Modeling and optimization methods of integrated production planning for steel plate mill with flexible customization

doi:10.1016/j.cjche.2015.10.003

Chin.J.Chem.Eng. ›› 2015, Vol. 23 ›› Issue (12): 2037-2047.DOI: 10.1016/j.cjche.2015.10.003

Previous Articles Next Articles

Modeling and optimization methods of integrated production planning for steel plate mill with flexible customization

Shan Lu¹, Hongye Su¹, Charlotta Johnsson², YueWang¹, Lei Xie ¹

1 State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China;
2 Department of Automatic Control, Lund University, Lund, Sweden

Received:2015-05-22 Revised:2015-07-27 Online:2016-01-19 Published:2015-12-28
Contact: Hongye Su
Supported by:
Supported in part by the National High Technology Research and Development Program of China (2012AA041701), the National Natural Science Foundation of China (61320106009) and the 111 Project of China (B07031).

Modeling and optimization methods of integrated production planning for steel plate mill with flexible customization

Shan Lu¹, Hongye Su¹, Charlotta Johnsson², YueWang¹, Lei Xie ¹

1 State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China;
2 Department of Automatic Control, Lund University, Lund, Sweden

通讯作者: Hongye Su
基金资助:
Supported in part by the National High Technology Research and Development Program of China (2012AA041701), the National Natural Science Foundation of China (61320106009) and the 111 Project of China (B07031).

Abstract

Abstract: With diversified requirements and varying manufacturing environments, the optimal production planning for a steelmill becomes more flexible and complicated. The flexibility provides operators with auxiliary requirements through an implementable integrated production planning. In this paper, a mixed-integer nonlinear programming (MINLP) model is proposed for the optimal planning that incorporates various manufacturing constraints and flexibility in a steel plate mill. Furthermore, two solution strategies are developed to overcome theweakness in solving the MINLP problem directly. The first one is to transformthe original MINLP formulation to an approximate mixed integer linear programming using a classic linearization method. The second one is to decompose the originalmodel using a branch-and-bound based iterative method. Computational experiments on various instances are presented in terms of the effectiveness and applicability. The result shows that the second method performs better in computational efforts and solution accuracy.

Key words: Production planning, Steel plate mill, Flexibility, Mixed integer nonlinear programming

摘要： With diversified requirements and varying manufacturing environments, the optimal production planning for a steelmill becomes more flexible and complicated. The flexibility provides operators with auxiliary requirements through an implementable integrated production planning. In this paper, a mixed-integer nonlinear programming (MINLP) model is proposed for the optimal planning that incorporates various manufacturing constraints and flexibility in a steel plate mill. Furthermore, two solution strategies are developed to overcome theweakness in solving the MINLP problem directly. The first one is to transformthe original MINLP formulation to an approximate mixed integer linear programming using a classic linearization method. The second one is to decompose the originalmodel using a branch-and-bound based iterative method. Computational experiments on various instances are presented in terms of the effectiveness and applicability. The result shows that the second method performs better in computational efforts and solution accuracy.

关键词: Production planning, Steel plate mill, Flexibility, Mixed integer nonlinear programming

Shan Lu, Hongye Su, Charlotta Johnsson, YueWang, Lei Xie . Modeling and optimization methods of integrated production planning for steel plate mill with flexible customization[J]. Chin.J.Chem.Eng., 2015, 23(12): 2037-2047.

References

[1] P. Cowling,W. Rezig, Integration of continuous caster and hot strip mill planning for steel production, J. Sched. 3 (4) (2000) 185-208.

[2] A. Balakrishnan, J. Geunes, Production planning with flexible product specifications: an application to specialty steel manufacturing, Oper. Res. 1 (51) (2003) 94-112.

[3] L.Y.Wang, Y.L. Chen, H.W.Ma, Production planning and control, Tsinghua University Press, Beijing, 2007 98-99.

[4] L.F. Cheng, E. Subrahmanian, A.W. Westerberg,Multi-objective decisions on capacity planning and production-inventory control under uncertainty, Ind. Eng. Chem. Res. 43 (9) (2004) 2092-2208.

[5] C.H. Chung, L.J. Krajewski, Planning horizons for master production scheduling, J. Oper. Manag. 4 (4) (1984) 389-406.

[6] T.Wuttipornpun, P. Yenradee, Development of finite capacity material requirement planning system for assembly operations, Prod. Plann. Control 5 (15) (2004) 534-549.

[7] J.X. Xie, X.D. Zhao, T.S. Lee, Freezing themaster production schedule under single resource constraint and demand uncertainty, Int. J. Prod. Econ. 1 (83) (2003) 65-84.

[8] L.X. Tang, J.Y. Liu, A.Y. Rong, Z.H. Yang, A review of planning and scheduling systems and methods for integrated steel production, Eur. J. Oper. Res. 1 (133) (2001) 1-20.

[9] S. Zanoni, L. Zavanella, Model and analysis of integrated production-inventory system: The case of steel production, Int. J. Prod. Econ. 93 (2005) 197-205.

[10] H. Li, J. Shang, Integrated model for production planning in a large iron and steel manufacturing environment, Int. J. Prod. Res. 39 (9) (2001) 2037-2062.

[11] B.D. Neureuther, G.G. Polak, N.R. Sanders, A hierarchical production plan for amaketo- order steel fabrication plant, Prod. Plann. Control 15 (3) (2004) 324-335.

[12] T. Spengler, S. Rehkopf, T. Volling, Revenue management in make-to-order manufacturing - An application to the iron and steel industry, OR Spectr. 29 (1) (2007) 157-171.

[13] S.X. Liu, J.F. Tang, J.H. Song, Order-planning model and algorithm for manufacturing steel sheets, Int. J. Prod. Econ. 100 (1) (2006) 30-43.

[14] T. Zhang, Y.J. Zhang, Q.P. Zheng, P.M. Pardalos, A hybrid particle swarmoptimization and tabu search algorithm for order planning problems of steel factories based on the make-to-stock and make-to-order management architecture, J. Ind. Manage. Optim. 7 (1) (2011) 31-51.

[15] A. Witt, S. Voss, Application of a mathematical model to an intermediate- to longterm real-world steel production planning problem based on standard software, Eur. J. Ind. Eng. 5 (1) (2011) 81-100.

[16] T.S. Adam Ng, E.L. Johnson, Production planning with flexible customization using a branch-price-cut method, IIE Trans. 40 (12) (2008) 1198-1210.

[17] S.M.R. Iravani, K.L. Luangkesorn, D. Simchi-Levi, On assemble-to-order systems with flexible customers, IIE Trans. 35 (5) (2003) 389-403.

[18] R. As'ad, K. Demirli, Production scheduling in steel rollingmillswith demand substitution: Rolling horizon implementation and approximations, Int. J. Prod. Econ. 126 (2) (2010) 361-369.

[19] M. Vanhoucke, D. Debels, A finite-capacity production scheduling procedure for a Belgian steel company, Int. J. Prod. Res. 47 (3) (2009) 561-584.

[20] S. Leyffer, Integrating SQP and branch-and-bound for mixed integer nonlinear programming, Comput. Optim. Appl. 18 (3) (2001) 295-309.

[21] R. Fletcher, S. Leyffer, Solving mixed integer nonlinear programs by outer approximation, Math. Program. 66 (1-3) (1994) 327-349.

[22] S.A. van den Heever, I.E. Grossmann, S. Vasantharajan, K. Edwards, A Lagrangean decomposition heuristic for the design and planning of offshore hydrocarbon field infrastructures with complex economic objectives, Ind. Eng. Chem. Res. 40 (13) (2001) 2857-2875.

[23] L. Costa, P. Oliveira, Evolutionary algorithms approach to the solution of mixed integer non-linear programming problems, Comput. Chem. Eng. 25 (2) (2001) 257-266.

[24] C.T. Young, Y. Zheng, C.W. Yeh, S.S. Jang, Information-guided genetic algorithm approach to the solution of MINLP problems, Ind. Eng. Chem. Res. 46 (5) (2007) 1527-1537.

[25] I.E. Grossmann, Review of nonlinear mixed-integer and disjunctive programming techniques, Optim. Eng. 3 (3) (2002) 227-252.

[26] C.A. Floudas, C.E. Gounaris, A review of recent advances in global optimization, J. Glob. Optim. 45 (1) (2009) 3-38.

[27] F. Glover, Improved linear integer programming formulations of nonlinear integer problems, Manag. Sci. 22 (4) (1975) 455-460.

[28] S. Lu, H. Su, C. Johnsson, et al., A Production Planning Model for a Steel Plate Fabrication Plant with Flexible Customization and Manufacturing[C]//World Congress, 19 (1) (2014) 3315-3320.

[29] P.I. Cowling, D. Ouelhadj, S. Petrovic, A multi-agent architecture for dynamic scheduling of steel hot rolling, J. Intell. Manuf. 14 (5) (2003) 457-470.

[30] Y.Q. Jiao, H.Y. Su, W.F. Hou, P. Li, Design and optimization of flexible hydrogen systems in refineries, Ind. Eng. Chem. Res. 52 (11) (2013) 4113-4131.

[31] F.Q. You, J.M. Pinto, E. Capon, I.E. Grossmann, N. Arora, L. Megan, Optimal distribution- inventory planning of industrial gases. I. Fast computational strategies for large-scale problems, Ind. Eng. Chem. Res. 50 (5) (2011) 2910-2927.

[32] H.D. Sherali, A. Alameddine, A new reformulation-linearization technique for bilinear programming problems, J. Glob. Optim. 2 (4) (1992) 379-410.

[33] S. Orcun, I.K. Altinel, O. Hortacsu, General continuous time models for production planning and scheduling of batch processing plants: Mixed integer linear program formulations and computational issues, Comput. Chem. Eng. 25 (2) (2001) 371-389.

[34] N. Absi, S. Kedad-Sidhoum, MIP-based heuristics for multi-item capacitated lotsizing problem with setup times and shortage costs, RAIRO Oper. Res. 41 (2) (2007) 171-192.

Modeling and optimization methods of integrated production planning for steel plate mill with flexible customization

Modeling and optimization methods of integrated production planning for steel plate mill with flexible customization

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Wenhui Yang, Haoyu Yin, Zhihong Yuan, Bingzhen Chen. Flexibility analysis for continuous ibuprofen manufacturing processes [J]. Chinese Journal of Chemical Engineering, 2022, 51(11): 115-125.
[2]	Wenhui Yang, Wuxi Qian, Zhihong Yuan, Bingzhen Chen. Perspectives on the flexibility analysis for continuous pharmaceutical manufacturing processes [J]. Chinese Journal of Chemical Engineering, 2022, 41(1): 29-41.
[3]	Siwen Gu, Linlin Liu, Lei Zhang, Yiyuan Bai, Shaojing Wang, Jian Du. Heat exchanger network synthesis integrated with flexibility and controllability [J]. Chinese Journal of Chemical Engineering, 2019, 27(7): 1474-1484.
[4]	Lixia Kang, Yongzhong Liu. Synthesis of flexible heat exchanger networks: A review [J]. Chinese Journal of Chemical Engineering, 2019, 27(7): 1485-1497.
[5]	Tao Yang, Yiqing Luo, Yingjie Ma, Xigang Yuan. Optimal synthesis of compression refrigeration system using a novel MINLP approach [J]. Chin.J.Chem.Eng., 2018, 26(8): 1662-1669.
[6]	Danlei Chen, Xue Ma, Yiqing Luo, Yingjie Ma, Xigang Yuan. Synthesis of refrigeration system based on generalized disjunctive programming model [J]. Chin.J.Chem.Eng., 2018, 26(8): 1613-1620.
[7]	Yang Yuan, Kejin Huang, Liang Zhang, Haisheng Chen, Shaofeng Wang, Yingjie Jiao. Elevating the flexibility and operability of dividing-wall distillation columns via feed thermal condition adjustment [J]. , 2017, 25(7): 947-954.
[8]	Jin Lang, Jiao Zhao. Modeling and optimization for oil well production scheduling [J]. , 2016, 24(10): 1423-1430.
[9]	Jianguang Qi, Yufeng Hu, Yamei Zhao, Jiguang Li. The role of the symmetry and the flexibility of the anion on the characteristics of the nanostructures and the viscosities of ionic liquids [J]. , 2015, 23(9): 1565-1571.
[10]	Zhen Gao. A planning model for multiple blending schemes [J]. Chin.J.Chem.Eng., 2015, 23(4): 675-680.
[11]	JIAO Yunqiang, SU Hongye, LIAO Zuwei, HOU Weifeng. Modeling and Multi-objective Optimization of Refinery Hydrogen Network [J]. , 2011, 19(6): 990-998.
[12]	ZHOU Hua, QIAN Yu, LI Xiuxi, CUI Jian, Andrzej Kraslawski. The Dynamic Flexibility of Batch Exothermic Reaction System:Take into Account the Effect of the Initial Operational Temperature [J]. , 2008, 16(6): 916-922.
[13]	WANG Ruqiang, LI Chufu, HE Xiaorong, CHEN Bingzhen. A Novel Close-loop Strategy for Integrating Process Operations of Fluidized Catalytic Cracking Unit with Production Planning Optimization [J]. , 2008, 16(6): 909-915.
[14]	LI Chufu, HE Xiaorong, CHEN Bingzhen, XU Qiang, LIU Chaowei. A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery [J]. , 2008, 16(2): 241-246.
[15]	AN Weizhong, YU Fengjuan, DONG Fenglei, HU Yangdong. Simulated Annealing Approach to the Optimal Synthesis of Distillation Column with Intermediate Heat Exchangers [J]. Chin.J.Chem.Eng., 2008, 16(1): 30-35.