基于“爐?機對應”的煉鋼?連鑄生產調度問題遺傳優化模型

劉倩; 楊建平; 王柏琳; 劉青; 高山; 李宏輝

doi:10.13374/j.issn2095-9389.2019.08.02.004

基于“爐?機對應”的煉鋼?連鑄生產調度問題遺傳優化模型

doi: 10.13374/j.issn2095-9389.2019.08.02.004

劉倩¹,
楊建平¹,
王柏琳^{2, 3},
劉青^{1, 3, ,},
高山⁴,
李宏輝⁴

1.
北京科技大學鋼鐵冶金新技術國家重點實驗室，北京 100083
2.
北京科技大學東凌經濟管理學院，北京 100083
3.
鋼鐵生產制造執行系統技術教育部工程研究中心，北京 100083
4.
萊蕪鋼鐵集團銀山型鋼有限公司，萊蕪 271104

基金項目: 教育部博士學科點專項科研基金資助項目(20090006110024)；中央高校基本科研業務費專項基金資助項目(FRF-BR-17-029A)

詳細信息

通訊作者:
E-mail：qliu@ustb.edu.cn

中圖分類號: TF087
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出版歷程
- 收稿日期: 2019-08-02
- 刊出日期: 2020-05-01

Genetic optimization model of steelmaking?continuous casting production scheduling based on the “furnace?caster coordinating” strategy

LIU Qian¹,
YANG Jian-ping¹,
WANG Bai-lin^{2, 3},
LIU Qing^{1, 3
, ,},
GAO Shan⁴,
LI Hong-hui⁴

1.
State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China
2.
Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
3.
Engineering Research Center of MES Technology for Iron and Steel Production, Ministry of Education, Beijing 100083, China
4.
Laiwu Iron and Steel Group Yinshan Section Steel Co. Ltd., Laiwu 271104, China

More Information

Corresponding author: E-mail: qliu@ustb.edu.cn

摘要

摘要: 針對煉鋼?連鑄過程因車間布局復雜造成工序間鋼水交叉供應頻繁、等待時間過長以及天車調度困難等問題，本文建立以計劃內所有爐次總等待時間最小為優化目標的煉鋼?連鑄過程生產調度模型，并采用改進的遺傳算法求解該模型。在遺傳操作過程中，引入“爐?機對應”調控策略以改善初始種群質量，并根據轉爐（精煉）與連鑄作業周期的比較，來確定是否對個體進行交叉、變異操作。以國內某中大型煉鋼廠主要生產模式下的實際生產計劃為仿真算例進行實驗，結果表明：本文提出的基于“爐?機對應”的改進遺傳算法的性能顯著優于基本遺傳算法及啟發式算法，針對煉鋼廠產量占比超過80%的主要生產模式4BOF?3CCM下的算例1，優化了生產過程等待時間，工序間最長等待時間由77 min減小到54 min；爐?機匹配程度也明顯提高，3號精煉爐去往3號連鑄機的鋼水比例由25%提升到67%，減少了個別爐次由于設備隨機指派造成的工序設備間對應關系不明確及由于生產路徑不合理造成等待時間過長的現象，為研究煉鋼廠復雜生產調度問題提供了一種高效的解決方案。
- 煉鋼?連鑄 /
- 生產調度 /
- 改進遺傳算法 /
- 爐?機對應 /
- 生產模式
Abstract: To avoid the frequent cross supply, excessive waiting time and difficult crane dispatching of molten steel among processes that resulted from the complex workshop layout of the steelmaking continuous casting process, a production scheduling model for the steelmaking-continuous casting process was established in this study with the objective of optimizing and minimizing the total waiting time of all furnaces in the plan. Moreover, an improved genetic algorithm was used to solve the model. In the operation process of the genetic algorithm, the “furnace-caster coordinating” strategy was introduced to improve the quality of the initial population. Furthermore, the crossover and mutation operations were determined based on the comparison of the operating cycles of steelmaking (refining) and continuous casting. The actual production plan under the main production mode of a large domestic steel plant was utilized as the simulation sample. Results show that the performance of the improved algorithm based on the “furnace-caster coordinating” strategy is significantly better than that of the basic genetic and heuristic algorithms. The output of Sample 1 of the main production model 4BOF?3CCM accounts for more than 80% in steel plants. After optimization, the waiting time of the production process is optimized, and the maximum waiting time between steelmaking and continuous casting processes is reduced from 77 to 54 min. The degree of matching of the refining furnace-continuous caster machine is significantly improved. Moreover, the proportion of molten steel poured from the No. 3 refining furnace on the No. 3 continuous caster machine is increased from 25% to 67%. The phenomenon of unclear matching among processes and facilities caused by random facility assignment for one or two furnaces is reduced. Furthermore, the phenomenon of excessive waiting time caused by unreasonable production path for one or two furnaces is reduced. An efficient solution for the study of complex production scheduling problems in steel plants is provided.
- steelmaking?continuous casting /
- production scheduling /
- improved genetic algorithm /
- furnace?caster coordinating /
- production mode

HTML全文

圖 1 典型煉鋼?連鑄生產流程示意圖

Figure 1. Flowchart of typical steelmaking continuous casting production scheduling

下載: 全尺寸圖片幻燈片

圖 2 爐次在冶煉、精煉工序的時間參數

Figure 2. Time parameters of the heat sequences of the smelting and refining process

下載: 全尺寸圖片幻燈片

圖 3 爐次順序調整

Figure 3. Adjustment of the heat sequences

下載: 全尺寸圖片幻燈片

圖 4 基于“爐?機對應”策略的遺傳算法求解流程

Figure 4. Solution procedure of the scheduling problems derived using the genetic algorithm based on the “furnace?caster coordinating” strategy

下載: 全尺寸圖片幻燈片

圖 5 改進前（a）后（b）算例1的爐?機對應關系

Figure 5. “Furnace?caster coordinating” in Sample 1 before (a) and after (b) improvement

下載: 全尺寸圖片幻燈片

圖 6 改進前（a）后（b）算例2的爐?機對應關系

Figure 6. “Furnace?caster coordinating” in Sample 2 before (a) and after (b) improvement

下載: 全尺寸圖片幻燈片

表 1 三種算法的測試結果

Table 1. Test results of three algorithms

Example	Heat	Production model	T₀/min			T₁/min			p/%			T₂/min
Example	Heat	Production model	A1	A2	A3	A1	A2	A3	A1	A2	A3	A1	A2	A3
1	76	4BOF?3CCM	2331	2281	4413	77	54	102	19	13	41	0	0	80
2	77	4BOF?4CCM	3844	3366	4167	97	76	102	35	29	33	0	0	94
3	90	4BOF?3CCM	1952	2036	5735	65	34	103	6	16	57	0	0	105
4	93	3BOF?3CCM	4302	3944	4932	94	76	100	30	29	39	0	0	59
5	65	4BOF?3CCM	1371	1272	2926	45	43	97	9	7	40	0	0	94
6	77	4BOF?3CCM	2456	2014	4658	78	60	92	27	12	54	0	0	78
7	84	4BOF?3CCM	2932	2811	5518	106	88	110	22	18	60	0	0	110
8	77	4BOF?4CCM	2878	3055	6052	84	78	114	28	30	67	0	0	108
9	80	4BOF?4CCM	2968	2280	2532	116	84	88	27	15	23	0	0	52
10	67	4BOF?3CCM	2286	2091	4046	75	72	102	25	18	52	0	0	44

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