the appropriate steel grade for each slab. In this paper, based on the analysis of technics constraints in steel-making process, the steel grade intensivism problem is described, and the mathematical model is also established. To solve the above problem, a newly designed hierarchical genetic algorithm is proposed, where the hierarchical manner is used to decrease the solution space. The effectiveness of the approach is demonstrated by a simulation. The optimal solution can be obtained in reasonable time, which will be helpful to decrease the scraps between two steel grades while casting, to decrease the sum of surplus, and eventually to cut down the stock.

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Section: Introductionmentioning

confidence: 99%

Modeling and Optimization for Steel Grade Selection Based on Hierarchical Genetic Algorithm

Liu

et al. 2012

AMR

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“…(2) The method using the Pareto-optimum based nondominated sorting genetic algorithm II (NSGA-II) [10][11][12][13]. However, NSGA-II has the following defects [14][15][16][17]: In NSGA-II, the elitist extent is not limited. Preserving elitist means emphasizing the elite solutions in the subsequent generation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for Parameters of Energy Management Strategy of HEV Based on Improved NSGA-II

Zhao

2010

AMM

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Hybrid Electric Vehicle (HEV) provides fairly high fuel economy with lower emissions compared to conventional vehicles. To enhance HEV performance in terms of fuel economy and emissions, subject to the satisfaction of driving performance, multi-objective optimization for parameters of energy management strategy is inevitable. Considering the defect of the method which transfers multi-objective optimization problem into that of single-objective and the shortage of the Pareto-optimum based nondominated sorting genetic algorithm II (NSGA-II), the NSGA-II has been improved and then applied to the optimization in this paper. The simulation results show that each run of the algorithm can produce many Pareto-optimal solutions and the satisfactory solution can be selected by decision-maker according to the requirement. The results also demonstrate the effectiveness of the approach.

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Comparative Study of Two Genetic Algorithms for Steel Production Planning Under Different Order Backlog Circumstances

Merten,

Hütt,

Uygun

et al. 2024

Engineering Materials

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Multi-objective order planning model and algorithm for integrated steel production

Cited by 5 publications

References 20 publications

Modeling and Optimization for Steel Grade Selection Based on Hierarchical Genetic Algorithm

Modeling and Optimization for Steel Grade Selection Based on Hierarchical Genetic Algorithm

Multi-Objective Optimization for Parameters of Energy Management Strategy of HEV Based on Improved NSGA-II

Comparative Study of Two Genetic Algorithms for Steel Production Planning Under Different Order Backlog Circumstances

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