Improved fast elitist non-dominated sorting genetic algorithm for multi-objective steelmaking-continuous casting production scheduling

被引：0
|
作者：
Yuan S. [1 ,2 ]
Li T. [1 ,2 ]
Wang B. [1 ,2 ]
机构：
[1] Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing
[2] Engineering Research Center of MES Technology for Iron & Steel Production, Ministry of Education, Beijing
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2019年 / 25卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Adaptive grid algorithm; Fast elitist non-dominated sorting genetic algorithm; Genetic algorithms; Multi-objective optimization; Prodution scheduling; Steelmaking-continuous casting;
D O I：
10.13196/j.cims.2019.01.011
中图分类号：
学科分类号：
摘要：
Aiming at the special process requirements of steelmaking-continuous casting scheduling and considering the principle of furnace matching and multiple refining stages, a constraint satisfaction model was established with the objectives of furnace-caster matching degree, the total waiting time and the advance/delay time of the cast. For its multi-objective characteristics, an improved fast elitist Non-Dominated Sorting Genetic Algorithm (NSGA-Ⅱ) algorithm with adaptive grid selection strategy was proposed, which overcame the problem of easily losing the useful information with traditional Pareto domination method. The experiments and compared algorithm were set up based on actual production data of various scales, and the results showed that the proposed improved NSGA-Ⅱ algorithm proposed was superior to the traditional NSGA-Ⅱ in terms of convergence, diversity of optimal solution set and computational efficiency based on the scheduling problems of steelmaking-continuous casting. © 2019, Editorial Department of CIMS. All right reserved.
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页码：115 / 124
页数：9
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